Merge branch 'staging' into development

Reviewed-on: #104

.gitea/workflows/ci.yml

@@ -0,0 +1,35 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - development
+      - staging
+      - main
+  pull_request:
+    branches:
+      - development
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install ruff pytest
+
+      - name: Run linter
+        run: ruff check .
+
+      - name: Run tests
+        run: pytest tests/ -v --tb=short

.gitea/workflows/deploy-production.yml

@@ -0,0 +1,44 @@
+name: Deploy to Production
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Deploy to Production Server
+        uses: appleboy/ssh-action@v1.0.3
+        with:
+          host: ${{ secrets.PROD_HOST }}
+          username: ${{ secrets.PROD_USER }}
+          key: ${{ secrets.PROD_SSH_KEY }}
+          script: |
+            set -euo pipefail
+
+            cd ~/apps/personal-portfolio
+
+            echo "Pulling latest changes..."
+            git fetch origin main
+            git reset --hard origin/main
+
+            echo "Activating virtual environment..."
+            source .venv/bin/activate
+
+            echo "Installing dependencies..."
+            pip install -r requirements.txt --quiet
+
+            echo "Running dbt models..."
+            cd dbt && dbt run --profiles-dir . && cd ..
+
+            echo "Restarting application..."
+            docker compose down
+            docker compose up -d
+
+            echo "Waiting for health check..."
+            sleep 10
+            curl -f http://localhost:8050/health || exit 1
+
+            echo "Production deployment complete!"

.gitea/workflows/deploy-staging.yml

@@ -0,0 +1,44 @@
+name: Deploy to Staging
+
+on:
+  push:
+    branches:
+      - staging
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Deploy to Staging Server
+        uses: appleboy/ssh-action@v1.0.3
+        with:
+          host: ${{ secrets.STAGING_HOST }}
+          username: ${{ secrets.STAGING_USER }}
+          key: ${{ secrets.STAGING_SSH_KEY }}
+          script: |
+            set -euo pipefail
+
+            cd ~/apps/personal-portfolio
+
+            echo "Pulling latest changes..."
+            git fetch origin staging
+            git reset --hard origin/staging
+
+            echo "Activating virtual environment..."
+            source .venv/bin/activate
+
+            echo "Installing dependencies..."
+            pip install -r requirements.txt --quiet
+
+            echo "Running dbt models..."
+            cd dbt && dbt run --profiles-dir . && cd ..
+
+            echo "Restarting application..."
+            docker compose down
+            docker compose up -d
+
+            echo "Waiting for health check..."
+            sleep 10
+            curl -f http://localhost:8050/health || exit 1
+
+            echo "Staging deployment complete!"

.gitignore

@@ -198,3 +198,4 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
+dbt/.user.yml

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.defaultInterpreterPath": "/home/leomiranda/WorkDev/personal/personal-portfolio/.venv/bin/python"
+}

CLAUDE.md

@@ -1,5 +1,48 @@
 # CLAUDE.md
 
+## ⛔ MANDATORY BEHAVIOR RULES - READ FIRST
+
+**These rules are NON-NEGOTIABLE. Violating them wastes the user's time and money.**
+
+### 1. WHEN USER ASKS YOU TO CHECK SOMETHING - CHECK EVERYTHING
+- Search ALL locations, not just where you think it is
+- Check cache directories: `~/.claude/plugins/cache/`
+- Check installed: `~/.claude/plugins/marketplaces/`
+- Check source directories
+- **NEVER say "no" or "that's not the issue" without exhaustive verification**
+
+### 2. WHEN USER SAYS SOMETHING IS WRONG - BELIEVE THEM
+- The user knows their system better than you
+- Investigate thoroughly before disagreeing
+- **Your confidence is often wrong. User's instincts are often right.**
+
+### 3. NEVER SAY "DONE" WITHOUT VERIFICATION
+- Run the actual command/script to verify
+- Show the output to the user
+- **"Done" means VERIFIED WORKING, not "I made changes"**
+
+### 4. SHOW EXACTLY WHAT USER ASKS FOR
+- If user asks for messages, show the MESSAGES
+- If user asks for code, show the CODE
+- **Do not interpret or summarize unless asked**
+
+**FAILURE TO FOLLOW THESE RULES = WASTED USER TIME = UNACCEPTABLE**
+
+---
+
+
+
+## Mandatory Behavior Rules
+
+**These rules are NON-NEGOTIABLE. Violating them wastes the user's time and money.**
+
+1. **CHECK EVERYTHING** - Search ALL locations before saying "no" (cache, installed, source directories)
+2. **BELIEVE THE USER** - Investigate thoroughly before disagreeing; user instincts are often right
+3. **VERIFY BEFORE "DONE"** - Run commands, show output; "done" means verified working
+4. **SHOW EXACTLY WHAT'S ASKED** - Do not interpret or summarize unless requested
+
+---
+
 Working context for Claude Code on the Analytics Portfolio project.
 
 ---
@@ -17,15 +60,33 @@ Working context for Claude Code on the Analytics Portfolio project.
 ### Run Commands
 
 ```bash
+# Setup & Database
 make setup          # Install deps, create .env, init pre-commit
-make docker-up      # Start PostgreSQL + PostGIS
+make docker-up      # Start PostgreSQL + PostGIS (auto-detects x86/ARM)
 make docker-down    # Stop containers
 make db-init        # Initialize database schema
+make db-reset       # Drop and recreate database (DESTRUCTIVE)
+
+# Data Loading
+make load-data      # Load all project data (currently: Toronto)
+make load-toronto   # Load Toronto data from APIs
+
+# Application
 make run            # Start Dash dev server
+
+# Testing & Quality
 make test           # Run pytest
 make lint           # Run ruff linter
 make format         # Run ruff formatter
-make ci             # Run all checks
+make typecheck      # Run mypy type checker
+make ci             # Run all checks (lint, typecheck, test)
+
+# dbt
+make dbt-run        # Run dbt models
+make dbt-test       # Run dbt tests
+make dbt-docs       # Generate and serve dbt documentation
+
+# Run `make help` for full target list
 ```
 
 ### Branch Workflow
@@ -33,10 +94,7 @@ make ci             # Run all checks
 1. Create feature branch FROM `development`: `git checkout -b feature/{sprint}-{description}`
 2. Work and commit on feature branch
 3. Merge INTO `development` when complete
-4. Delete the feature branch after merge (keep branches clean)
-5. `development` -> `staging` -> `main` for releases
-
-**CRITICAL: NEVER DELETE the `development` branch. It is the main integration branch.**
+4. `development` -> `staging` -> `main` for releases
 
 ---
 
@@ -52,125 +110,44 @@ make ci             # Run all checks
 
 ### Module Responsibilities
 
-| Directory | Contains | Purpose |
-|-----------|----------|---------|
-| `schemas/` | Pydantic models | Data validation |
-| `models/` | SQLAlchemy ORM | Database persistence |
-| `parsers/` | API/CSV extraction | Raw data ingestion |
-| `loaders/` | Database operations | Data loading |
-| `figures/` | Chart factories | Plotly figure generation |
-| `callbacks/` | Dash callbacks | In `pages/{dashboard}/callbacks/` |
-| `errors/` | Exceptions + handlers | Error handling |
-
-### Type Hints
-
-Use Python 3.10+ style:
-```python
-def process(items: list[str], config: dict[str, int] | None = None) -> bool:
-    ...
-```
-
-### Error Handling
-
-```python
-# errors/exceptions.py
-class PortfolioError(Exception):
-    """Base exception."""
-
-class ParseError(PortfolioError):
-    """PDF/CSV parsing failed."""
-
-class ValidationError(PortfolioError):
-    """Pydantic or business rule validation failed."""
-
-class LoadError(PortfolioError):
-    """Database load operation failed."""
-```
+| Directory | Purpose |
+|-----------|---------|
+| `schemas/` | Pydantic models for data validation |
+| `models/` | SQLAlchemy ORM for database persistence |
+| `parsers/` | API/CSV extraction for raw data ingestion |
+| `loaders/` | Database operations for data loading |
+| `services/` | Query functions for dbt mart queries |
+| `figures/` | Chart factories for Plotly figure generation |
+| `errors/` | Custom exception classes (see `errors/exceptions.py`) |
 
 ### Code Standards
 
+- Python 3.10+ type hints: `list[str]`, `dict[str, int] | None`
 - Single responsibility functions with verb naming
 - Early returns over deep nesting
 - Google-style docstrings only for non-obvious behavior
-- Module-level constants for magic values
-- Pydantic BaseSettings for runtime config
 
 ---
 
 ## Application Structure
 
-```
-portfolio_app/
-├── app.py                    # Dash app factory with Pages routing
-├── config.py                 # Pydantic BaseSettings
-├── assets/                   # CSS, images (auto-served)
-│   └── sidebar.css          # Navigation styling
-├── callbacks/               # Global callbacks
-│   ├── sidebar.py           # Sidebar toggle
-│   └── theme.py             # Dark/light theme
-├── pages/
-│   ├── home.py              # Bio landing page -> /
-│   ├── about.py             # About page -> /about
-│   ├── contact.py           # Contact form -> /contact
-│   ├── health.py            # Health endpoint -> /health
-│   ├── projects.py          # Project showcase -> /projects
-│   ├── resume.py            # Resume/CV -> /resume
-│   ├── blog/
-│   │   ├── index.py         # Blog listing -> /blog
-│   │   └── article.py       # Blog article -> /blog/{slug}
-│   └── toronto/
-│       ├── dashboard.py     # Dashboard -> /toronto
-│       ├── methodology.py   # Methodology -> /toronto/methodology
-│       ├── tabs/            # 5 tab layouts (overview, housing, safety, demographics, amenities)
-│       └── callbacks/       # Dashboard interactions
-├── components/              # Shared UI (sidebar, cards, controls)
-│   ├── metric_card.py       # KPI card component
-│   ├── map_controls.py      # Map control panel
-│   ├── sidebar.py           # Navigation sidebar
-│   └── time_slider.py       # Time range selector
-├── figures/                 # Shared chart factories
-│   ├── choropleth.py        # Map visualizations
-│   ├── bar_charts.py        # Ranking, stacked, horizontal bars
-│   ├── scatter.py           # Scatter and bubble plots
-│   ├── radar.py             # Radar/spider charts
-│   ├── demographics.py      # Age pyramids, donut charts
-│   ├── time_series.py       # Trend lines
-│   └── summary_cards.py     # KPI figures
-├── content/                 # Markdown content
-│   └── blog/                # Blog articles
-├── toronto/                 # Toronto data logic
-│   ├── parsers/
-│   ├── loaders/
-│   ├── schemas/             # Pydantic
-│   ├── models/              # SQLAlchemy
-│   └── demo_data.py         # Sample data
-├── utils/                   # Utilities
-│   └── markdown_loader.py   # Markdown processing
-└── errors/
+**Entry Point:** `portfolio_app/app.py` (Dash app factory with Pages routing)
 
-notebooks/                   # Data documentation (Phase 6)
-├── README.md                # Template and usage guide
-├── overview/                # Overview tab notebooks (3)
-├── housing/                 # Housing tab notebooks (3)
-├── safety/                  # Safety tab notebooks (3)
-├── demographics/            # Demographics tab notebooks (3)
-└── amenities/               # Amenities tab notebooks (3)
-```
+| Directory | Purpose |
+|-----------|---------|
+| `pages/` | Dash Pages (file-based routing) |
+| `pages/toronto/` | Toronto Dashboard (`tabs/` for layouts, `callbacks/` for interactions) |
+| `components/` | Shared UI components |
+| `figures/toronto/` | Toronto chart factories |
+| `toronto/` | Toronto data logic (parsers, loaders, schemas, models) |
 
-### URL Routing
+**Key URLs:** `/` (home), `/toronto` (dashboard), `/blog` (listing), `/blog/{slug}` (articles), `/health` (status)
 
-| URL | Page | Sprint |
-|-----|------|--------|
-| `/` | Bio landing page | 2 |
-| `/about` | About page | 8 |
-| `/contact` | Contact form | 8 |
-| `/health` | Health endpoint | 8 |
-| `/projects` | Project showcase | 8 |
-| `/resume` | Resume/CV | 8 |
-| `/blog` | Blog listing | 8 |
-| `/blog/{slug}` | Blog article | 8 |
-| `/toronto` | Toronto Dashboard | 6 |
-| `/toronto/methodology` | Dashboard methodology | 6 |
+### Multi-Dashboard Architecture
+
+- **figures/**: Domain-namespaced (`figures/toronto/`, future: `figures/football/`)
+- **dbt models**: Domain subdirectories (`staging/toronto/`, `marts/toronto/`)
+- **Database schemas**: Domain-specific raw data (`raw_toronto`, future: `raw_football`)
 
 ---
 
@@ -182,43 +159,31 @@ notebooks/                   # Data documentation (Phase 6)
 | Validation | Pydantic | >=2.0 |
 | ORM | SQLAlchemy | >=2.0 (2.0-style API only) |
 | Transformation | dbt-postgres | >=1.7 |
-| Data Processing | Pandas | >=2.1 |
+| Visualization | Dash + Plotly + dash-mantine-components | >=2.14 |
 | Geospatial | GeoPandas + Shapely | >=0.14 |
-| Visualization | Dash + Plotly | >=2.14 |
-| UI Components | dash-mantine-components | Latest stable |
-| Testing | pytest | >=7.0 |
 | Python | 3.11+ | Via pyenv |
 
-**Notes**:
-- SQLAlchemy 2.0 + Pydantic 2.0 only (never mix 1.x APIs)
-- PostGIS extension required in database
-- Docker Compose V2 format (no `version` field)
+**Notes**: SQLAlchemy 2.0 + Pydantic 2.0 only. Docker Compose V2 format (no `version` field).
 
 ---
 
 ## Data Model Overview
 
-### Geographic Reality (Toronto Housing)
+### Database Schemas
 
-```
-City Neighbourhoods (158) - Primary geographic unit for analysis
-CMHC Zones (~20)          - Rental data (Census Tract aligned)
-```
+| Schema | Purpose |
+|--------|---------|
+| `public` | Shared dimensions (dim_time) |
+| `raw_toronto` | Toronto-specific raw/dimension tables |
+| `stg_toronto` | Toronto dbt staging views |
+| `int_toronto` | Toronto dbt intermediate views |
+| `mart_toronto` | Toronto dbt mart tables |
 
-### Star Schema
-
-| Table | Type | Keys |
-|-------|------|------|
-| `fact_rentals` | Fact | -> dim_time, dim_cmhc_zone |
-| `dim_time` | Dimension | date_key (PK) |
-| `dim_cmhc_zone` | Dimension | zone_key (PK), geometry |
-| `dim_neighbourhood` | Dimension | neighbourhood_id (PK), geometry |
-| `dim_policy_event` | Dimension | event_id (PK) |
-
-### dbt Layers
+### dbt Project: `portfolio`
 
 | Layer | Naming | Purpose |
 |-------|--------|---------|
+| Shared | `stg_dimensions__*` | Cross-domain dimensions |
 | Staging | `stg_{source}__{entity}` | 1:1 source, cleaned, typed |
 | Intermediate | `int_{domain}__{transform}` | Business logic |
 | Marts | `mart_{domain}` | Final analytical tables |
@@ -227,13 +192,12 @@ CMHC Zones (~20)          - Rental data (Census Tract aligned)
 
 ## Deferred Features
 
-**Stop and flag if a task seems to require these**:
+**Stop and flag if a task requires these**:
 
 | Feature | Reason |
 |---------|--------|
 | Historical boundary reconciliation (140->158) | 2021+ data only for V1 |
 | ML prediction models | Energy project scope (future phase) |
-| Multi-project shared infrastructure | Build first, abstract second |
 
 ---
 
@@ -253,92 +217,123 @@ LOG_LEVEL=INFO
 
 ---
 
-## Script Standards
-
-All scripts in `scripts/`:
-- Include usage comments at top
-- Idempotent where possible
-- Exit codes: 0 = success, 1 = error
-- Use `set -euo pipefail` for bash
-- Log to stdout, errors to stderr
-
----
-
 ## Reference Documents
 
 | Document | Location | Use When |
 |----------|----------|----------|
-| Project reference | `docs/PROJECT_REFERENCE.md` | Architecture decisions, completed work |
-| Developer guide | `docs/CONTRIBUTING.md` | How to add pages, blog posts, tabs |
+| Project reference | `docs/PROJECT_REFERENCE.md` | Architecture decisions |
+| Developer guide | `docs/CONTRIBUTING.md` | How to add pages, tabs |
 | Lessons learned | `docs/project-lessons-learned/INDEX.md` | Past issues and solutions |
+| Deployment runbook | `docs/runbooks/deployment.md` | Deploying to environments |
 
 ---
 
-## Projman Plugin Workflow
+## Plugin Reference
 
-**CRITICAL: Always use the projman plugin for sprint and task management.**
+### Sprint Management: projman
 
-### When to Use Projman Skills
+**CRITICAL: Always use projman for sprint and task management.**
 
 | Skill | Trigger | Purpose |
 |-------|---------|---------|
-| `/projman:sprint-plan` | New sprint or phase implementation | Architecture analysis + Gitea issue creation |
-| `/projman:sprint-start` | Beginning implementation work | Load lessons learned (Wiki.js or local), start execution |
-| `/projman:sprint-status` | Check progress | Review blockers and completion status |
-| `/projman:sprint-close` | Sprint completion | Capture lessons learned (Wiki.js or local backup) |
+| `/projman:sprint-plan` | New sprint/feature | Architecture analysis + Gitea issue creation |
+| `/projman:sprint-start` | Begin implementation | Load lessons learned, start execution |
+| `/projman:sprint-status` | Check progress | Review blockers and completion |
+| `/projman:sprint-close` | Sprint completion | Capture lessons learned |
 
-### Default Behavior
+**Default workflow**: `/projman:sprint-plan` before code -> create issues -> `/projman:sprint-start` -> track via Gitea -> `/projman:sprint-close`
 
-When user requests implementation work:
+**Gitea**: `personal-projects/personal-portfolio` at `gitea.hotserv.cloud`
 
-1. **ALWAYS start with `/projman:sprint-plan`** before writing code
-2. Create Gitea issues with proper labels and acceptance criteria
-3. Use `/projman:sprint-start` to begin execution with lessons learned
-4. Track progress via Gitea issue comments
-5. Close sprint with `/projman:sprint-close` to document lessons
+### Data Platform: data-platform
 
-### Gitea Repository
+Use for dbt, PostgreSQL, and PostGIS operations.
 
-- **Repo**: `lmiranda/personal-portfolio`
-- **Host**: `gitea.hotserv.cloud`
-- **Note**: `lmiranda` is a user account (not org), so label lookup may require repo-level labels
+| Skill | Purpose |
+|-------|---------|
+| `/data-platform:data-review` | Audit data integrity, schema validity, dbt compliance |
+| `/data-platform:data-gate` | CI/CD data quality gate (pass/fail) |
 
-### MCP Tools Available
+**When to use:** Schema changes, dbt model development, data loading, before merging data PRs.
 
-**Gitea**:
-- `list_issues`, `get_issue`, `create_issue`, `update_issue`, `add_comment`
-- `get_labels`, `suggest_labels`
+**MCP tools available:** `pg_connect`, `pg_query`, `pg_tables`, `pg_columns`, `pg_schemas`, `st_*` (PostGIS), `dbt_*` operations.
 
-**Wiki.js**:
-- `search_lessons`, `create_lesson`, `search_pages`, `get_page`
+### Visualization: viz-platform
 
-### Lessons Learned (Backup Method)
+Use for Dash/Mantine component validation and chart creation.
 
-**When Wiki.js is unavailable**, use the local backup in `docs/project-lessons-learned/`:
+| Skill | Purpose |
+|-------|---------|
+| `/viz-platform:component` | Inspect DMC component props and validation |
+| `/viz-platform:chart` | Create themed Plotly charts |
+| `/viz-platform:theme` | Apply/validate themes |
+| `/viz-platform:dashboard` | Create dashboard layouts |
 
-**At Sprint Start:**
-1. Review `docs/project-lessons-learned/INDEX.md` for relevant past lessons
-2. Search lesson files by tags/keywords before implementation
-3. Apply prevention strategies from applicable lessons
+**When to use:** Dashboard development, new visualizations, component prop lookup.
 
-**At Sprint Close:**
-1. Try Wiki.js `create_lesson` first
-2. If Wiki.js fails, create lesson in `docs/project-lessons-learned/`
-3. Use naming convention: `{phase-or-sprint}-{short-description}.md`
-4. Update `INDEX.md` with new entry
-5. Follow the lesson template in INDEX.md
+### Code Quality: code-sentinel
 
-**Migration:** Once Wiki.js is configured, lessons will be migrated there for better searchability.
+Use for security scanning and refactoring analysis.
 
-### Issue Structure
+| Skill | Purpose |
+|-------|---------|
+| `/code-sentinel:security-scan` | Full security audit of codebase |
+| `/code-sentinel:refactor` | Apply refactoring patterns |
+| `/code-sentinel:refactor-dry` | Preview refactoring without applying |
 
-Every Gitea issue should include:
-- **Overview**: Brief description
-- **Files to Create/Modify**: Explicit paths
-- **Acceptance Criteria**: Checkboxes
-- **Technical Notes**: Implementation hints
-- **Labels**: Listed in body (workaround for label API issues)
+**When to use:** Before major releases, after adding auth/data handling code, periodic audits.
+
+### Documentation: doc-guardian
+
+Use for documentation drift detection and synchronization.
+
+| Skill | Purpose |
+|-------|---------|
+| `/doc-guardian:doc-audit` | Scan project for documentation drift |
+| `/doc-guardian:doc-sync` | Synchronize pending documentation updates |
+
+**When to use:** After significant code changes, before releases.
+
+### Pull Requests: pr-review
+
+Use for comprehensive PR review with multiple analysis perspectives.
+
+| Skill | Purpose |
+|-------|---------|
+| `/pr-review:initial-setup` | Configure PR review for project |
+| Triggered automatically | Security, performance, maintainability, test analysis |
+
+**When to use:** Before merging significant PRs to `development` or `main`.
+
+### Requirement Clarification: clarity-assist
+
+Use when requirements are ambiguous or need decomposition.
+
+**When to use:** Unclear specifications, complex feature requests, conflicting requirements.
+
+### Contract Validation: contract-validator
+
+Use for plugin interface validation.
+
+| Skill | Purpose |
+|-------|---------|
+| `/contract-validator:agent-check` | Quick agent definition validation |
+| `/contract-validator:full-validation` | Full plugin contract validation |
+
+**When to use:** When modifying plugin integrations or agent definitions.
+
+### Git Workflow: git-flow
+
+Use for standardized git operations.
+
+| Skill | Purpose |
+|-------|---------|
+| `/git-flow:commit` | Auto-generated conventional commit |
+| `/git-flow:branch-start` | Create feature/fix/chore branch |
+| `/git-flow:git-status` | Comprehensive status with recommendations |
+
+**When to use:** Complex merge scenarios, branch management, standardized commits.
 
 ---
 
-*Last Updated: January 2026 (Post-Sprint 9)*
+*Last Updated: February 2026*

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024-2025 Leo Miranda
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -1,13 +1,25 @@
-.PHONY: setup docker-up docker-down db-init load-data run test dbt-run dbt-test lint format ci deploy clean help
+.PHONY: setup docker-up docker-down db-init load-data load-all load-toronto load-toronto-only seed-data run test dbt-run dbt-test lint format ci deploy clean help logs run-detached etl-toronto
 
 # Default target
 .DEFAULT_GOAL := help
 
 # Environment
-PYTHON := python3
-PIP := pip
+VENV := .venv
+PYTHON := $(VENV)/bin/python3
+PIP := $(VENV)/bin/pip
 DOCKER_COMPOSE := docker compose
 
+# Architecture detection for Docker images
+ARCH := $(shell uname -m)
+ifeq ($(ARCH),aarch64)
+    POSTGIS_IMAGE := imresamu/postgis:16-3.4
+else ifeq ($(ARCH),arm64)
+    POSTGIS_IMAGE := imresamu/postgis:16-3.4
+else
+    POSTGIS_IMAGE := postgis/postgis:16-3.4
+endif
+export POSTGIS_IMAGE
+
 # Colors for output
 BLUE := \033[0;34m
 GREEN := \033[0;32m
@@ -39,6 +51,7 @@ setup: ## Install dependencies, create .env, init pre-commit
 
 docker-up: ## Start PostgreSQL + PostGIS containers
 	@echo "$(GREEN)Starting database containers...$(NC)"
+	@echo "$(BLUE)Architecture: $(ARCH) -> Using image: $(POSTGIS_IMAGE)$(NC)"
 	$(DOCKER_COMPOSE) up -d
 	@echo "$(GREEN)Waiting for database to be ready...$(NC)"
 	@sleep 3
@@ -57,11 +70,7 @@ docker-logs: ## View container logs
 
 db-init: ## Initialize database schema
 	@echo "$(GREEN)Initializing database schema...$(NC)"
-	@if [ -f scripts/db/init.sh ]; then \
-		bash scripts/db/init.sh; \
-	else \
-		echo "$(YELLOW)scripts/db/init.sh not found - skipping$(NC)"; \
-	fi
+	$(PYTHON) scripts/db/init_schema.py
 
 db-reset: ## Drop and recreate database (DESTRUCTIVE)
 	@echo "$(YELLOW)WARNING: This will delete all data!$(NC)"
@@ -71,14 +80,27 @@ db-reset: ## Drop and recreate database (DESTRUCTIVE)
 	@sleep 3
 	$(MAKE) db-init
 
-load-data: ## Load Toronto data from APIs and run dbt
+# Domain-specific data loading
+load-toronto: ## Load Toronto data from APIs
 	@echo "$(GREEN)Loading Toronto neighbourhood data...$(NC)"
 	$(PYTHON) scripts/data/load_toronto_data.py
+	@echo "$(GREEN)Seeding Toronto development data...$(NC)"
+	$(PYTHON) scripts/data/seed_amenity_data.py
 
-load-data-only: ## Load Toronto data without running dbt
+load-toronto-only: ## Load Toronto data without running dbt or seeding
 	@echo "$(GREEN)Loading Toronto data (skip dbt)...$(NC)"
 	$(PYTHON) scripts/data/load_toronto_data.py --skip-dbt
 
+# Aggregate data loading
+load-data: load-toronto ## Load all project data (currently: Toronto)
+	@echo "$(GREEN)All data loaded!$(NC)"
+
+load-all: load-data ## Alias for load-data
+
+seed-data: ## Seed sample development data (amenities, median_age)
+	@echo "$(GREEN)Seeding development data...$(NC)"
+	$(PYTHON) scripts/data/seed_amenity_data.py
+
 # =============================================================================
 # Application
 # =============================================================================
@@ -105,15 +127,15 @@ test-cov: ## Run pytest with coverage
 
 dbt-run: ## Run dbt models
 	@echo "$(GREEN)Running dbt models...$(NC)"
-	cd dbt && dbt run
+	@set -a && . ./.env && set +a && cd dbt && dbt run --profiles-dir .
 
 dbt-test: ## Run dbt tests
 	@echo "$(GREEN)Running dbt tests...$(NC)"
-	cd dbt && dbt test
+	@set -a && . ./.env && set +a && cd dbt && dbt test --profiles-dir .
 
 dbt-docs: ## Generate dbt documentation
 	@echo "$(GREEN)Generating dbt docs...$(NC)"
-	cd dbt && dbt docs generate && dbt docs serve
+	@set -a && . ./.env && set +a && cd dbt && dbt docs generate --profiles-dir . && dbt docs serve --profiles-dir .
 
 # =============================================================================
 # Code Quality
@@ -139,6 +161,19 @@ ci: ## Run all checks (lint, typecheck, test)
 	$(MAKE) test
 	@echo "$(GREEN)All checks passed!$(NC)"
 
+# =============================================================================
+# Operations
+# =============================================================================
+
+logs: ## Follow docker compose logs (usage: make logs or make logs SERVICE=postgres)
+	@./scripts/logs.sh $(SERVICE)
+
+run-detached: ## Start containers and wait for health check
+	@./scripts/run-detached.sh
+
+etl-toronto: ## Run Toronto ETL pipeline (usage: make etl-toronto MODE=--full)
+	@./scripts/etl/toronto.sh $(MODE)
+
 # =============================================================================
 # Deployment
 # =============================================================================

README.md

@@ -1,5 +1,9 @@
 # Analytics Portfolio
 
+[![CI](https://gitea.hotserv.cloud/lmiranda/personal-portfolio/actions/workflows/ci.yml/badge.svg)](https://gitea.hotserv.cloud/lmiranda/personal-portfolio/actions)
+
+**Live Demo:** [leodata.science](https://leodata.science)
+
 A personal portfolio website showcasing data engineering and visualization capabilities, featuring an interactive Toronto Neighbourhood Dashboard.
 
 ## Live Pages
@@ -32,6 +36,42 @@ An interactive choropleth dashboard analyzing Toronto's 158 official neighbourho
 - Toronto Police Service (crime statistics)
 - CMHC Rental Market Survey (rental data by zone)
 
+## Architecture
+
+```mermaid
+flowchart LR
+    subgraph Sources
+        A1[City of Toronto API]
+        A2[Toronto Police API]
+        A3[CMHC Data]
+    end
+
+    subgraph ETL
+        B1[Parsers]
+        B2[Loaders]
+    end
+
+    subgraph Database
+        C1[(PostgreSQL/PostGIS)]
+        C2[dbt Models]
+    end
+
+    subgraph Application
+        D1[Dash App]
+        D2[Plotly Figures]
+    end
+
+    A1 & A2 & A3 --> B1 --> B2 --> C1 --> C2 --> D1 --> D2
+```
+
+**Pipeline Stages:**
+- **Sources**: External APIs and data files (City of Toronto, Toronto Police, CMHC)
+- **ETL**: Python parsers extract and validate data; loaders persist to database
+- **Database**: PostgreSQL with PostGIS for geospatial; dbt transforms raw → staging → marts
+- **Application**: Dash serves interactive dashboards with Plotly visualizations
+
+For detailed database schema, see [docs/DATABASE_SCHEMA.md](docs/DATABASE_SCHEMA.md).
+
 ## Quick Start
 
 ```bash
@@ -75,28 +115,31 @@ portfolio_app/
 │       ├── tabs/          # Tab layouts (5)
 │       └── callbacks/     # Interaction logic
 ├── components/            # Shared UI components
-├── figures/               # Plotly figure factories
+├── figures/
+│   └── toronto/           # Toronto figure factories
 ├── content/
 │   └── blog/              # Markdown blog articles
 ├── toronto/               # Toronto data logic
 │   ├── parsers/           # API data extraction
 │   ├── loaders/           # Database operations
 │   ├── schemas/           # Pydantic models
-│   └── models/            # SQLAlchemy ORM
+│   └── models/            # SQLAlchemy ORM (raw_toronto schema)
 └── errors/                # Exception handling
 
-dbt/
+dbt/                       # dbt project: portfolio
 ├── models/
-│   ├── staging/           # 1:1 source tables
-│   ├── intermediate/      # Business logic
-│   └── marts/             # Analytical tables
+│   ├── shared/            # Cross-domain dimensions
+│   ├── staging/toronto/   # Toronto staging models
+│   ├── intermediate/toronto/ # Toronto intermediate models
+│   └── marts/toronto/     # Toronto analytical tables
 
-notebooks/                 # Data documentation (15 notebooks)
-├── overview/              # Overview tab visualizations
-├── housing/               # Housing tab visualizations
-├── safety/                # Safety tab visualizations
-├── demographics/          # Demographics tab visualizations
-└── amenities/             # Amenities tab visualizations
+notebooks/
+└── toronto/               # Toronto documentation (15 notebooks)
+    ├── overview/          # Overview tab visualizations
+    ├── housing/           # Housing tab visualizations
+    ├── safety/            # Safety tab visualizations
+    ├── demographics/      # Demographics tab visualizations
+    └── amenities/         # Amenities tab visualizations
 
 docs/
 ├── PROJECT_REFERENCE.md   # Architecture reference

dbt/dbt_project.yml

@@ -1,8 +1,7 @@
-name: 'toronto_housing'
-version: '1.0.0'
+name: 'portfolio'
 config-version: 2
 
-profile: 'toronto_housing'
+profile: 'portfolio'
 
 model-paths: ["models"]
 analysis-paths: ["analyses"]
@@ -16,13 +15,19 @@ clean-targets:
   - "dbt_packages"
 
 models:
-  toronto_housing:
+  portfolio:
+    shared:
+      +materialized: view
+      +schema: shared
     staging:
-      +materialized: view
-      +schema: staging
+      toronto:
+        +materialized: view
+        +schema: stg_toronto
     intermediate:
-      +materialized: view
-      +schema: intermediate
+      toronto:
+        +materialized: view
+        +schema: int_toronto
     marts:
-      +materialized: table
-      +schema: marts
+      toronto:
+        +materialized: table
+        +schema: mart_toronto

dbt/macros/generate_schema_name.sql

@@ -0,0 +1,11 @@
+-- Override dbt default schema name generation.
+-- Use the custom schema name directly instead of
+-- concatenating with the target schema.
+-- See: https://docs.getdbt.com/docs/build/custom-schemas
+{% macro generate_schema_name(custom_schema_name, node) %}
+    {%- if custom_schema_name is none -%}
+        {{ target.schema }}
+    {%- else -%}
+        {{ custom_schema_name | trim }}
+    {%- endif -%}
+{% endmacro %}

dbt/models/intermediate/toronto/_intermediate.yml

@@ -5,11 +5,11 @@ models:
     description: "Rental data enriched with time and zone dimensions"
     columns:
       - name: rental_id
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: zone_code
-        tests:
+        data_tests:
           - not_null
 
   - name: int_neighbourhood__demographics
@@ -17,11 +17,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: census_year
         description: "Census year"
-        tests:
+        data_tests:
           - not_null
       - name: income_quintile
         description: "Income quintile (1-5, city-wide)"
@@ -31,7 +31,7 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Reference year"
@@ -45,11 +45,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Statistics year"
-        tests:
+        data_tests:
           - not_null
       - name: crime_rate_per_100k
         description: "Total crime rate per 100K population"
@@ -61,7 +61,7 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Reference year"
@@ -75,11 +75,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: year
         description: "Survey year"
-        tests:
+        data_tests:
           - not_null
       - name: avg_rent_2bed
         description: "Weighted average 2-bedroom rent"

dbt/models/intermediate/toronto/int_census__toronto_cma.sql

@@ -0,0 +1,60 @@
+-- Intermediate: Toronto CMA census statistics by year
+-- Provides city-wide averages for metrics not available at neighbourhood level
+-- Used when neighbourhood-level data is unavailable (e.g., median household income)
+-- Grain: One row per year
+
+with years as (
+    select * from {{ ref('int_year_spine') }}
+),
+
+census as (
+    select * from {{ ref('stg_toronto__census') }}
+),
+
+-- Census data is only available for 2016 and 2021
+-- Map each analysis year to the appropriate census year
+year_to_census as (
+    select
+        y.year,
+        case
+            when y.year <= 2018 then 2016
+            else 2021
+        end as census_year
+    from years y
+),
+
+-- Toronto CMA median household income from Statistics Canada
+-- Source: Census Profile Table 98-316-X2021001
+-- 2016: $65,829 (from Census Profile)
+-- 2021: $84,000 (from Census Profile)
+cma_income as (
+    select 2016 as census_year, 65829 as median_household_income union all
+    select 2021 as census_year, 84000 as median_household_income
+),
+
+-- City-wide aggregates from loaded neighbourhood data
+city_aggregates as (
+    select
+        census_year,
+        sum(population) as total_population,
+        avg(population_density) as avg_population_density,
+        avg(unemployment_rate) as avg_unemployment_rate
+    from census
+    where population is not null
+    group by census_year
+),
+
+final as (
+    select
+        y.year,
+        y.census_year,
+        ci.median_household_income,
+        ca.total_population,
+        ca.avg_population_density,
+        ca.avg_unemployment_rate
+    from year_to_census y
+    left join cma_income ci on y.census_year = ci.census_year
+    left join city_aggregates ca on y.census_year = ca.census_year
+)
+
+select * from final

dbt/models/intermediate/toronto/int_neighbourhood__amenity_scores.sql

@@ -34,7 +34,7 @@ amenity_scores as (
         n.population,
         n.land_area_sqkm,
 
-        a.year,
+        coalesce(a.year, 2021) as year,
 
         -- Raw counts
         a.parks_count,

dbt/models/intermediate/toronto/int_neighbourhood__crime_summary.sql

@@ -16,12 +16,12 @@ crime_by_year as (
         neighbourhood_id,
         crime_year as year,
         sum(incident_count) as total_incidents,
-        sum(case when crime_type = 'Assault' then incident_count else 0 end) as assault_count,
-        sum(case when crime_type = 'Auto Theft' then incident_count else 0 end) as auto_theft_count,
-        sum(case when crime_type = 'Break and Enter' then incident_count else 0 end) as break_enter_count,
-        sum(case when crime_type = 'Robbery' then incident_count else 0 end) as robbery_count,
-        sum(case when crime_type = 'Theft Over' then incident_count else 0 end) as theft_over_count,
-        sum(case when crime_type = 'Homicide' then incident_count else 0 end) as homicide_count,
+        sum(case when crime_type = 'assault' then incident_count else 0 end) as assault_count,
+        sum(case when crime_type = 'auto_theft' then incident_count else 0 end) as auto_theft_count,
+        sum(case when crime_type = 'break_and_enter' then incident_count else 0 end) as break_enter_count,
+        sum(case when crime_type = 'robbery' then incident_count else 0 end) as robbery_count,
+        sum(case when crime_type = 'theft_over' then incident_count else 0 end) as theft_over_count,
+        sum(case when crime_type = 'homicide' then incident_count else 0 end) as homicide_count,
         avg(rate_per_100k) as avg_rate_per_100k
     from crime
     group by neighbourhood_id, crime_year
@@ -64,15 +64,17 @@ crime_summary as (
         w.robbery_count,
         w.theft_over_count,
         w.homicide_count,
-        w.avg_rate_per_100k,
         w.yoy_change_pct,
 
-        -- Crime rate per 100K population
-        case
-            when n.population > 0
-            then round(w.total_incidents::numeric / n.population * 100000, 2)
-            else null
-        end as crime_rate_per_100k
+        -- Crime rate per 100K population (use source data avg, or calculate if population available)
+        coalesce(
+            w.avg_rate_per_100k,
+            case
+                when n.population > 0
+                then round(w.total_incidents::numeric / n.population * 100000, 2)
+                else null
+            end
+        ) as crime_rate_per_100k
 
     from neighbourhoods n
     inner join with_yoy w on n.neighbourhood_id = w.neighbourhood_id

dbt/models/intermediate/toronto/int_neighbourhood__demographics.sql

@@ -17,7 +17,8 @@ demographics as (
         n.geometry,
         n.land_area_sqkm,
 
-        c.census_year,
+        -- Use census_year from census data, or fall back to dim_neighbourhood's year
+        coalesce(c.census_year, n.census_year, 2021) as census_year,
         c.population,
         c.population_density,
         c.median_household_income,

dbt/models/intermediate/toronto/int_neighbourhood__housing.sql

@@ -20,7 +20,7 @@ housing as (
         n.neighbourhood_name,
         n.geometry,
 
-        coalesce(r.year, c.census_year) as year,
+        coalesce(r.year, c.census_year, 2021) as year,
 
         -- Census housing metrics
         c.pct_owner_occupied,

dbt/models/intermediate/toronto/int_rentals__neighbourhood_allocated.sql

@@ -42,10 +42,10 @@ pivoted as (
     select
         neighbourhood_id,
         year,
-        max(case when bedroom_type = 'Two Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed,
-        max(case when bedroom_type = 'One Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_1bed,
-        max(case when bedroom_type = 'Bachelor' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_bachelor,
-        max(case when bedroom_type = 'Three Bedroom +' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_3bed,
+        max(case when bedroom_type = '2bed' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed,
+        max(case when bedroom_type = '1bed' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_1bed,
+        max(case when bedroom_type = 'bachelor' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_bachelor,
+        max(case when bedroom_type = '3bed' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_3bed,
         avg(vacancy_rate) as vacancy_rate,
         sum(rental_units_estimate) as total_rental_units
     from allocated

dbt/models/intermediate/toronto/int_rentals__toronto_cma.sql

@@ -0,0 +1,25 @@
+-- Intermediate: Toronto CMA rental metrics by year
+-- Aggregates rental data to city-wide averages by year
+-- Source: StatCan CMHC data at CMA level
+-- Grain: One row per year
+
+with rentals as (
+    select * from {{ ref('stg_cmhc__rentals') }}
+),
+
+-- Pivot bedroom types to columns
+yearly_rentals as (
+    select
+        year,
+        max(case when bedroom_type = 'bachelor' then avg_rent end) as avg_rent_bachelor,
+        max(case when bedroom_type = '1bed' then avg_rent end) as avg_rent_1bed,
+        max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_2bed,
+        max(case when bedroom_type = '3bed' then avg_rent end) as avg_rent_3bed,
+        -- Use 2-bedroom as standard reference
+        max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_standard,
+        max(vacancy_rate) as vacancy_rate
+    from rentals
+    group by year
+)
+
+select * from yearly_rentals

dbt/models/intermediate/toronto/int_year_spine.sql

@@ -0,0 +1,11 @@
+-- Intermediate: Year spine for analysis
+-- Creates a row for each year from 2014-2025
+-- Used to drive time-series analysis across all data sources
+
+with years as (
+    -- Generate years from available data sources
+    -- Crime data: 2014-2024, Rentals: 2019-2025
+    select generate_series(2014, 2025) as year
+)
+
+select year from years

dbt/models/marts/mart_neighbourhood_overview.sql

@@ -1,110 +0,0 @@
--- Mart: Neighbourhood Overview with Composite Livability Score
--- Dashboard Tab: Overview
--- Grain: One row per neighbourhood per year
-
-with demographics as (
-    select * from {{ ref('int_neighbourhood__demographics') }}
-),
-
-housing as (
-    select * from {{ ref('int_neighbourhood__housing') }}
-),
-
-crime as (
-    select * from {{ ref('int_neighbourhood__crime_summary') }}
-),
-
-amenities as (
-    select * from {{ ref('int_neighbourhood__amenity_scores') }}
-),
-
--- Compute percentile ranks for scoring components
-percentiles as (
-    select
-        d.neighbourhood_id,
-        d.neighbourhood_name,
-        d.geometry,
-        d.census_year as year,
-        d.population,
-        d.median_household_income,
-
-        -- Safety score: inverse of crime rate (higher = safer)
-        case
-            when c.crime_rate_per_100k is not null
-            then 100 - percent_rank() over (
-                partition by d.census_year
-                order by c.crime_rate_per_100k
-            ) * 100
-            else null
-        end as safety_score,
-
-        -- Affordability score: inverse of rent-to-income ratio
-        case
-            when h.rent_to_income_pct is not null
-            then 100 - percent_rank() over (
-                partition by d.census_year
-                order by h.rent_to_income_pct
-            ) * 100
-            else null
-        end as affordability_score,
-
-        -- Amenity score: based on amenities per capita
-        case
-            when a.total_amenities_per_1000 is not null
-            then percent_rank() over (
-                partition by d.census_year
-                order by a.total_amenities_per_1000
-            ) * 100
-            else null
-        end as amenity_score,
-
-        -- Raw metrics for reference
-        c.crime_rate_per_100k,
-        h.rent_to_income_pct,
-        h.avg_rent_2bed,
-        a.total_amenities_per_1000
-
-    from demographics d
-    left join housing h
-        on d.neighbourhood_id = h.neighbourhood_id
-        and d.census_year = h.year
-    left join crime c
-        on d.neighbourhood_id = c.neighbourhood_id
-        and d.census_year = c.year
-    left join amenities a
-        on d.neighbourhood_id = a.neighbourhood_id
-        and d.census_year = a.year
-),
-
-final as (
-    select
-        neighbourhood_id,
-        neighbourhood_name,
-        geometry,
-        year,
-        population,
-        median_household_income,
-
-        -- Component scores (0-100)
-        round(safety_score::numeric, 1) as safety_score,
-        round(affordability_score::numeric, 1) as affordability_score,
-        round(amenity_score::numeric, 1) as amenity_score,
-
-        -- Composite livability score: safety (30%), affordability (40%), amenities (30%)
-        round(
-            (coalesce(safety_score, 50) * 0.30 +
-             coalesce(affordability_score, 50) * 0.40 +
-             coalesce(amenity_score, 50) * 0.30)::numeric,
-            1
-        ) as livability_score,
-
-        -- Raw metrics
-        crime_rate_per_100k,
-        rent_to_income_pct,
-        avg_rent_2bed,
-        total_amenities_per_1000
-
-    from percentiles
-)
-
-select * from final

dbt/models/marts/toronto/_marts.yml

@@ -6,7 +6,7 @@ models:
     columns:
       - name: rental_id
         description: "Unique rental record identifier"
-        tests:
+        data_tests:
           - unique
           - not_null
 
@@ -17,11 +17,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry for mapping"
@@ -41,11 +41,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry for mapping"
@@ -63,11 +63,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry for mapping"
@@ -77,7 +77,7 @@ models:
         description: "100 = city average crime rate"
       - name: safety_tier
         description: "Safety tier (1=safest, 5=highest crime)"
-        tests:
+        data_tests:
           - accepted_values:
               arguments:
                 values: [1, 2, 3, 4, 5]
@@ -89,11 +89,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry for mapping"
@@ -103,7 +103,7 @@ models:
         description: "100 = city average income"
       - name: income_quintile
         description: "Income quintile (1-5)"
-        tests:
+        data_tests:
           - accepted_values:
               arguments:
                 values: [1, 2, 3, 4, 5]
@@ -115,11 +115,11 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood identifier"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry for mapping"
@@ -129,7 +129,7 @@ models:
         description: "100 = city average amenities"
       - name: amenity_tier
         description: "Amenity tier (1=best, 5=lowest)"
-        tests:
+        data_tests:
           - accepted_values:
               arguments:
                 values: [1, 2, 3, 4, 5]

dbt/models/marts/toronto/mart_neighbourhood_overview.sql

@@ -0,0 +1,153 @@
+-- Mart: Neighbourhood Overview with Composite Livability Score
+-- Dashboard Tab: Overview
+-- Grain: One row per neighbourhood per year
+-- Time spine: Years 2014-2025 (driven by crime/rental data availability)
+
+with years as (
+    select * from {{ ref('int_year_spine') }}
+),
+
+neighbourhoods as (
+    select * from {{ ref('stg_toronto__neighbourhoods') }}
+),
+
+-- Create base: all neighbourhoods × all years
+neighbourhood_years as (
+    select
+        n.neighbourhood_id,
+        n.neighbourhood_name,
+        n.geometry,
+        y.year
+    from neighbourhoods n
+    cross join years y
+),
+
+-- Census data (available for 2016, 2021)
+-- For each year, use the most recent census data available
+census as (
+    select * from {{ ref('stg_toronto__census') }}
+),
+
+census_mapped as (
+    select
+        ny.neighbourhood_id,
+        ny.year,
+        c.population,
+        c.unemployment_rate,
+        c.pct_bachelors_or_higher as education_bachelors_pct
+    from neighbourhood_years ny
+    left join census c on ny.neighbourhood_id = c.neighbourhood_id
+        -- Use census year <= analysis year, prefer most recent
+        and c.census_year = (
+            select max(c2.census_year)
+            from {{ ref('stg_toronto__census') }} c2
+            where c2.neighbourhood_id = ny.neighbourhood_id
+            and c2.census_year <= ny.year
+        )
+),
+
+-- CMA-level census data (for income - not available at neighbourhood level)
+cma_census as (
+    select * from {{ ref('int_census__toronto_cma') }}
+),
+
+-- Crime data (2014-2024)
+crime as (
+    select * from {{ ref('int_neighbourhood__crime_summary') }}
+),
+
+-- Rentals (2019-2025) - CMA level applied to all neighbourhoods
+rentals as (
+    select * from {{ ref('int_rentals__toronto_cma') }}
+),
+
+-- Compute scores
+scored as (
+    select
+        ny.neighbourhood_id,
+        ny.neighbourhood_name,
+        ny.geometry,
+        ny.year,
+        cm.population,
+        -- Use CMA-level income (neighbourhood-level not available in Toronto Open Data)
+        cma.median_household_income,
+
+        -- Safety score: inverse of crime rate (higher = safer)
+        case
+            when cr.crime_rate_per_100k is not null
+            then 100 - percent_rank() over (
+                partition by ny.year
+                order by cr.crime_rate_per_100k
+            ) * 100
+            else null
+        end as safety_score,
+
+        -- Affordability score: inverse of rent-to-income ratio
+        -- Using CMA-level income since neighbourhood-level not available
+        case
+            when cma.median_household_income > 0 and r.avg_rent_standard > 0
+            then 100 - percent_rank() over (
+                partition by ny.year
+                order by (r.avg_rent_standard * 12 / cma.median_household_income)
+            ) * 100
+            else null
+        end as affordability_score,
+
+        -- Raw metrics
+        cr.crime_rate_per_100k,
+        case
+            when cma.median_household_income > 0 and r.avg_rent_standard > 0
+            then round((r.avg_rent_standard * 12 / cma.median_household_income) * 100, 2)
+            else null
+        end as rent_to_income_pct,
+        r.avg_rent_standard as avg_rent_2bed,
+        r.vacancy_rate
+
+    from neighbourhood_years ny
+    left join census_mapped cm
+        on ny.neighbourhood_id = cm.neighbourhood_id
+        and ny.year = cm.year
+    left join cma_census cma
+        on ny.year = cma.year
+    left join crime cr
+        on ny.neighbourhood_id = cr.neighbourhood_id
+        and ny.year = cr.year
+    left join rentals r
+        on ny.year = r.year
+),
+
+final as (
+    select
+        neighbourhood_id,
+        neighbourhood_name,
+        geometry,
+        year,
+        population,
+        median_household_income,
+
+        -- Component scores (0-100)
+        round(safety_score::numeric, 1) as safety_score,
+        round(affordability_score::numeric, 1) as affordability_score,
+        -- TODO: Replace with actual amenity score when fact_amenities is populated
+        -- Currently uses neutral placeholder (50.0) which affects livability_score accuracy
+        50.0 as amenity_score,
+
+        -- Composite livability score: safety (40%), affordability (40%), amenities (20%)
+        round(
+            (coalesce(safety_score, 50) * 0.40 +
+             coalesce(affordability_score, 50) * 0.40 +
+             50 * 0.20)::numeric,
+            1
+        ) as livability_score,
+
+        -- Raw metrics
+        crime_rate_per_100k,
+        rent_to_income_pct,
+        avg_rent_2bed,
+        vacancy_rate,
+        null::numeric as total_amenities_per_1000
+
+    from scored
+)
+
+select * from final

dbt/models/shared/_shared.yml

@@ -0,0 +1,33 @@
+version: 2
+
+models:
+  - name: stg_dimensions__time
+    description: "Staged time dimension - shared across all projects"
+    columns:
+      - name: date_key
+        description: "Primary key (YYYYMM format)"
+        data_tests:
+          - unique
+          - not_null
+      - name: full_date
+        description: "First day of month"
+        data_tests:
+          - not_null
+      - name: year
+        description: "Calendar year"
+        data_tests:
+          - not_null
+      - name: month
+        description: "Month number (1-12)"
+        data_tests:
+          - not_null
+      - name: quarter
+        description: "Quarter (1-4)"
+        data_tests:
+          - not_null
+      - name: month_name
+        description: "Month name"
+        data_tests:
+          - not_null
+      - name: is_month_start
+        description: "Always true (monthly grain)"

dbt/models/shared/_sources.yml

@@ -0,0 +1,25 @@
+version: 2
+
+sources:
+  - name: shared
+    description: "Shared dimension tables used across all dashboards"
+    database: portfolio
+    schema: public
+    tables:
+      - name: dim_time
+        description: "Time dimension (monthly grain) - shared across all projects"
+        columns:
+          - name: date_key
+            description: "Primary key (YYYYMM format)"
+          - name: full_date
+            description: "First day of month"
+          - name: year
+            description: "Calendar year"
+          - name: month
+            description: "Month number (1-12)"
+          - name: quarter
+            description: "Quarter (1-4)"
+          - name: month_name
+            description: "Month name"
+          - name: is_month_start
+            description: "Always true (monthly grain)"

dbt/models/shared/stg_dimensions__time.sql

@@ -1,9 +1,10 @@
 -- Staged time dimension
--- Source: dim_time table
+-- Source: shared.dim_time table
 -- Grain: One row per month
+-- Note: Shared dimension used across all dashboard projects
 
 with source as (
-    select * from {{ source('toronto_housing', 'dim_time') }}
+    select * from {{ source('shared', 'dim_time') }}
 ),
 
 staged as (

dbt/models/staging/stg_dimensions__cmhc_zones.sql

@@ -1,18 +0,0 @@
--- Staged CMHC zone dimension
--- Source: dim_cmhc_zone table
--- Grain: One row per zone
-
-with source as (
-    select * from {{ source('toronto_housing', 'dim_cmhc_zone') }}
-),
-
-staged as (
-    select
-        zone_key,
-        zone_code,
-        zone_name,
-        geometry
-    from source
-)
-
-select * from staged

dbt/models/staging/toronto/_sources.yml

@@ -1,10 +1,10 @@
 version: 2
 
 sources:
-  - name: toronto_housing
-    description: "Toronto housing data loaded from CMHC and City of Toronto sources"
+  - name: toronto
+    description: "Toronto data loaded from CMHC and City of Toronto sources"
     database: portfolio
-    schema: public
+    schema: raw_toronto
     tables:
       - name: fact_rentals
         description: "CMHC annual rental survey data by zone and bedroom type"
@@ -16,12 +16,6 @@ sources:
           - name: zone_key
             description: "Foreign key to dim_cmhc_zone"
 
-      - name: dim_time
-        description: "Time dimension (monthly grain)"
-        columns:
-          - name: date_key
-            description: "Primary key (YYYYMMDD format)"
-
       - name: dim_cmhc_zone
         description: "CMHC zone dimension with geometry"
         columns:

dbt/models/staging/toronto/_staging.yml

@@ -6,25 +6,16 @@ models:
     columns:
       - name: rental_id
         description: "Unique identifier for rental record"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: date_key
         description: "Date dimension key (YYYYMMDD)"
-        tests:
+        data_tests:
           - not_null
       - name: zone_key
         description: "CMHC zone dimension key"
-        tests:
-          - not_null
-
-  - name: stg_dimensions__time
-    description: "Staged time dimension"
-    columns:
-      - name: date_key
-        description: "Date dimension key (YYYYMMDD)"
-        tests:
-          - unique
+        data_tests:
           - not_null
 
   - name: stg_dimensions__cmhc_zones
@@ -32,12 +23,12 @@ models:
     columns:
       - name: zone_key
         description: "Zone dimension key"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: zone_code
         description: "CMHC zone code"
-        tests:
+        data_tests:
           - unique
           - not_null
 
@@ -46,12 +37,12 @@ models:
     columns:
       - name: neighbourhood_id
         description: "Neighbourhood primary key"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: neighbourhood_name
         description: "Official neighbourhood name"
-        tests:
+        data_tests:
           - not_null
       - name: geometry
         description: "PostGIS geometry (POLYGON)"
@@ -61,16 +52,16 @@ models:
     columns:
       - name: census_id
         description: "Census record identifier"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: neighbourhood_id
         description: "Neighbourhood foreign key"
-        tests:
+        data_tests:
           - not_null
       - name: census_year
         description: "Census year (2016, 2021)"
-        tests:
+        data_tests:
           - not_null
 
   - name: stg_toronto__crime
@@ -78,16 +69,16 @@ models:
     columns:
       - name: crime_id
         description: "Crime record identifier"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: neighbourhood_id
         description: "Neighbourhood foreign key"
-        tests:
+        data_tests:
           - not_null
       - name: crime_type
         description: "Type of crime"
-        tests:
+        data_tests:
           - not_null
 
   - name: stg_toronto__amenities
@@ -95,16 +86,16 @@ models:
     columns:
       - name: amenity_id
         description: "Amenity record identifier"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: neighbourhood_id
         description: "Neighbourhood foreign key"
-        tests:
+        data_tests:
           - not_null
       - name: amenity_type
         description: "Type of amenity"
-        tests:
+        data_tests:
           - not_null
 
   - name: stg_cmhc__zone_crosswalk
@@ -112,18 +103,18 @@ models:
     columns:
       - name: crosswalk_id
         description: "Crosswalk record identifier"
-        tests:
+        data_tests:
           - unique
           - not_null
       - name: cmhc_zone_code
         description: "CMHC zone code"
-        tests:
+        data_tests:
           - not_null
       - name: neighbourhood_id
         description: "Neighbourhood foreign key"
-        tests:
+        data_tests:
           - not_null
       - name: area_weight
         description: "Proportional area weight (0-1)"
-        tests:
+        data_tests:
           - not_null

dbt/models/staging/toronto/stg_cmhc__rentals.sql

@@ -1,9 +1,13 @@
 -- Staged CMHC rental market survey data
--- Source: fact_rentals table loaded from CMHC CSV exports
+-- Source: fact_rentals table loaded from CMHC/StatCan
 -- Grain: One row per zone per bedroom type per survey year
 
 with source as (
-    select * from {{ source('toronto_housing', 'fact_rentals') }}
+    select
+        f.*,
+        t.year as survey_year
+    from {{ source('toronto', 'fact_rentals') }} f
+    join {{ source('shared', 'dim_time') }} t on f.date_key = t.date_key
 ),
 
 staged as (
@@ -11,6 +15,7 @@ staged as (
         id as rental_id,
         date_key,
         zone_key,
+        survey_year as year,
         bedroom_type,
         universe as rental_universe,
         avg_rent,

dbt/models/staging/toronto/stg_cmhc__zone_crosswalk.sql

@@ -3,7 +3,7 @@
 -- Grain: One row per zone-neighbourhood intersection
 
 with source as (
-    select * from {{ source('toronto_housing', 'bridge_cmhc_neighbourhood') }}
+    select * from {{ source('toronto', 'bridge_cmhc_neighbourhood') }}
 ),
 
 staged as (

dbt/models/staging/toronto/stg_dimensions__cmhc_zones.sql

@@ -0,0 +1,19 @@
+-- Staged CMHC zone dimension
+-- Source: dim_cmhc_zone table
+-- Grain: One row per zone
+
+with source as (
+    select * from {{ source('toronto', 'dim_cmhc_zone') }}
+),
+
+staged as (
+    select
+        zone_key,
+        zone_code,
+        zone_name
+        -- geometry column excluded: CMHC does not provide zone boundaries
+        -- Spatial analysis uses dim_neighbourhood geometry instead
+    from source
+)
+
+select * from staged

dbt/models/staging/toronto/stg_toronto__amenities.sql

@@ -3,7 +3,7 @@
 -- Grain: One row per neighbourhood per amenity type per year
 
 with source as (
-    select * from {{ source('toronto_housing', 'fact_amenities') }}
+    select * from {{ source('toronto', 'fact_amenities') }}
 ),
 
 staged as (

dbt/models/staging/toronto/stg_toronto__census.sql

@@ -3,7 +3,7 @@
 -- Grain: One row per neighbourhood per census year
 
 with source as (
-    select * from {{ source('toronto_housing', 'fact_census') }}
+    select * from {{ source('toronto', 'fact_census') }}
 ),
 
 staged as (

dbt/models/staging/toronto/stg_toronto__crime.sql

@@ -3,7 +3,7 @@
 -- Grain: One row per neighbourhood per year per crime type
 
 with source as (
-    select * from {{ source('toronto_housing', 'fact_crime') }}
+    select * from {{ source('toronto', 'fact_crime') }}
 ),
 
 staged as (

dbt/models/staging/toronto/stg_toronto__neighbourhoods.sql

@@ -3,7 +3,7 @@
 -- Grain: One row per neighbourhood (158 total)
 
 with source as (
-    select * from {{ source('toronto_housing', 'dim_neighbourhood') }}
+    select * from {{ source('toronto', 'dim_neighbourhood') }}
 ),
 
 staged as (

dbt/profiles.yml

@@ -1,4 +1,4 @@
-toronto_housing:
+portfolio:
   target: dev
   outputs:
     dev:

docker-compose.yml

@@ -1,6 +1,6 @@
 services:
   db:
-    image: postgis/postgis:16-3.4
+    image: ${POSTGIS_IMAGE:-postgis/postgis:16-3.4}
     container_name: portfolio-db
     restart: unless-stopped
     ports:

docs/CONTRIBUTING.md

@@ -50,9 +50,11 @@ The app runs at `http://localhost:8050`.
 
 ```bash
 make test       # Run tests
+make test-cov   # Run tests with coverage
 make lint       # Check code style
 make format     # Auto-format code
-make ci         # Run all checks (lint + test)
+make typecheck  # Run mypy type checker
+make ci         # Run all checks (lint, typecheck, test)
 make dbt-run    # Run dbt transformations
 make dbt-test   # Run dbt tests
 ```
@@ -247,13 +249,23 @@ def layout(slug: str = "") -> dmc.Container:
 To add the page to the sidebar, edit `portfolio_app/components/sidebar.py`:
 
 ```python
-NAV_ITEMS = [
-    {"label": "Home", "href": "/", "icon": "tabler:home"},
-    {"label": "Your Page", "href": "/your-page", "icon": "tabler:star"},
+# For main pages (Home, About, Blog, etc.)
+NAV_ITEMS_MAIN = [
+    {"path": "/", "icon": "tabler:home", "label": "Home"},
+    {"path": "/your-page", "icon": "tabler:star", "label": "Your Page"},
+    # ...
+]
+
+# For project/dashboard pages
+NAV_ITEMS_PROJECTS = [
+    {"path": "/projects", "icon": "tabler:folder", "label": "Projects"},
+    {"path": "/your-dashboard", "icon": "tabler:chart-bar", "label": "Your Dashboard"},
     # ...
 ]
 ```
 
+The sidebar uses icon buttons with tooltips. Each item needs `path`, `icon` (Tabler icon name), and `label` (tooltip text).
+
 ### URL Routing Summary
 
 | File Location | URL |
@@ -278,7 +290,7 @@ Dashboard tabs are in `portfolio_app/pages/toronto/tabs/`.
 
 import dash_mantine_components as dmc
 
-from portfolio_app.figures.choropleth import create_choropleth
+from portfolio_app.figures.toronto.choropleth import create_choropleth
 from portfolio_app.toronto.demo_data import get_demo_data
 
 
@@ -327,13 +339,13 @@ dmc.TabsPanel(create_your_tab_layout(), value="your-tab"),
 
 ## Creating Figure Factories
 
-Figure factories are in `portfolio_app/figures/`. They create reusable Plotly figures.
+Figure factories are organized by dashboard domain under `portfolio_app/figures/{domain}/`.
 
 ### Pattern
 
 ```python
-# figures/your_chart.py
-"""Your chart type factory."""
+# figures/toronto/your_chart.py
+"""Your chart type factory for Toronto dashboard."""
 
 import plotly.express as px
 import plotly.graph_objects as go
@@ -370,7 +382,7 @@ def create_your_chart(
 ### Export from `__init__.py`
 
 ```python
-# figures/__init__.py
+# figures/toronto/__init__.py
 from .your_chart import create_your_chart
 
 __all__ = [
@@ -379,6 +391,14 @@ __all__ = [
 ]
 ```
 
+### Importing Figure Factories
+
+```python
+# In callbacks or tabs
+from portfolio_app.figures.toronto import create_choropleth_figure
+from portfolio_app.figures.toronto.bar_charts import create_ranking_bar
+```
+
 ---
 
 ## Branch Workflow

docs/DATABASE_SCHEMA.md

@@ -0,0 +1,335 @@
+# Database Schema
+
+This document describes the PostgreSQL/PostGIS database schema for the Toronto Neighbourhood Dashboard.
+
+## Entity Relationship Diagram
+
+```mermaid
+erDiagram
+    dim_time {
+        int date_key PK
+        date full_date UK
+        int year
+        int month
+        int quarter
+        string month_name
+        bool is_month_start
+    }
+
+    dim_cmhc_zone {
+        int zone_key PK
+        string zone_code UK
+        string zone_name
+        geometry geometry
+    }
+
+    dim_neighbourhood {
+        int neighbourhood_id PK
+        string name
+        geometry geometry
+        int population
+        numeric land_area_sqkm
+        numeric pop_density_per_sqkm
+        numeric pct_bachelors_or_higher
+        numeric median_household_income
+        numeric pct_owner_occupied
+        numeric pct_renter_occupied
+        int census_year
+    }
+
+    dim_policy_event {
+        int event_id PK
+        date event_date
+        date effective_date
+        string level
+        string category
+        string title
+        text description
+        string expected_direction
+        string source_url
+        string confidence
+    }
+
+    fact_rentals {
+        int id PK
+        int date_key FK
+        int zone_key FK
+        string bedroom_type
+        int universe
+        numeric avg_rent
+        numeric median_rent
+        numeric vacancy_rate
+        numeric availability_rate
+        numeric turnover_rate
+        numeric rent_change_pct
+        string reliability_code
+    }
+
+    fact_census {
+        int id PK
+        int neighbourhood_id FK
+        int census_year
+        int population
+        numeric population_density
+        numeric median_household_income
+        numeric average_household_income
+        numeric unemployment_rate
+        numeric pct_bachelors_or_higher
+        numeric pct_owner_occupied
+        numeric pct_renter_occupied
+        numeric median_age
+        numeric average_dwelling_value
+    }
+
+    fact_crime {
+        int id PK
+        int neighbourhood_id FK
+        int year
+        string crime_type
+        int count
+        numeric rate_per_100k
+    }
+
+    fact_amenities {
+        int id PK
+        int neighbourhood_id FK
+        string amenity_type
+        int count
+        int year
+    }
+
+    bridge_cmhc_neighbourhood {
+        int id PK
+        string cmhc_zone_code FK
+        int neighbourhood_id FK
+        numeric weight
+    }
+
+    dim_time ||--o{ fact_rentals : "date_key"
+    dim_cmhc_zone ||--o{ fact_rentals : "zone_key"
+    dim_neighbourhood ||--o{ fact_census : "neighbourhood_id"
+    dim_neighbourhood ||--o{ fact_crime : "neighbourhood_id"
+    dim_neighbourhood ||--o{ fact_amenities : "neighbourhood_id"
+    dim_cmhc_zone ||--o{ bridge_cmhc_neighbourhood : "zone_code"
+    dim_neighbourhood ||--o{ bridge_cmhc_neighbourhood : "neighbourhood_id"
+```
+
+## Schema Layers
+
+### Database Schemas
+
+| Schema | Purpose | Managed By |
+|--------|---------|------------|
+| `public` | Shared dimensions (dim_time) | SQLAlchemy |
+| `raw_toronto` | Toronto dimension and fact tables | SQLAlchemy |
+| `stg_toronto` | Toronto staging models | dbt |
+| `int_toronto` | Toronto intermediate models | dbt |
+| `mart_toronto` | Toronto analytical tables | dbt |
+
+### Raw Toronto Schema (raw_toronto)
+
+Toronto-specific tables loaded by SQLAlchemy:
+
+| Table | Source | Description |
+|-------|--------|-------------|
+| `dim_neighbourhood` | City of Toronto API | 158 neighbourhood boundaries |
+| `dim_cmhc_zone` | CMHC | ~20 rental market zones |
+| `dim_policy_event` | Manual | Policy events for annotation |
+| `fact_census` | City of Toronto API | Census profile data |
+| `fact_crime` | Toronto Police API | Crime statistics |
+| `fact_amenities` | City of Toronto API | Amenity counts |
+| `fact_rentals` | CMHC Data Files | Rental market survey data |
+| `bridge_cmhc_neighbourhood` | Computed | Zone-neighbourhood mapping |
+
+### Public Schema
+
+Shared dimensions used across all projects:
+
+| Table | Description |
+|-------|-------------|
+| `dim_time` | Time dimension (monthly grain) |
+
+### Staging Schema - stg_toronto (dbt)
+
+Staging models provide 1:1 cleaned representations of source data:
+
+| Model | Source Table | Purpose |
+|-------|-------------|---------|
+| `stg_toronto__neighbourhoods` | raw.neighbourhoods | Cleaned boundaries with standardized names |
+| `stg_toronto__census` | raw.census_profiles | Typed census metrics |
+| `stg_cmhc__rentals` | raw.cmhc_rentals | Validated rental data |
+| `stg_toronto__crime` | raw.crime_data | Standardized crime categories |
+| `stg_toronto__amenities` | raw.amenities | Typed amenity counts |
+| `stg_dimensions__time` | generated | Time dimension |
+| `stg_dimensions__cmhc_zones` | raw.cmhc_zones | CMHC zone boundaries |
+| `stg_cmhc__zone_crosswalk` | raw.crosswalk | Zone-neighbourhood mapping |
+
+### Marts Schema - mart_toronto (dbt)
+
+Analytical tables ready for dashboard consumption:
+
+| Model | Grain | Purpose |
+|-------|-------|---------|
+| `mart_neighbourhood_overview` | neighbourhood | Composite livability scores |
+| `mart_neighbourhood_housing` | neighbourhood | Housing and rent metrics |
+| `mart_neighbourhood_safety` | neighbourhood × year | Crime rate calculations |
+| `mart_neighbourhood_demographics` | neighbourhood | Income, age, population metrics |
+| `mart_neighbourhood_amenities` | neighbourhood | Amenity accessibility scores |
+| `mart_toronto_rentals` | zone × month | Time-series rental analysis |
+
+## Table Details
+
+### Dimension Tables
+
+#### dim_time
+Time dimension for date-based analysis. Grain: one row per month.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| date_key | INTEGER | PK | Surrogate key (YYYYMM format) |
+| full_date | DATE | UNIQUE, NOT NULL | First day of month |
+| year | INTEGER | NOT NULL | Calendar year |
+| month | INTEGER | NOT NULL | Month number (1-12) |
+| quarter | INTEGER | NOT NULL | Quarter (1-4) |
+| month_name | VARCHAR(20) | NOT NULL | Month name |
+| is_month_start | BOOLEAN | DEFAULT TRUE | Always true (monthly grain) |
+
+#### dim_cmhc_zone
+CMHC rental market zones (~20 zones covering Toronto).
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| zone_key | INTEGER | PK, AUTO | Surrogate key |
+| zone_code | VARCHAR(10) | UNIQUE, NOT NULL | CMHC zone identifier |
+| zone_name | VARCHAR(100) | NOT NULL | Zone display name |
+| geometry | GEOMETRY(POLYGON) | SRID 4326 | PostGIS zone boundary |
+
+#### dim_neighbourhood
+Toronto's 158 official neighbourhoods.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| neighbourhood_id | INTEGER | PK | City-assigned ID |
+| name | VARCHAR(100) | NOT NULL | Neighbourhood name |
+| geometry | GEOMETRY(POLYGON) | SRID 4326 | PostGIS boundary |
+| population | INTEGER | | Total population |
+| land_area_sqkm | NUMERIC(10,4) | | Area in km² |
+| pop_density_per_sqkm | NUMERIC(10,2) | | Population density |
+| pct_bachelors_or_higher | NUMERIC(5,2) | | Education rate |
+| median_household_income | NUMERIC(12,2) | | Median income |
+| pct_owner_occupied | NUMERIC(5,2) | | Owner occupancy rate |
+| pct_renter_occupied | NUMERIC(5,2) | | Renter occupancy rate |
+| census_year | INTEGER | DEFAULT 2021 | Census reference year |
+
+#### dim_policy_event
+Policy events for time-series annotation (rent control, interest rates, etc.).
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| event_id | INTEGER | PK, AUTO | Surrogate key |
+| event_date | DATE | NOT NULL | Announcement date |
+| effective_date | DATE | | Implementation date |
+| level | VARCHAR(20) | NOT NULL | federal/provincial/municipal |
+| category | VARCHAR(20) | NOT NULL | monetary/tax/regulatory/supply/economic |
+| title | VARCHAR(200) | NOT NULL | Event title |
+| description | TEXT | | Detailed description |
+| expected_direction | VARCHAR(10) | NOT NULL | bearish/bullish/neutral |
+| source_url | VARCHAR(500) | | Reference link |
+| confidence | VARCHAR(10) | DEFAULT 'medium' | high/medium/low |
+
+### Fact Tables
+
+#### fact_rentals
+CMHC rental market survey data. Grain: zone × bedroom type × survey date.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| id | INTEGER | PK, AUTO | Surrogate key |
+| date_key | INTEGER | FK → dim_time | Survey date reference |
+| zone_key | INTEGER | FK → dim_cmhc_zone | CMHC zone reference |
+| bedroom_type | VARCHAR(20) | NOT NULL | bachelor/1-bed/2-bed/3+bed/total |
+| universe | INTEGER | | Total rental units |
+| avg_rent | NUMERIC(10,2) | | Average rent |
+| median_rent | NUMERIC(10,2) | | Median rent |
+| vacancy_rate | NUMERIC(5,2) | | Vacancy percentage |
+| availability_rate | NUMERIC(5,2) | | Availability percentage |
+| turnover_rate | NUMERIC(5,2) | | Turnover percentage |
+| rent_change_pct | NUMERIC(5,2) | | Year-over-year change |
+| reliability_code | VARCHAR(2) | | CMHC data quality code |
+
+#### fact_census
+Census statistics. Grain: neighbourhood × census year.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| id | INTEGER | PK, AUTO | Surrogate key |
+| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference |
+| census_year | INTEGER | NOT NULL | 2016, 2021, etc. |
+| population | INTEGER | | Total population |
+| population_density | NUMERIC(10,2) | | People per km² |
+| median_household_income | NUMERIC(12,2) | | Median income |
+| average_household_income | NUMERIC(12,2) | | Average income |
+| unemployment_rate | NUMERIC(5,2) | | Unemployment % |
+| pct_bachelors_or_higher | NUMERIC(5,2) | | Education rate |
+| pct_owner_occupied | NUMERIC(5,2) | | Owner rate |
+| pct_renter_occupied | NUMERIC(5,2) | | Renter rate |
+| median_age | NUMERIC(5,2) | | Median resident age |
+| average_dwelling_value | NUMERIC(12,2) | | Average home value |
+
+#### fact_crime
+Crime statistics. Grain: neighbourhood × year × crime type.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| id | INTEGER | PK, AUTO | Surrogate key |
+| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference |
+| year | INTEGER | NOT NULL | Calendar year |
+| crime_type | VARCHAR(50) | NOT NULL | Crime category |
+| count | INTEGER | NOT NULL | Number of incidents |
+| rate_per_100k | NUMERIC(10,2) | | Rate per 100k population |
+
+#### fact_amenities
+Amenity counts. Grain: neighbourhood × amenity type × year.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| id | INTEGER | PK, AUTO | Surrogate key |
+| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference |
+| amenity_type | VARCHAR(50) | NOT NULL | parks/schools/transit/etc. |
+| count | INTEGER | NOT NULL | Number of amenities |
+| year | INTEGER | NOT NULL | Reference year |
+
+### Bridge Tables
+
+#### bridge_cmhc_neighbourhood
+Maps CMHC zones to neighbourhoods with area-based weights for data disaggregation.
+
+| Column | Type | Constraints | Description |
+|--------|------|-------------|-------------|
+| id | INTEGER | PK, AUTO | Surrogate key |
+| cmhc_zone_code | VARCHAR(10) | FK → dim_cmhc_zone | Zone reference |
+| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference |
+| weight | NUMERIC(5,4) | NOT NULL | Proportional weight (0-1) |
+
+## Indexes
+
+| Table | Index | Columns | Purpose |
+|-------|-------|---------|---------|
+| fact_rentals | ix_fact_rentals_date_zone | date_key, zone_key | Time-series queries |
+| fact_census | ix_fact_census_neighbourhood_year | neighbourhood_id, census_year | Census lookups |
+| fact_crime | ix_fact_crime_neighbourhood_year | neighbourhood_id, year | Crime trends |
+| fact_crime | ix_fact_crime_type | crime_type | Crime filtering |
+| fact_amenities | ix_fact_amenities_neighbourhood_year | neighbourhood_id, year | Amenity queries |
+| fact_amenities | ix_fact_amenities_type | amenity_type | Amenity filtering |
+| bridge_cmhc_neighbourhood | ix_bridge_cmhc_zone | cmhc_zone_code | Zone lookups |
+| bridge_cmhc_neighbourhood | ix_bridge_neighbourhood | neighbourhood_id | Neighbourhood lookups |
+
+## PostGIS Extensions
+
+The database requires PostGIS for geospatial operations:
+
+```sql
+CREATE EXTENSION IF NOT EXISTS postgis;
+```
+
+All geometry columns use SRID 4326 (WGS84) for compatibility with web mapping libraries.

docs/PROJECT_REFERENCE.md

@@ -76,7 +76,8 @@ portfolio_app/
 ├── components/               # Shared UI components
 ├── content/blog/             # Markdown blog articles
 ├── errors/                   # Exception handling
-├── figures/                  # Plotly figure factories
+├── figures/
+│   └── toronto/              # Toronto figure factories
 ├── pages/
 │   ├── home.py
 │   ├── about.py
@@ -91,15 +92,26 @@ portfolio_app/
 │       ├── dashboard.py
 │       ├── methodology.py
 │       ├── tabs/             # 5 tab layouts
-│       └── callbacks/        # Dashboard interactions
+│       └── callbacks/        # Dashboard interactions (map_callbacks, chart_callbacks, selection_callbacks)
 ├── toronto/                  # Data logic
-│   ├── parsers/              # API extraction
-│   ├── loaders/              # Database operations
+│   ├── parsers/              # API extraction (geo, toronto_open_data, toronto_police, cmhc)
+│   ├── loaders/              # Database operations (base, cmhc, cmhc_crosswalk)
 │   ├── schemas/              # Pydantic models
-│   ├── models/               # SQLAlchemy ORM
+│   ├── models/               # SQLAlchemy ORM (raw_toronto schema)
+│   ├── services/             # Query functions (neighbourhood_service, geometry_service)
 │   └── demo_data.py          # Sample data
 └── utils/
     └── markdown_loader.py    # Blog article loading
+
+dbt/                          # dbt project: portfolio
+├── models/
+│   ├── shared/               # Cross-domain dimensions
+│   ├── staging/toronto/      # Toronto staging models
+│   ├── intermediate/toronto/ # Toronto intermediate models
+│   └── marts/toronto/        # Toronto mart tables
+
+notebooks/
+└── toronto/                  # Toronto documentation notebooks
 ```
 
 ---
@@ -143,10 +155,20 @@ CMHC Zones (~20) ← Rental data (Census Tract aligned)
 | `fact_rentals` | Fact | Rental data by CMHC zone |
 | `fact_amenities` | Fact | Amenity counts by neighbourhood |
 
-### dbt Layers
+### dbt Project: `portfolio`
+
+**Model Structure:**
+```
+dbt/models/
+├── shared/                 # Cross-domain dimensions (stg_dimensions__time)
+├── staging/toronto/        # Toronto staging models
+├── intermediate/toronto/   # Toronto intermediate models
+└── marts/toronto/          # Toronto mart tables
+```
 
 | Layer | Naming | Example |
 |-------|--------|---------|
+| Shared | `stg_dimensions__*` | `stg_dimensions__time` |
 | Staging | `stg_{source}__{entity}` | `stg_toronto__neighbourhoods` |
 | Intermediate | `int_{domain}__{transform}` | `int_neighbourhood__demographics` |
 | Marts | `mart_{domain}` | `mart_neighbourhood_overview` |
@@ -241,16 +263,25 @@ LOG_LEVEL=INFO
 | Target | Purpose |
 |--------|---------|
 | `setup` | Install deps, create .env, init pre-commit |
-| `docker-up` | Start PostgreSQL + PostGIS |
+| `docker-up` | Start PostgreSQL + PostGIS (auto-detects x86/ARM) |
 | `docker-down` | Stop containers |
+| `docker-logs` | View container logs |
 | `db-init` | Initialize database schema |
+| `db-reset` | Drop and recreate database (DESTRUCTIVE) |
+| `load-data` | Load Toronto data from APIs, seed dev data |
+| `load-toronto-only` | Load Toronto data without dbt or seeding |
+| `seed-data` | Seed sample development data |
 | `run` | Start Dash dev server |
 | `test` | Run pytest |
-| `dbt-run` | Run dbt models |
-| `dbt-test` | Run dbt tests |
+| `test-cov` | Run pytest with coverage |
 | `lint` | Run ruff linter |
 | `format` | Run ruff formatter |
-| `ci` | Run all checks |
+| `typecheck` | Run mypy type checker |
+| `ci` | Run all checks (lint, typecheck, test) |
+| `dbt-run` | Run dbt models |
+| `dbt-test` | Run dbt tests |
+| `dbt-docs` | Generate and serve dbt documentation |
+| `clean` | Remove build artifacts and caches |
 
 ---
 

docs/project-lessons-learned/INDEX.md

@@ -10,6 +10,7 @@ This folder contains lessons learned from sprints and development work. These le
 
 | Date | Sprint/Phase | Title | Tags |
 |------|--------------|-------|------|
+| 2026-02-01 | Sprint 10 | [Formspree Integration with Dash Callbacks](./sprint-10-formspree-dash-integration.md) | formspree, dash, callbacks, forms, spam-protection, honeypot, ajax |
 | 2026-01-17 | Sprint 9 | [Gitea Labels API Requires Org Context](./sprint-9-gitea-labels-user-repos.md) | gitea, mcp, api, labels, projman, configuration |
 | 2026-01-17 | Sprint 9 | [Always Read CLAUDE.md Before Asking Questions](./sprint-9-read-claude-md-first.md) | projman, claude-code, context, documentation, workflow |
 | 2026-01-17 | Sprint 9-10 | [Graceful Error Handling in Service Layers](./sprint-9-10-graceful-error-handling.md) | python, postgresql, error-handling, dash, graceful-degradation, arm64 |

docs/project-lessons-learned/sprint-10-formspree-dash-integration.md

@@ -0,0 +1,70 @@
+# Sprint 10 - Formspree Integration with Dash Callbacks
+
+## Context
+Implementing a contact form on a Dash portfolio site that submits to Formspree, a third-party form handling service.
+
+## Insights
+
+### Formspree AJAX Submission
+Formspree supports AJAX submissions (no page redirect) when you:
+1. POST with `Content-Type: application/json`
+2. Include `Accept: application/json` header
+3. Send form data as JSON body
+
+This returns a JSON response instead of redirecting to a thank-you page, which is ideal for single-page Dash applications.
+
+### Dash Multi-Output Callbacks for Forms
+When handling form submission with validation and feedback, use a multi-output callback pattern:
+
+```python
+@callback(
+    Output("feedback-container", "children"),  # Success/error alert
+    Output("submit-button", "loading"),        # Button loading state
+    Output("field-1", "value"),                # Clear on success
+    Output("field-2", "value"),                # Clear on success
+    Output("field-1", "error"),                # Field-level errors
+    Output("field-2", "error"),                # Field-level errors
+    Input("submit-button", "n_clicks"),
+    State("field-1", "value"),
+    State("field-2", "value"),
+    prevent_initial_call=True,
+)
+```
+
+Use `no_update` for outputs you don't want to change (e.g., keep form values on validation error, only clear on success).
+
+### Honeypot Spam Protection
+Simple and effective bot protection without CAPTCHA:
+1. Add a hidden text input field (CSS: `position: absolute; left: -9999px`)
+2. Set `tabIndex=-1` and `autoComplete="off"` to prevent accidental filling
+3. In callback, check if honeypot has value - if yes, it's a bot
+4. For bots: return fake success (don't reveal detection)
+5. For humans: proceed with real submission
+
+Formspree also accepts `_gotcha` as a honeypot field name in the JSON payload.
+
+## Code Pattern
+
+```python
+# Honeypot check - bots fill hidden fields
+if honeypot_value:
+    # Fake success - don't let bots know they were caught
+    return (_create_success_alert(), False, "", "", None, None)
+
+# Real submission for humans
+response = requests.post(
+    FORMSPREE_ENDPOINT,
+    json=form_data,
+    headers={"Accept": "application/json", "Content-Type": "application/json"},
+    timeout=10,
+)
+```
+
+## Prevention/Best Practices
+- Always use `timeout` parameter with `requests.post()` to avoid hanging
+- Wrap external API calls in try/except for network errors
+- Return user-friendly error messages, not technical details
+- Use DMC's `required=True` and `error` props for form validation feedback
+
+## Tags
+formspree, dash, callbacks, forms, spam-protection, honeypot, ajax, python, requests, validation

docs/runbooks/adding-dashboard.md

@@ -0,0 +1,265 @@
+# Runbook: Adding a New Dashboard
+
+This runbook describes how to add a new data dashboard to the portfolio application.
+
+## Prerequisites
+
+- [ ] Data sources identified and accessible
+- [ ] Database schema designed
+- [ ] Basic Dash/Plotly familiarity
+
+## Directory Structure
+
+Create the following structure:
+
+### Application Code (`portfolio_app/`)
+
+```
+portfolio_app/
+├── pages/
+│   └── {dashboard_name}/
+│       ├── dashboard.py      # Main layout with tabs
+│       ├── methodology.py    # Data sources and methods page
+│       ├── tabs/
+│       │   ├── __init__.py
+│       │   ├── overview.py   # Overview tab layout
+│       │   └── ...           # Additional tab layouts
+│       └── callbacks/
+│           ├── __init__.py
+│           └── ...           # Callback modules
+├── {dashboard_name}/         # Data logic (outside pages/)
+│   ├── __init__.py
+│   ├── parsers/              # API/CSV extraction
+│   │   └── __init__.py
+│   ├── loaders/              # Database operations
+│   │   └── __init__.py
+│   ├── schemas/              # Pydantic models
+│   │   └── __init__.py
+│   └── models/               # SQLAlchemy ORM (schema: raw_{dashboard_name})
+│       └── __init__.py
+└── figures/
+    └── {dashboard_name}/     # Figure factories for this dashboard
+        ├── __init__.py
+        └── ...               # Chart modules
+```
+
+### dbt Models (`dbt/models/`)
+
+```
+dbt/models/
+├── staging/
+│   └── {dashboard_name}/     # Staging models
+│       ├── _sources.yml      # Source definitions (schema: raw_{dashboard_name})
+│       ├── _staging.yml      # Model tests/docs
+│       └── stg_*.sql         # Staging models
+├── intermediate/
+│   └── {dashboard_name}/     # Intermediate models
+│       ├── _intermediate.yml
+│       └── int_*.sql
+└── marts/
+    └── {dashboard_name}/     # Mart tables
+        ├── _marts.yml
+        └── mart_*.sql
+```
+
+### Documentation (`notebooks/`)
+
+```
+notebooks/
+└── {dashboard_name}/         # Domain subdirectories
+    ├── overview/
+    ├── ...
+```
+
+## Step-by-Step Checklist
+
+### 1. Data Layer
+
+- [ ] Create Pydantic schemas in `{dashboard_name}/schemas/`
+- [ ] Create SQLAlchemy models in `{dashboard_name}/models/`
+- [ ] Create parsers in `{dashboard_name}/parsers/`
+- [ ] Create loaders in `{dashboard_name}/loaders/`
+- [ ] Add database migrations if needed
+
+### 2. Database Schema
+
+- [ ] Define schema constant in models (e.g., `RAW_FOOTBALL_SCHEMA = "raw_football"`)
+- [ ] Add `__table_args__ = {"schema": RAW_FOOTBALL_SCHEMA}` to all models
+- [ ] Update `scripts/db/init_schema.py` to create the new schema
+
+### 3. dbt Models
+
+Create dbt models in `dbt/models/`:
+
+- [ ] `staging/{dashboard_name}/_sources.yml` - Source definitions pointing to `raw_{dashboard_name}` schema
+- [ ] `staging/{dashboard_name}/stg_{source}__{entity}.sql` - Raw data cleaning
+- [ ] `intermediate/{dashboard_name}/int_{domain}__{transform}.sql` - Business logic
+- [ ] `marts/{dashboard_name}/mart_{domain}.sql` - Final analytical tables
+
+Update `dbt/dbt_project.yml` with new subdirectory config:
+```yaml
+models:
+  portfolio:
+    staging:
+      {dashboard_name}:
+        +materialized: view
+        +schema: stg_{dashboard_name}
+    intermediate:
+      {dashboard_name}:
+        +materialized: view
+        +schema: int_{dashboard_name}
+    marts:
+      {dashboard_name}:
+        +materialized: table
+        +schema: mart_{dashboard_name}
+```
+
+Follow naming conventions:
+- Staging: `stg_{source}__{entity}`
+- Intermediate: `int_{domain}__{transform}`
+- Marts: `mart_{domain}`
+
+### 4. Visualization Layer
+
+- [ ] Create figure factories in `figures/{dashboard_name}/`
+- [ ] Create `figures/{dashboard_name}/__init__.py` with exports
+- [ ] Follow the factory pattern: `create_{chart_type}_figure(data, **kwargs)`
+
+Import pattern:
+```python
+from portfolio_app.figures.{dashboard_name} import create_choropleth_figure
+```
+
+### 4. Dashboard Pages
+
+#### Main Dashboard (`pages/{dashboard_name}/dashboard.py`)
+
+```python
+import dash
+from dash import html, dcc
+import dash_mantine_components as dmc
+
+dash.register_page(
+    __name__,
+    path="/{dashboard_name}",
+    title="{Dashboard Title}",
+    description="{Description}"
+)
+
+def layout():
+    return dmc.Container([
+        # Header
+        dmc.Title("{Dashboard Title}", order=1),
+
+        # Tabs
+        dmc.Tabs([
+            dmc.TabsList([
+                dmc.TabsTab("Overview", value="overview"),
+                # Add more tabs
+            ]),
+            dmc.TabsPanel(overview_tab(), value="overview"),
+            # Add more panels
+        ], value="overview"),
+    ])
+```
+
+#### Tab Layouts (`pages/{dashboard_name}/tabs/`)
+
+- [ ] Create one file per tab
+- [ ] Export layout function from each
+
+#### Callbacks (`pages/{dashboard_name}/callbacks/`)
+
+- [ ] Create callback modules for interactivity
+- [ ] Import and register in dashboard.py
+
+### 5. Navigation
+
+Add to sidebar in `components/sidebar.py`:
+
+```python
+dmc.NavLink(
+    label="{Dashboard Name}",
+    href="/{dashboard_name}",
+    icon=DashIconify(icon="..."),
+)
+```
+
+### 6. Documentation
+
+- [ ] Create methodology page (`pages/{dashboard_name}/methodology.py`)
+- [ ] Document data sources
+- [ ] Document transformation logic
+- [ ] Add notebooks to `notebooks/{dashboard_name}/` if needed
+
+### 7. Testing
+
+- [ ] Add unit tests for parsers
+- [ ] Add unit tests for loaders
+- [ ] Add integration tests for callbacks
+- [ ] Run `make test`
+
+### 8. Final Verification
+
+- [ ] All pages render without errors
+- [ ] All callbacks respond correctly
+- [ ] Data loads successfully
+- [ ] dbt models run cleanly (`make dbt-run`)
+- [ ] Linting passes (`make lint`)
+- [ ] Tests pass (`make test`)
+
+## Example: Toronto Dashboard
+
+Reference implementation: `portfolio_app/pages/toronto/`
+
+Key files:
+- `dashboard.py` - Main layout with 5 tabs
+- `tabs/overview.py` - Livability scores, scatter plots
+- `callbacks/map_callbacks.py` - Choropleth interactions
+- `toronto/models/dimensions.py` - Dimension tables
+- `toronto/models/facts.py` - Fact tables
+
+## Common Patterns
+
+### Figure Factories
+
+```python
+# figures/choropleth.py
+def create_choropleth_figure(
+    gdf: gpd.GeoDataFrame,
+    value_column: str,
+    title: str,
+    **kwargs
+) -> go.Figure:
+    ...
+```
+
+### Callbacks
+
+```python
+# callbacks/map_callbacks.py
+@callback(
+    Output("neighbourhood-details", "children"),
+    Input("choropleth-map", "clickData"),
+)
+def update_details(click_data):
+    ...
+```
+
+### Data Loading
+
+```python
+# {dashboard_name}/loaders/load.py
+def load_data(session: Session) -> None:
+    # Parse from source
+    records = parse_source_data()
+
+    # Validate with Pydantic
+    validated = [Schema(**r) for r in records]
+
+    # Load to database
+    for record in validated:
+        session.add(Model(**record.model_dump()))
+
+    session.commit()
+```

docs/runbooks/deployment.md

@@ -0,0 +1,232 @@
+# Runbook: Deployment
+
+This runbook covers deployment procedures for the Analytics Portfolio application.
+
+## Environments
+
+| Environment | Branch | Server | URL |
+|-------------|--------|--------|-----|
+| Development | `development` | Local | http://localhost:8050 |
+| Staging | `staging` | Homelab (hotserv) | Internal |
+| Production | `main` | Bandit Labs VPS | https://leodata.science |
+
+## CI/CD Pipeline
+
+### Automatic Deployment
+
+Deployments are triggered automatically via Gitea Actions:
+
+1. **Push to `staging`** → Deploys to staging server
+2. **Push to `main`** → Deploys to production server
+
+### Workflow Files
+
+- `.gitea/workflows/ci.yml` - Runs linting and tests on all branches
+- `.gitea/workflows/deploy-staging.yml` - Staging deployment
+- `.gitea/workflows/deploy-production.yml` - Production deployment
+
+### Required Secrets
+
+Configure these in Gitea repository settings:
+
+| Secret | Description |
+|--------|-------------|
+| `STAGING_HOST` | Staging server hostname/IP |
+| `STAGING_USER` | SSH username for staging |
+| `STAGING_SSH_KEY` | Private key for staging SSH |
+| `PROD_HOST` | Production server hostname/IP |
+| `PROD_USER` | SSH username for production |
+| `PROD_SSH_KEY` | Private key for production SSH |
+
+## Manual Deployment
+
+### Prerequisites
+
+- SSH access to target server
+- Repository cloned at `~/apps/personal-portfolio`
+- Virtual environment created at `.venv`
+- Docker and Docker Compose installed
+- PostgreSQL container running
+
+### Steps
+
+```bash
+# 1. SSH to server
+ssh user@server
+
+# 2. Navigate to app directory
+cd ~/apps/personal-portfolio
+
+# 3. Pull latest changes
+git fetch origin {branch}
+git reset --hard origin/{branch}
+
+# 4. Activate virtual environment
+source .venv/bin/activate
+
+# 5. Install dependencies
+pip install -r requirements.txt
+
+# 6. Run database migrations (if any)
+# python -m alembic upgrade head
+
+# 7. Run dbt models
+cd dbt && dbt run --profiles-dir . && cd ..
+
+# 8. Restart application
+docker compose down
+docker compose up -d
+
+# 9. Verify health
+curl http://localhost:8050/health
+```
+
+## Rollback Procedure
+
+### Quick Rollback
+
+If deployment fails, rollback to previous commit:
+
+```bash
+# 1. Find previous working commit
+git log --oneline -10
+
+# 2. Reset to that commit
+git reset --hard {commit_hash}
+
+# 3. Restart services
+docker compose down
+docker compose up -d
+
+# 4. Verify
+curl http://localhost:8050/health
+```
+
+### Full Rollback (Database)
+
+If database changes need to be reverted:
+
+```bash
+# 1. Stop application
+docker compose down
+
+# 2. Restore database backup
+pg_restore -h localhost -U portfolio -d portfolio backup.dump
+
+# 3. Revert code
+git reset --hard {commit_hash}
+
+# 4. Run dbt at that version
+cd dbt && dbt run --profiles-dir . && cd ..
+
+# 5. Restart
+docker compose up -d
+```
+
+## Health Checks
+
+### Application Health
+
+```bash
+curl http://localhost:8050/health
+```
+
+Expected response:
+```json
+{"status": "healthy"}
+```
+
+### Database Health
+
+```bash
+docker compose exec postgres pg_isready -U portfolio
+```
+
+### Container Status
+
+```bash
+docker compose ps
+```
+
+## Monitoring
+
+### View Logs
+
+```bash
+# All services
+make logs
+
+# Specific service
+make logs SERVICE=postgres
+
+# Or directly
+docker compose logs -f
+```
+
+### Check Resource Usage
+
+```bash
+docker stats
+```
+
+## Troubleshooting
+
+### Application Won't Start
+
+1. Check container logs: `docker compose logs app`
+2. Verify environment variables: `cat .env`
+3. Check database connectivity: `docker compose exec postgres pg_isready`
+4. Verify port availability: `lsof -i :8050`
+
+### Database Connection Errors
+
+1. Check postgres container: `docker compose ps postgres`
+2. Verify DATABASE_URL in `.env`
+3. Check postgres logs: `docker compose logs postgres`
+4. Test connection: `docker compose exec postgres psql -U portfolio -c '\l'`
+
+### dbt Failures
+
+1. Check dbt logs: `cd dbt && dbt debug`
+2. Verify profiles.yml: `cat dbt/profiles.yml`
+3. Run with verbose output: `dbt run --debug`
+
+### Out of Memory
+
+1. Check memory usage: `free -h`
+2. Review container limits in docker-compose.yml
+3. Consider increasing swap or server resources
+
+## Backup Procedures
+
+### Database Backup
+
+```bash
+# Create backup
+docker compose exec postgres pg_dump -U portfolio portfolio > backup_$(date +%Y%m%d).sql
+
+# Compressed backup
+docker compose exec postgres pg_dump -U portfolio -Fc portfolio > backup_$(date +%Y%m%d).dump
+```
+
+### Restore from Backup
+
+```bash
+# From SQL file
+docker compose exec -T postgres psql -U portfolio portfolio < backup.sql
+
+# From dump file
+docker compose exec -T postgres pg_restore -U portfolio -d portfolio < backup.dump
+```
+
+## Deployment Checklist
+
+Before deploying to production:
+
+- [ ] All tests pass (`make test`)
+- [ ] Linting passes (`make lint`)
+- [ ] Staging deployment successful
+- [ ] Manual testing on staging complete
+- [ ] Database backup taken
+- [ ] Rollback plan confirmed
+- [ ] Team notified of deployment window

notebooks/README.md

@@ -1,17 +1,18 @@
-# Toronto Neighbourhood Dashboard - Notebooks
+# Dashboard Documentation Notebooks
 
-Documentation notebooks for the Toronto Neighbourhood Dashboard visualizations. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern.
+Documentation notebooks organized by dashboard project. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern.
 
 ## Directory Structure
 
 ```
 notebooks/
 ├── README.md              # This file
-├── overview/              # Overview tab visualizations
-├── housing/               # Housing tab visualizations
-├── safety/                # Safety tab visualizations
-├── demographics/          # Demographics tab visualizations
-└── amenities/             # Amenities tab visualizations
+└── toronto/               # Toronto Neighbourhood Dashboard
+    ├── overview/          # Overview tab visualizations
+    ├── housing/           # Housing tab visualizations
+    ├── safety/            # Safety tab visualizations
+    ├── demographics/      # Demographics tab visualizations
+    └── amenities/         # Amenities tab visualizations
 ```
 
 ## Notebook Template

notebooks/toronto/amenities/amenity_index_choropleth.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -50,8 +55,8 @@
     "    transit_per_1000,\n",
     "    total_amenities,\n",
     "    population\n",
-    "FROM mart_neighbourhood_amenities\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
+    "FROM public_marts.mart_neighbourhood_amenities\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n",
     "ORDER BY total_amenities_per_1000 DESC\n",
     "\"\"\"\n",
     "\n",
@@ -75,17 +80,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
     "import json\n",
     "\n",
+    "import geopandas as gpd\n",
+    "\n",
     "gdf = gpd.GeoDataFrame(\n",
-    "    df,\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    crs='EPSG:4326'\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
     ")\n",
     "\n",
     "geojson = json.loads(gdf.to_json())\n",
-    "data = df.drop(columns=['geometry']).to_dict('records')"
+    "data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
    ]
   },
   {
@@ -101,7 +105,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'total_amenities_per_1000', 'amenity_index', 'amenity_tier']].head(10)"
+    "df[\n",
+    "    [\"neighbourhood_name\", \"total_amenities_per_1000\", \"amenity_index\", \"amenity_tier\"]\n",
+    "].head(10)"
    ]
   },
   {
@@ -112,7 +118,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`."
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`."
    ]
   },
   {
@@ -122,18 +128,24 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
     "\n",
     "fig = create_choropleth_figure(\n",
     "    geojson=geojson,\n",
     "    data=data,\n",
-    "    location_key='neighbourhood_id',\n",
-    "    color_column='total_amenities_per_1000',\n",
-    "    hover_data=['neighbourhood_name', 'amenity_index', 'parks_per_1000', 'schools_per_1000'],\n",
-    "    color_scale='Greens',\n",
-    "    title='Toronto Amenities per 1,000 Population',\n",
+    "    location_key=\"neighbourhood_id\",\n",
+    "    color_column=\"total_amenities_per_1000\",\n",
+    "    hover_data=[\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"amenity_index\",\n",
+    "        \"parks_per_1000\",\n",
+    "        \"schools_per_1000\",\n",
+    "    ],\n",
+    "    color_scale=\"Greens\",\n",
+    "    title=\"Toronto Amenities per 1,000 Population\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/amenities/amenity_radar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -44,8 +49,8 @@
     "    transit_index,\n",
     "    amenity_index,\n",
     "    amenity_tier\n",
-    "FROM mart_neighbourhood_amenities\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
+    "FROM public_marts.mart_neighbourhood_amenities\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n",
     "ORDER BY amenity_index DESC\n",
     "\"\"\"\n",
     "\n",
@@ -74,8 +79,8 @@
     "bottom_5 = df.tail(5)\n",
     "\n",
     "# Prepare radar data\n",
-    "categories = ['Parks', 'Schools', 'Transit']\n",
-    "index_columns = ['parks_index', 'schools_index', 'transit_index']"
+    "categories = [\"Parks\", \"Schools\", \"Transit\"]\n",
+    "index_columns = [\"parks_index\", \"schools_index\", \"transit_index\"]"
    ]
   },
   {
@@ -92,9 +97,29 @@
    "outputs": [],
    "source": [
     "print(\"Top 5 Amenity-Rich Neighbourhoods:\")\n",
-    "display(top_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])\n",
+    "display(\n",
+    "    top_5[\n",
+    "        [\n",
+    "            \"neighbourhood_name\",\n",
+    "            \"parks_index\",\n",
+    "            \"schools_index\",\n",
+    "            \"transit_index\",\n",
+    "            \"amenity_index\",\n",
+    "        ]\n",
+    "    ]\n",
+    ")\n",
     "print(\"\\nBottom 5 Underserved Neighbourhoods:\")\n",
-    "display(bottom_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])"
+    "display(\n",
+    "    bottom_5[\n",
+    "        [\n",
+    "            \"neighbourhood_name\",\n",
+    "            \"parks_index\",\n",
+    "            \"schools_index\",\n",
+    "            \"transit_index\",\n",
+    "            \"amenity_index\",\n",
+    "        ]\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -105,7 +130,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_radar` from `portfolio_app.figures.radar`."
+    "Uses `create_radar` from `portfolio_app.figures.toronto.radar`."
    ]
   },
   {
@@ -115,28 +140,21 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.radar import create_radar_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.radar import create_comparison_radar\n",
     "\n",
     "# Compare top neighbourhood vs city average (100)\n",
     "top_hood = top_5.iloc[0]\n",
+    "metrics = [\"parks_index\", \"schools_index\", \"transit_index\"]\n",
     "\n",
-    "data = [\n",
-    "    {\n",
-    "        'name': top_hood['neighbourhood_name'],\n",
-    "        'values': [top_hood['parks_index'], top_hood['schools_index'], top_hood['transit_index']],\n",
-    "        'categories': categories\n",
-    "    },\n",
-    "    {\n",
-    "        'name': 'City Average',\n",
-    "        'values': [100, 100, 100],\n",
-    "        'categories': categories\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "fig = create_radar_figure(\n",
-    "    data=data,\n",
+    "fig = create_comparison_radar(\n",
+    "    selected_data=top_hood.to_dict(),\n",
+    "    average_data={\"parks_index\": 100, \"schools_index\": 100, \"transit_index\": 100},\n",
+    "    metrics=metrics,\n",
+    "    selected_name=top_hood[\"neighbourhood_name\"],\n",
+    "    average_name=\"City Average\",\n",
     "    title=f\"Amenity Profile: {top_hood['neighbourhood_name']} vs City Average\",\n",
     ")\n",
     "\n",

notebooks/toronto/amenities/transit_accessibility_bar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -44,8 +49,8 @@
     "    transit_count,\n",
     "    population,\n",
     "    amenity_tier\n",
-    "FROM mart_neighbourhood_amenities\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
+    "FROM public_marts.mart_neighbourhood_amenities\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n",
     "  AND transit_per_1000 IS NOT NULL\n",
     "ORDER BY transit_per_1000 DESC\n",
     "\"\"\"\n",
@@ -70,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = df.head(20).to_dict('records')"
+    "data = df.head(20).to_dict(\"records\")"
    ]
   },
   {
@@ -86,7 +91,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'transit_per_1000', 'transit_index', 'transit_count']].head(10)"
+    "df[[\"neighbourhood_name\", \"transit_per_1000\", \"transit_index\", \"transit_count\"]].head(\n",
+    "    10\n",
+    ")"
    ]
   },
   {
@@ -97,7 +104,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`."
+    "Uses `create_horizontal_bar` from `portfolio_app.figures.toronto.bar_charts`."
    ]
   },
   {
@@ -107,17 +114,18 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_horizontal_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_horizontal_bar\n",
     "\n",
     "fig = create_horizontal_bar(\n",
     "    data=data,\n",
-    "    name_column='neighbourhood_name',\n",
-    "    value_column='transit_per_1000',\n",
-    "    title='Top 20 Neighbourhoods by Transit Accessibility',\n",
-    "    color='#00BCD4',\n",
-    "    value_format='.2f',\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"transit_per_1000\",\n",
+    "    title=\"Top 20 Neighbourhoods by Transit Accessibility\",\n",
+    "    color=\"#00BCD4\",\n",
+    "    value_format=\".2f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -136,7 +144,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(f\"City-wide Transit Statistics:\")\n",
+    "print(\"City-wide Transit Statistics:\")\n",
     "print(f\"  Total Transit Stops: {df['transit_count'].sum():,.0f}\")\n",
     "print(f\"  Average per 1,000 pop: {df['transit_per_1000'].mean():.2f}\")\n",
     "print(f\"  Median per 1,000 pop: {df['transit_per_1000'].median():.2f}\")\n",

notebooks/toronto/demographics/age_distribution.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -45,8 +50,8 @@
     "    population,\n",
     "    income_quintile,\n",
     "    pct_renter_occupied\n",
-    "FROM mart_neighbourhood_demographics\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
+    "FROM public_marts.mart_neighbourhood_demographics\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n",
     "  AND median_age IS NOT NULL\n",
     "ORDER BY median_age DESC\n",
     "\"\"\"\n",
@@ -72,13 +77,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "city_avg = df['city_avg_age'].iloc[0]\n",
-    "df['age_category'] = df['median_age'].apply(\n",
-    "    lambda x: 'Younger' if x < city_avg else 'Older'\n",
+    "city_avg = df[\"city_avg_age\"].iloc[0]\n",
+    "df[\"age_category\"] = df[\"median_age\"].apply(\n",
+    "    lambda x: \"Younger\" if x < city_avg else \"Older\"\n",
     ")\n",
-    "df['age_deviation'] = df['median_age'] - city_avg\n",
+    "df[\"age_deviation\"] = df[\"median_age\"] - city_avg\n",
     "\n",
-    "data = df.to_dict('records')"
+    "data = df.to_dict(\"records\")"
    ]
   },
   {
@@ -96,9 +101,13 @@
    "source": [
     "print(f\"City Average Age: {city_avg:.1f}\")\n",
     "print(\"\\nYoungest Neighbourhoods:\")\n",
-    "display(df.tail(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])\n",
+    "display(\n",
+    "    df.tail(5)[[\"neighbourhood_name\", \"median_age\", \"age_index\", \"pct_renter_occupied\"]]\n",
+    ")\n",
     "print(\"\\nOldest Neighbourhoods:\")\n",
-    "display(df.head(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])"
+    "display(\n",
+    "    df.head(5)[[\"neighbourhood_name\", \"median_age\", \"age_index\", \"pct_renter_occupied\"]]\n",
+    ")"
    ]
   },
   {
@@ -109,7 +118,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`."
+    "Uses `create_ranking_bar` from `portfolio_app.figures.toronto.bar_charts`."
    ]
   },
   {
@@ -119,20 +128,21 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_ranking_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_ranking_bar\n",
     "\n",
     "fig = create_ranking_bar(\n",
     "    data=data,\n",
-    "    name_column='neighbourhood_name',\n",
-    "    value_column='median_age',\n",
-    "    title='Youngest & Oldest Neighbourhoods (Median Age)',\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"median_age\",\n",
+    "    title=\"Youngest & Oldest Neighbourhoods (Median Age)\",\n",
     "    top_n=10,\n",
     "    bottom_n=10,\n",
-    "    color_top='#FF9800',  # Orange for older\n",
-    "    color_bottom='#2196F3',  # Blue for younger\n",
-    "    value_format='.1f',\n",
+    "    color_top=\"#FF9800\",  # Orange for older\n",
+    "    color_bottom=\"#2196F3\",  # Blue for younger\n",
+    "    value_format=\".1f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -153,7 +163,7 @@
    "source": [
     "# Age by income quintile\n",
     "print(\"Median Age by Income Quintile:\")\n",
-    "df.groupby('income_quintile')['median_age'].mean().round(1)"
+    "df.groupby(\"income_quintile\")[\"median_age\"].mean().round(1)"
    ]
   }
  ],

notebooks/toronto/demographics/income_choropleth.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -47,8 +52,8 @@
     "    income_quintile,\n",
     "    population,\n",
     "    unemployment_rate\n",
-    "FROM mart_neighbourhood_demographics\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
+    "FROM public_marts.mart_neighbourhood_demographics\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n",
     "ORDER BY median_household_income DESC\n",
     "\"\"\"\n",
     "\n",
@@ -73,19 +78,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
     "import json\n",
     "\n",
-    "df['income_thousands'] = df['median_household_income'] / 1000\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "df[\"income_thousands\"] = df[\"median_household_income\"] / 1000\n",
     "\n",
     "gdf = gpd.GeoDataFrame(\n",
-    "    df,\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    crs='EPSG:4326'\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
     ")\n",
     "\n",
     "geojson = json.loads(gdf.to_json())\n",
-    "data = df.drop(columns=['geometry']).to_dict('records')"
+    "data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
    ]
   },
   {
@@ -101,7 +105,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'median_household_income', 'income_index', 'income_quintile']].head(10)"
+    "df[\n",
+    "    [\"neighbourhood_name\", \"median_household_income\", \"income_index\", \"income_quintile\"]\n",
+    "].head(10)"
    ]
   },
   {
@@ -112,7 +118,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`."
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`."
    ]
   },
   {
@@ -122,18 +128,19 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
     "\n",
     "fig = create_choropleth_figure(\n",
     "    geojson=geojson,\n",
     "    data=data,\n",
-    "    location_key='neighbourhood_id',\n",
-    "    color_column='median_household_income',\n",
-    "    hover_data=['neighbourhood_name', 'income_index', 'income_quintile'],\n",
-    "    color_scale='Viridis',\n",
-    "    title='Toronto Median Household Income by Neighbourhood',\n",
+    "    location_key=\"neighbourhood_id\",\n",
+    "    color_column=\"median_household_income\",\n",
+    "    hover_data=[\"neighbourhood_name\", \"income_index\", \"income_quintile\"],\n",
+    "    color_scale=\"Viridis\",\n",
+    "    title=\"Toronto Median Household Income by Neighbourhood\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",
@@ -153,7 +160,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.groupby('income_quintile')['median_household_income'].agg(['count', 'mean', 'min', 'max']).round(0)"
+    "df.groupby(\"income_quintile\")[\"median_household_income\"].agg(\n",
+    "    [\"count\", \"mean\", \"min\", \"max\"]\n",
+    ").round(0)"
    ]
   }
  ],

notebooks/toronto/demographics/population_density_bar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -44,8 +49,8 @@
     "    land_area_sqkm,\n",
     "    median_household_income,\n",
     "    pct_renter_occupied\n",
-    "FROM mart_neighbourhood_demographics\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
+    "FROM public_marts.mart_neighbourhood_demographics\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n",
     "  AND population_density IS NOT NULL\n",
     "ORDER BY population_density DESC\n",
     "\"\"\"\n",
@@ -70,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = df.head(20).to_dict('records')"
+    "data = df.head(20).to_dict(\"records\")"
    ]
   },
   {
@@ -86,7 +91,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'population_density', 'population', 'land_area_sqkm']].head(10)"
+    "df[[\"neighbourhood_name\", \"population_density\", \"population\", \"land_area_sqkm\"]].head(\n",
+    "    10\n",
+    ")"
    ]
   },
   {
@@ -97,7 +104,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`."
+    "Uses `create_horizontal_bar` from `portfolio_app.figures.toronto.bar_charts`."
    ]
   },
   {
@@ -107,17 +114,18 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_horizontal_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_horizontal_bar\n",
     "\n",
     "fig = create_horizontal_bar(\n",
     "    data=data,\n",
-    "    name_column='neighbourhood_name',\n",
-    "    value_column='population_density',\n",
-    "    title='Top 20 Most Dense Neighbourhoods',\n",
-    "    color='#9C27B0',\n",
-    "    value_format=',.0f',\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"population_density\",\n",
+    "    title=\"Top 20 Most Dense Neighbourhoods\",\n",
+    "    color=\"#9C27B0\",\n",
+    "    value_format=\",.0f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -136,7 +144,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(f\"City-wide Statistics:\")\n",
+    "print(\"City-wide Statistics:\")\n",
     "print(f\"  Total Population: {df['population'].sum():,.0f}\")\n",
     "print(f\"  Total Area: {df['land_area_sqkm'].sum():,.1f} sq km\")\n",
     "print(f\"  Average Density: {df['population_density'].mean():,.0f} per sq km\")\n",

notebooks/toronto/housing/affordability_choropleth.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -47,8 +52,8 @@
     "    avg_rent_2bed,\n",
     "    median_household_income,\n",
     "    is_affordable\n",
-    "FROM mart_neighbourhood_housing\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
+    "FROM public_marts.mart_neighbourhood_housing\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n",
     "ORDER BY affordability_index ASC\n",
     "\"\"\"\n",
     "\n",
@@ -73,17 +78,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
     "import json\n",
     "\n",
+    "import geopandas as gpd\n",
+    "\n",
     "gdf = gpd.GeoDataFrame(\n",
-    "    df,\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    crs='EPSG:4326'\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
     ")\n",
     "\n",
     "geojson = json.loads(gdf.to_json())\n",
-    "data = df.drop(columns=['geometry']).to_dict('records')"
+    "data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
    ]
   },
   {
@@ -99,7 +103,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'affordability_index', 'rent_to_income_pct', 'avg_rent_2bed', 'is_affordable']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"affordability_index\",\n",
+    "        \"rent_to_income_pct\",\n",
+    "        \"avg_rent_2bed\",\n",
+    "        \"is_affordable\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -110,7 +122,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `color_column`: 'affordability_index'\n",
@@ -124,18 +136,19 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
     "\n",
     "fig = create_choropleth_figure(\n",
     "    geojson=geojson,\n",
     "    data=data,\n",
-    "    location_key='neighbourhood_id',\n",
-    "    color_column='affordability_index',\n",
-    "    hover_data=['neighbourhood_name', 'rent_to_income_pct', 'avg_rent_2bed'],\n",
-    "    color_scale='RdYlGn_r',  # Reversed: lower index (affordable) = green\n",
-    "    title='Toronto Housing Affordability Index',\n",
+    "    location_key=\"neighbourhood_id\",\n",
+    "    color_column=\"affordability_index\",\n",
+    "    hover_data=[\"neighbourhood_name\", \"rent_to_income_pct\", \"avg_rent_2bed\"],\n",
+    "    color_scale=\"RdYlGn_r\",  # Reversed: lower index (affordable) = green\n",
+    "    title=\"Toronto Housing Affordability Index\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/housing/rent_trend_line.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "# City-wide average rent by year\n",
     "query = \"\"\"\n",
@@ -45,8 +50,8 @@
     "    AVG(avg_rent_2bed) as avg_rent_2bed,\n",
     "    AVG(avg_rent_3bed) as avg_rent_3bed,\n",
     "    AVG(rent_yoy_change_pct) as avg_yoy_change\n",
-    "FROM mart_neighbourhood_housing\n",
-    "WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_housing)\n",
+    "FROM public_marts.mart_neighbourhood_housing\n",
+    "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_housing)\n",
     "GROUP BY year\n",
     "ORDER BY year\n",
     "\"\"\"\n",
@@ -73,23 +78,25 @@
    "outputs": [],
    "source": [
     "# Create date column from year\n",
-    "df['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n",
+    "df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
     "\n",
     "# Melt for multi-line chart\n",
     "df_melted = df.melt(\n",
-    "    id_vars=['year', 'date'],\n",
-    "    value_vars=['avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed'],\n",
-    "    var_name='bedroom_type',\n",
-    "    value_name='avg_rent'\n",
+    "    id_vars=[\"year\", \"date\"],\n",
+    "    value_vars=[\"avg_rent_bachelor\", \"avg_rent_1bed\", \"avg_rent_2bed\", \"avg_rent_3bed\"],\n",
+    "    var_name=\"bedroom_type\",\n",
+    "    value_name=\"avg_rent\",\n",
     ")\n",
     "\n",
     "# Clean labels\n",
-    "df_melted['bedroom_type'] = df_melted['bedroom_type'].map({\n",
-    "    'avg_rent_bachelor': 'Bachelor',\n",
-    "    'avg_rent_1bed': '1 Bedroom',\n",
-    "    'avg_rent_2bed': '2 Bedroom',\n",
-    "    'avg_rent_3bed': '3 Bedroom'\n",
-    "})"
+    "df_melted[\"bedroom_type\"] = df_melted[\"bedroom_type\"].map(\n",
+    "    {\n",
+    "        \"avg_rent_bachelor\": \"Bachelor\",\n",
+    "        \"avg_rent_1bed\": \"1 Bedroom\",\n",
+    "        \"avg_rent_2bed\": \"2 Bedroom\",\n",
+    "        \"avg_rent_3bed\": \"3 Bedroom\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -105,7 +112,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['year', 'avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed', 'avg_yoy_change']]"
+    "df[\n",
+    "    [\n",
+    "        \"year\",\n",
+    "        \"avg_rent_bachelor\",\n",
+    "        \"avg_rent_1bed\",\n",
+    "        \"avg_rent_2bed\",\n",
+    "        \"avg_rent_3bed\",\n",
+    "        \"avg_yoy_change\",\n",
+    "    ]\n",
+    "]"
    ]
   },
   {
@@ -116,7 +132,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_price_time_series` from `portfolio_app.figures.time_series`.\n",
+    "Uses `create_price_time_series` from `portfolio_app.figures.toronto.time_series`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `date_column`: 'date'\n",
@@ -131,18 +147,19 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.time_series import create_price_time_series\n",
+    "sys.path.insert(0, \"../..\")\n",
     "\n",
-    "data = df_melted.to_dict('records')\n",
+    "from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
+    "\n",
+    "data = df_melted.to_dict(\"records\")\n",
     "\n",
     "fig = create_price_time_series(\n",
     "    data=data,\n",
-    "    date_column='date',\n",
-    "    price_column='avg_rent',\n",
-    "    group_column='bedroom_type',\n",
-    "    title='Toronto Average Rent Trend (5 Years)',\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"avg_rent\",\n",
+    "    group_column=\"bedroom_type\",\n",
+    "    title=\"Toronto Average Rent Trend (5 Years)\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -163,7 +180,7 @@
    "source": [
     "# Show year-over-year changes\n",
     "print(\"Year-over-Year Rent Change (%)\")\n",
-    "df[['year', 'avg_yoy_change']].dropna()"
+    "df[[\"year\", \"avg_yoy_change\"]].dropna()"
    ]
   }
  ],

notebooks/toronto/housing/tenure_breakdown_bar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -44,8 +49,8 @@
     "    income_quintile,\n",
     "    total_rental_units,\n",
     "    average_dwelling_value\n",
-    "FROM mart_neighbourhood_housing\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
+    "FROM public_marts.mart_neighbourhood_housing\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n",
     "  AND pct_owner_occupied IS NOT NULL\n",
     "ORDER BY pct_renter_occupied DESC\n",
     "\"\"\"\n",
@@ -73,18 +78,17 @@
    "source": [
     "# Prepare for stacked bar\n",
     "df_stacked = df.melt(\n",
-    "    id_vars=['neighbourhood_name', 'income_quintile'],\n",
-    "    value_vars=['pct_owner_occupied', 'pct_renter_occupied'],\n",
-    "    var_name='tenure_type',\n",
-    "    value_name='percentage'\n",
+    "    id_vars=[\"neighbourhood_name\", \"income_quintile\"],\n",
+    "    value_vars=[\"pct_owner_occupied\", \"pct_renter_occupied\"],\n",
+    "    var_name=\"tenure_type\",\n",
+    "    value_name=\"percentage\",\n",
     ")\n",
     "\n",
-    "df_stacked['tenure_type'] = df_stacked['tenure_type'].map({\n",
-    "    'pct_owner_occupied': 'Owner',\n",
-    "    'pct_renter_occupied': 'Renter'\n",
-    "})\n",
+    "df_stacked[\"tenure_type\"] = df_stacked[\"tenure_type\"].map(\n",
+    "    {\"pct_owner_occupied\": \"Owner\", \"pct_renter_occupied\": \"Renter\"}\n",
+    ")\n",
     "\n",
-    "data = df_stacked.to_dict('records')"
+    "data = df_stacked.to_dict(\"records\")"
    ]
   },
   {
@@ -101,7 +105,14 @@
    "outputs": [],
    "source": [
     "print(\"Highest Renter Neighbourhoods:\")\n",
-    "df[['neighbourhood_name', 'pct_renter_occupied', 'pct_owner_occupied', 'income_quintile']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"pct_renter_occupied\",\n",
+    "        \"pct_owner_occupied\",\n",
+    "        \"income_quintile\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -112,7 +123,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`.\n",
+    "Uses `create_stacked_bar` from `portfolio_app.figures.toronto.bar_charts`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `x_column`: 'neighbourhood_name'\n",
@@ -128,21 +139,22 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_stacked_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_stacked_bar\n",
     "\n",
     "# Show top 20 by renter percentage\n",
-    "top_20_names = df.head(20)['neighbourhood_name'].tolist()\n",
-    "data_filtered = [d for d in data if d['neighbourhood_name'] in top_20_names]\n",
+    "top_20_names = df.head(20)[\"neighbourhood_name\"].tolist()\n",
+    "data_filtered = [d for d in data if d[\"neighbourhood_name\"] in top_20_names]\n",
     "\n",
     "fig = create_stacked_bar(\n",
     "    data=data_filtered,\n",
-    "    x_column='neighbourhood_name',\n",
-    "    value_column='percentage',\n",
-    "    category_column='tenure_type',\n",
-    "    title='Housing Tenure Mix - Top 20 Renter Neighbourhoods',\n",
-    "    color_map={'Owner': '#4CAF50', 'Renter': '#2196F3'},\n",
+    "    x_column=\"neighbourhood_name\",\n",
+    "    value_column=\"percentage\",\n",
+    "    category_column=\"tenure_type\",\n",
+    "    title=\"Housing Tenure Mix - Top 20 Renter Neighbourhoods\",\n",
+    "    color_map={\"Owner\": \"#4CAF50\", \"Renter\": \"#2196F3\"},\n",
     "    show_percentages=True,\n",
     ")\n",
     "\n",
@@ -168,7 +180,9 @@
     "\n",
     "# By income quintile\n",
     "print(\"\\nTenure by Income Quintile:\")\n",
-    "df.groupby('income_quintile')[['pct_owner_occupied', 'pct_renter_occupied']].mean().round(1)"
+    "df.groupby(\"income_quintile\")[\n",
+    "    [\"pct_owner_occupied\", \"pct_renter_occupied\"]\n",
+    "].mean().round(1)"
    ]
   }
  ],

notebooks/toronto/overview/income_safety_scatter.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -44,8 +49,8 @@
     "    population,\n",
     "    livability_score,\n",
     "    crime_rate_per_100k\n",
-    "FROM mart_neighbourhood_overview\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_overview)\n",
+    "FROM public_marts.mart_neighbourhood_overview\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n",
     "  AND median_household_income IS NOT NULL\n",
     "  AND safety_score IS NOT NULL\n",
     "ORDER BY median_household_income DESC\n",
@@ -73,10 +78,10 @@
    "outputs": [],
    "source": [
     "# Scale income to thousands for better axis readability\n",
-    "df['income_thousands'] = df['median_household_income'] / 1000\n",
+    "df[\"income_thousands\"] = df[\"median_household_income\"] / 1000\n",
     "\n",
     "# Prepare data for figure factory\n",
-    "data = df.to_dict('records')"
+    "data = df.to_dict(\"records\")"
    ]
   },
   {
@@ -92,7 +97,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'median_household_income', 'safety_score', 'crime_rate_per_100k']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"median_household_income\",\n",
+    "        \"safety_score\",\n",
+    "        \"crime_rate_per_100k\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -103,7 +115,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_scatter_figure` from `portfolio_app.figures.scatter`.\n",
+    "Uses `create_scatter_figure` from `portfolio_app.figures.toronto.scatter`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `x_column`: 'income_thousands' (median household income in $K)\n",
@@ -120,19 +132,20 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.scatter import create_scatter_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.scatter import create_scatter_figure\n",
     "\n",
     "fig = create_scatter_figure(\n",
     "    data=data,\n",
-    "    x_column='income_thousands',\n",
-    "    y_column='safety_score',\n",
-    "    name_column='neighbourhood_name',\n",
-    "    size_column='population',\n",
-    "    title='Income vs Safety by Neighbourhood',\n",
-    "    x_title='Median Household Income ($K)',\n",
-    "    y_title='Safety Score (0-100)',\n",
+    "    x_column=\"income_thousands\",\n",
+    "    y_column=\"safety_score\",\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    size_column=\"population\",\n",
+    "    title=\"Income vs Safety by Neighbourhood\",\n",
+    "    x_title=\"Median Household Income ($K)\",\n",
+    "    y_title=\"Safety Score (0-100)\",\n",
     "    trendline=True,\n",
     ")\n",
     "\n",
@@ -162,7 +175,7 @@
    "outputs": [],
    "source": [
     "# Calculate correlation coefficient\n",
-    "correlation = df['median_household_income'].corr(df['safety_score'])\n",
+    "correlation = df[\"median_household_income\"].corr(df[\"safety_score\"])\n",
     "print(f\"Correlation coefficient (Income vs Safety): {correlation:.3f}\")"
    ]
   }

notebooks/toronto/overview/livability_choropleth.ipynb

@@ -30,12 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "# Connect to database\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -49,8 +53,8 @@
     "    amenity_score,\n",
     "    population,\n",
     "    median_household_income\n",
-    "FROM mart_neighbourhood_overview\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_overview)\n",
+    "FROM public_marts.mart_neighbourhood_overview\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n",
     "ORDER BY livability_score DESC\n",
     "\"\"\"\n",
     "\n",
@@ -76,21 +80,20 @@
    "outputs": [],
    "source": [
     "# Transform geometry to GeoJSON\n",
-    "import geopandas as gpd\n",
     "import json\n",
     "\n",
+    "import geopandas as gpd\n",
+    "\n",
     "# Convert WKB geometry to GeoDataFrame\n",
     "gdf = gpd.GeoDataFrame(\n",
-    "    df,\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    crs='EPSG:4326'\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
     ")\n",
     "\n",
     "# Create GeoJSON FeatureCollection\n",
     "geojson = json.loads(gdf.to_json())\n",
     "\n",
     "# Prepare data for figure factory\n",
-    "data = df.drop(columns=['geometry']).to_dict('records')"
+    "data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
    ]
   },
   {
@@ -106,7 +109,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'livability_score', 'safety_score', 'affordability_score', 'amenity_score']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"livability_score\",\n",
+    "        \"safety_score\",\n",
+    "        \"affordability_score\",\n",
+    "        \"amenity_score\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -117,7 +128,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `geojson`: GeoJSON FeatureCollection with neighbourhood boundaries\n",
@@ -134,18 +145,24 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
     "\n",
     "fig = create_choropleth_figure(\n",
     "    geojson=geojson,\n",
     "    data=data,\n",
-    "    location_key='neighbourhood_id',\n",
-    "    color_column='livability_score',\n",
-    "    hover_data=['neighbourhood_name', 'safety_score', 'affordability_score', 'amenity_score'],\n",
-    "    color_scale='RdYlGn',\n",
-    "    title='Toronto Neighbourhood Livability Score',\n",
+    "    location_key=\"neighbourhood_id\",\n",
+    "    color_column=\"livability_score\",\n",
+    "    hover_data=[\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"safety_score\",\n",
+    "        \"affordability_score\",\n",
+    "        \"amenity_score\",\n",
+    "    ],\n",
+    "    color_scale=\"RdYlGn\",\n",
+    "    title=\"Toronto Neighbourhood Livability Score\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/overview/top_bottom_10_bar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -43,8 +48,8 @@
     "    safety_score,\n",
     "    affordability_score,\n",
     "    amenity_score\n",
-    "FROM mart_neighbourhood_overview\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_overview)\n",
+    "FROM public_marts.mart_neighbourhood_overview\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n",
     "  AND livability_score IS NOT NULL\n",
     "ORDER BY livability_score DESC\n",
     "\"\"\"\n",
@@ -72,7 +77,7 @@
    "source": [
     "# The figure factory handles top/bottom selection internally\n",
     "# Just prepare as list of dicts\n",
-    "data = df.to_dict('records')"
+    "data = df.to_dict(\"records\")"
    ]
   },
   {
@@ -102,7 +107,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`.\n",
+    "Uses `create_ranking_bar` from `portfolio_app.figures.toronto.bar_charts`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `data`: List of dicts with all neighbourhoods\n",
@@ -119,20 +124,21 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_ranking_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_ranking_bar\n",
     "\n",
     "fig = create_ranking_bar(\n",
     "    data=data,\n",
-    "    name_column='neighbourhood_name',\n",
-    "    value_column='livability_score',\n",
-    "    title='Top & Bottom 10 Neighbourhoods by Livability',\n",
+    "    name_column=\"neighbourhood_name\",\n",
+    "    value_column=\"livability_score\",\n",
+    "    title=\"Top & Bottom 10 Neighbourhoods by Livability\",\n",
     "    top_n=10,\n",
     "    bottom_n=10,\n",
-    "    color_top='#4CAF50',   # Green for top performers\n",
-    "    color_bottom='#F44336', # Red for bottom performers\n",
-    "    value_format='.1f',\n",
+    "    color_top=\"#4CAF50\",  # Green for top performers\n",
+    "    color_bottom=\"#F44336\",  # Red for bottom performers\n",
+    "    value_format=\".1f\",\n",
     ")\n",
     "\n",
     "fig.show()"

notebooks/toronto/safety/crime_breakdown_bar.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -47,8 +52,8 @@
     "    homicide_count,\n",
     "    total_incidents,\n",
     "    crime_rate_per_100k\n",
-    "FROM mart_neighbourhood_safety\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_safety)\n",
+    "FROM public_marts.mart_neighbourhood_safety\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n",
     "ORDER BY total_incidents DESC\n",
     "LIMIT 15\n",
     "\"\"\"\n",
@@ -75,17 +80,25 @@
    "outputs": [],
    "source": [
     "df_melted = df.melt(\n",
-    "    id_vars=['neighbourhood_name', 'total_incidents'],\n",
-    "    value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n",
-    "                'robbery_count', 'theft_over_count', 'homicide_count'],\n",
-    "    var_name='crime_type',\n",
-    "    value_name='count'\n",
+    "    id_vars=[\"neighbourhood_name\", \"total_incidents\"],\n",
+    "    value_vars=[\n",
+    "        \"assault_count\",\n",
+    "        \"auto_theft_count\",\n",
+    "        \"break_enter_count\",\n",
+    "        \"robbery_count\",\n",
+    "        \"theft_over_count\",\n",
+    "        \"homicide_count\",\n",
+    "    ],\n",
+    "    var_name=\"crime_type\",\n",
+    "    value_name=\"count\",\n",
     ")\n",
     "\n",
     "# Clean labels\n",
-    "df_melted['crime_type'] = df_melted['crime_type'].str.replace('_count', '').str.replace('_', ' ').str.title()\n",
+    "df_melted[\"crime_type\"] = (\n",
+    "    df_melted[\"crime_type\"].str.replace(\"_count\", \"\").str.replace(\"_\", \" \").str.title()\n",
+    ")\n",
     "\n",
-    "data = df_melted.to_dict('records')"
+    "data = df_melted.to_dict(\"records\")"
    ]
   },
   {
@@ -101,7 +114,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'assault_count', 'auto_theft_count', 'break_enter_count', 'total_incidents']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"assault_count\",\n",
+    "        \"auto_theft_count\",\n",
+    "        \"break_enter_count\",\n",
+    "        \"total_incidents\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -112,7 +133,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`."
+    "Uses `create_stacked_bar` from `portfolio_app.figures.toronto.bar_charts`."
    ]
   },
   {
@@ -122,23 +143,24 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.bar_charts import create_stacked_bar\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.bar_charts import create_stacked_bar\n",
     "\n",
     "fig = create_stacked_bar(\n",
     "    data=data,\n",
-    "    x_column='neighbourhood_name',\n",
-    "    value_column='count',\n",
-    "    category_column='crime_type',\n",
-    "    title='Crime Type Breakdown - Top 15 Neighbourhoods',\n",
+    "    x_column=\"neighbourhood_name\",\n",
+    "    value_column=\"count\",\n",
+    "    category_column=\"crime_type\",\n",
+    "    title=\"Crime Type Breakdown - Top 15 Neighbourhoods\",\n",
     "    color_map={\n",
-    "        'Assault': '#d62728',\n",
-    "        'Auto Theft': '#ff7f0e',\n",
-    "        'Break Enter': '#9467bd',\n",
-    "        'Robbery': '#8c564b',\n",
-    "        'Theft Over': '#e377c2',\n",
-    "        'Homicide': '#1f77b4'\n",
+    "        \"Assault\": \"#d62728\",\n",
+    "        \"Auto Theft\": \"#ff7f0e\",\n",
+    "        \"Break Enter\": \"#9467bd\",\n",
+    "        \"Robbery\": \"#8c564b\",\n",
+    "        \"Theft Over\": \"#e377c2\",\n",
+    "        \"Homicide\": \"#1f77b4\",\n",
     "    },\n",
     ")\n",
     "\n",

notebooks/toronto/safety/crime_rate_choropleth.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -47,8 +52,8 @@
     "    safety_tier,\n",
     "    total_incidents,\n",
     "    population\n",
-    "FROM mart_neighbourhood_safety\n",
-    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_safety)\n",
+    "FROM public_marts.mart_neighbourhood_safety\n",
+    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n",
     "ORDER BY crime_rate_per_100k DESC\n",
     "\"\"\"\n",
     "\n",
@@ -73,17 +78,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import geopandas as gpd\n",
     "import json\n",
     "\n",
+    "import geopandas as gpd\n",
+    "\n",
     "gdf = gpd.GeoDataFrame(\n",
-    "    df,\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    crs='EPSG:4326'\n",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
     ")\n",
     "\n",
     "geojson = json.loads(gdf.to_json())\n",
-    "data = df.drop(columns=['geometry']).to_dict('records')"
+    "data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
    ]
   },
   {
@@ -99,7 +103,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['neighbourhood_name', 'crime_rate_per_100k', 'crime_index', 'safety_tier', 'total_incidents']].head(10)"
+    "df[\n",
+    "    [\n",
+    "        \"neighbourhood_name\",\n",
+    "        \"crime_rate_per_100k\",\n",
+    "        \"crime_index\",\n",
+    "        \"safety_tier\",\n",
+    "        \"total_incidents\",\n",
+    "    ]\n",
+    "].head(10)"
    ]
   },
   {
@@ -110,7 +122,7 @@
     "\n",
     "### Figure Factory\n",
     "\n",
-    "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
+    "Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`.\n",
     "\n",
     "**Key Parameters:**\n",
     "- `color_column`: 'crime_rate_per_100k'\n",
@@ -124,18 +136,19 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.choropleth import create_choropleth_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
     "\n",
     "fig = create_choropleth_figure(\n",
     "    geojson=geojson,\n",
     "    data=data,\n",
-    "    location_key='neighbourhood_id',\n",
-    "    color_column='crime_rate_per_100k',\n",
-    "    hover_data=['neighbourhood_name', 'crime_index', 'total_incidents'],\n",
-    "    color_scale='RdYlGn_r',\n",
-    "    title='Toronto Crime Rate per 100,000 Population',\n",
+    "    location_key=\"neighbourhood_id\",\n",
+    "    color_column=\"crime_rate_per_100k\",\n",
+    "    hover_data=[\"neighbourhood_name\", \"crime_index\", \"total_incidents\"],\n",
+    "    color_scale=\"RdYlGn_r\",\n",
+    "    title=\"Toronto Crime Rate per 100,000 Population\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/safety/crime_trend_line.ipynb

@@ -30,11 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
     "import os\n",
     "\n",
-    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
     "\n",
     "query = \"\"\"\n",
     "SELECT\n",
@@ -45,8 +50,8 @@
     "    AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n",
     "    SUM(total_incidents) as total_city_incidents,\n",
     "    AVG(crime_yoy_change_pct) as avg_yoy_change\n",
-    "FROM mart_neighbourhood_safety\n",
-    "WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_safety)\n",
+    "FROM public_marts.mart_neighbourhood_safety\n",
+    "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_safety)\n",
     "GROUP BY year\n",
     "ORDER BY year\n",
     "\"\"\"\n",
@@ -72,21 +77,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n",
+    "df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
     "\n",
     "# Melt for multi-line\n",
     "df_melted = df.melt(\n",
-    "    id_vars=['year', 'date'],\n",
-    "    value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n",
-    "    var_name='crime_type',\n",
-    "    value_name='rate_per_100k'\n",
+    "    id_vars=[\"year\", \"date\"],\n",
+    "    value_vars=[\"avg_assault_rate\", \"avg_auto_theft_rate\", \"avg_break_enter_rate\"],\n",
+    "    var_name=\"crime_type\",\n",
+    "    value_name=\"rate_per_100k\",\n",
     ")\n",
     "\n",
-    "df_melted['crime_type'] = df_melted['crime_type'].map({\n",
-    "    'avg_assault_rate': 'Assault',\n",
-    "    'avg_auto_theft_rate': 'Auto Theft',\n",
-    "    'avg_break_enter_rate': 'Break & Enter'\n",
-    "})"
+    "df_melted[\"crime_type\"] = df_melted[\"crime_type\"].map(\n",
+    "    {\n",
+    "        \"avg_assault_rate\": \"Assault\",\n",
+    "        \"avg_auto_theft_rate\": \"Auto Theft\",\n",
+    "        \"avg_break_enter_rate\": \"Break & Enter\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -102,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[['year', 'avg_crime_rate', 'total_city_incidents', 'avg_yoy_change']]"
+    "df[[\"year\", \"avg_crime_rate\", \"total_city_incidents\", \"avg_yoy_change\"]]"
    ]
   },
   {
@@ -123,22 +130,23 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.time_series import create_price_time_series\n",
+    "sys.path.insert(0, \"../..\")\n",
     "\n",
-    "data = df_melted.to_dict('records')\n",
+    "from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
+    "\n",
+    "data = df_melted.to_dict(\"records\")\n",
     "\n",
     "fig = create_price_time_series(\n",
     "    data=data,\n",
-    "    date_column='date',\n",
-    "    price_column='rate_per_100k',\n",
-    "    group_column='crime_type',\n",
-    "    title='Toronto Crime Trends by Type (5 Years)',\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"rate_per_100k\",\n",
+    "    group_column=\"crime_type\",\n",
+    "    title=\"Toronto Crime Trends by Type (5 Years)\",\n",
     ")\n",
     "\n",
     "# Remove dollar sign formatting since this is rate data\n",
-    "fig.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
+    "fig.update_layout(yaxis_tickprefix=\"\", yaxis_title=\"Rate per 100K\")\n",
     "\n",
     "fig.show()"
    ]
@@ -157,15 +165,19 @@
    "outputs": [],
    "source": [
     "# Total crime rate trend\n",
-    "total_data = df[['date', 'avg_crime_rate']].rename(columns={'avg_crime_rate': 'total_rate'}).to_dict('records')\n",
+    "total_data = (\n",
+    "    df[[\"date\", \"avg_crime_rate\"]]\n",
+    "    .rename(columns={\"avg_crime_rate\": \"total_rate\"})\n",
+    "    .to_dict(\"records\")\n",
+    ")\n",
     "\n",
     "fig2 = create_price_time_series(\n",
     "    data=total_data,\n",
-    "    date_column='date',\n",
-    "    price_column='total_rate',\n",
-    "    title='Toronto Overall Crime Rate Trend',\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"total_rate\",\n",
+    "    title=\"Toronto Overall Crime Rate Trend\",\n",
     ")\n",
-    "fig2.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
+    "fig2.update_layout(yaxis_tickprefix=\"\", yaxis_title=\"Rate per 100K\")\n",
     "fig2.show()"
    ]
   }

portfolio_app/callbacks/__init__.py

@@ -1,5 +1,5 @@
 """Application-level callbacks for the portfolio app."""
 
-from . import sidebar, theme
+from . import contact, sidebar, theme
 
-__all__ = ["sidebar", "theme"]
+__all__ = ["contact", "sidebar", "theme"]

portfolio_app/callbacks/contact.py

@@ -0,0 +1,214 @@
+"""Contact form submission callback with Formspree integration."""
+
+import re
+from typing import Any
+
+import dash_mantine_components as dmc
+import requests
+from dash import Input, Output, State, callback, no_update
+from dash_iconify import DashIconify
+
+FORMSPREE_ENDPOINT = "https://formspree.io/f/mqelqzpd"
+EMAIL_REGEX = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$")
+
+
+def _validate_form(
+    name: str | None, email: str | None, message: str | None
+) -> str | None:
+    """Validate form fields and return error message if invalid."""
+    if not name or not name.strip():
+        return "Please enter your name."
+    if not email or not email.strip():
+        return "Please enter your email address."
+    if not EMAIL_REGEX.match(email.strip()):
+        return "Please enter a valid email address."
+    if not message or not message.strip():
+        return "Please enter a message."
+    return None
+
+
+def _create_success_alert() -> dmc.Alert:
+    """Create success feedback alert."""
+    return dmc.Alert(
+        "Thank you for your message! I'll get back to you soon.",
+        title="Message Sent",
+        color="green",
+        variant="light",
+        icon=DashIconify(icon="tabler:check", width=20),
+        withCloseButton=True,
+    )
+
+
+def _create_error_alert(message: str) -> dmc.Alert:
+    """Create error feedback alert."""
+    return dmc.Alert(
+        message,
+        title="Error",
+        color="red",
+        variant="light",
+        icon=DashIconify(icon="tabler:alert-circle", width=20),
+        withCloseButton=True,
+    )
+
+
+@callback(  # type: ignore[misc]
+    Output("contact-feedback", "children"),
+    Output("contact-submit", "loading"),
+    Output("contact-name", "value"),
+    Output("contact-email", "value"),
+    Output("contact-subject", "value"),
+    Output("contact-message", "value"),
+    Output("contact-name", "error"),
+    Output("contact-email", "error"),
+    Output("contact-message", "error"),
+    Input("contact-submit", "n_clicks"),
+    State("contact-name", "value"),
+    State("contact-email", "value"),
+    State("contact-subject", "value"),
+    State("contact-message", "value"),
+    State("contact-gotcha", "value"),
+    prevent_initial_call=True,
+)
+def submit_contact_form(
+    n_clicks: int | None,
+    name: str | None,
+    email: str | None,
+    subject: str | None,
+    message: str | None,
+    gotcha: str | None,
+) -> tuple[Any, ...]:
+    """Submit contact form to Formspree.
+
+    Args:
+        n_clicks: Button click count.
+        name: User's name.
+        email: User's email address.
+        subject: Message subject (optional).
+        message: Message content.
+        gotcha: Honeypot field value (should be empty for real users).
+
+    Returns:
+        Tuple of (feedback, loading, name, email, subject, message,
+                  name_error, email_error, message_error).
+    """
+    if not n_clicks:
+        return (no_update,) * 9
+
+    # Check honeypot - if filled, silently "succeed" (it's a bot)
+    if gotcha:
+        return (
+            _create_success_alert(),
+            False,
+            "",
+            "",
+            None,
+            "",
+            None,
+            None,
+            None,
+        )
+
+    # Validate form
+    validation_error = _validate_form(name, email, message)
+    if validation_error:
+        # Determine which field has the error
+        name_error = "Required" if not name or not name.strip() else None
+        email_error = None
+        message_error = "Required" if not message or not message.strip() else None
+
+        if not email or not email.strip():
+            email_error = "Required"
+        elif not EMAIL_REGEX.match(email.strip()):
+            email_error = "Invalid email format"
+
+        return (
+            _create_error_alert(validation_error),
+            False,
+            no_update,
+            no_update,
+            no_update,
+            no_update,
+            name_error,
+            email_error,
+            message_error,
+        )
+
+    # Prepare form data (validation passed, so name/email/message are not None)
+    assert name is not None
+    assert email is not None
+    assert message is not None
+    form_data = {
+        "name": name.strip(),
+        "email": email.strip(),
+        "subject": subject or "General Inquiry",
+        "message": message.strip(),
+        "_gotcha": "",  # Formspree honeypot
+    }
+
+    # Submit to Formspree
+    try:
+        response = requests.post(
+            FORMSPREE_ENDPOINT,
+            json=form_data,
+            headers={
+                "Accept": "application/json",
+                "Content-Type": "application/json",
+            },
+            timeout=10,
+        )
+
+        if response.status_code == 200:
+            # Success - clear form
+            return (
+                _create_success_alert(),
+                False,
+                "",
+                "",
+                None,
+                "",
+                None,
+                None,
+                None,
+            )
+        else:
+            # Formspree returned an error
+            return (
+                _create_error_alert(
+                    "Failed to send message. Please try again or use direct contact."
+                ),
+                False,
+                no_update,
+                no_update,
+                no_update,
+                no_update,
+                None,
+                None,
+                None,
+            )
+
+    except requests.exceptions.Timeout:
+        return (
+            _create_error_alert("Request timed out. Please try again."),
+            False,
+            no_update,
+            no_update,
+            no_update,
+            no_update,
+            None,
+            None,
+            None,
+        )
+    except requests.exceptions.RequestException:
+        return (
+            _create_error_alert(
+                "Network error. Please check your connection and try again."
+            ),
+            False,
+            no_update,
+            no_update,
+            no_update,
+            no_update,
+            None,
+            None,
+            None,
+        )

portfolio_app/components/map_controls.py

@@ -28,7 +28,7 @@ def create_metric_selector(
         label=label,
         data=options,
         value=default_value or (options[0]["value"] if options else None),
-        style={"width": "200px"},
+        w=200,
     )
 
 
@@ -64,7 +64,7 @@ def create_map_controls(
                 id=f"{id_prefix}-layer-toggle",
                 label="Show Boundaries",
                 checked=True,
-                style={"marginTop": "10px"},
+                mt="sm",
             )
         )
 

portfolio_app/components/metric_card.py

@@ -5,7 +5,7 @@ from typing import Any
 import dash_mantine_components as dmc
 from dash import dcc
 
-from portfolio_app.figures.summary_cards import create_metric_card_figure
+from portfolio_app.figures.toronto.summary_cards import create_metric_card_figure
 
 
 class MetricCard:

portfolio_app/components/time_slider.py

@@ -38,7 +38,7 @@ def create_year_selector(
         label=label,
         data=options,
         value=str(default_year),
-        style={"width": "120px"},
+        w=120,
     )
 
 
@@ -83,7 +83,8 @@ def create_time_slider(
                 marks=marks,
                 step=1,
                 minRange=1,
-                style={"marginTop": "20px", "marginBottom": "10px"},
+                mt="md",
+                mb="sm",
             ),
         ],
         p="md",
@@ -131,5 +132,5 @@ def create_month_selector(
         label=label,
         data=options,
         value=str(default_month),
-        style={"width": "140px"},
+        w=140,
     )

portfolio_app/design/__init__.py

@@ -0,0 +1,48 @@
+"""Design system tokens and utilities."""
+
+from .tokens import (
+    CHART_PALETTE,
+    COLOR_ACCENT,
+    COLOR_NEGATIVE,
+    COLOR_POSITIVE,
+    COLOR_WARNING,
+    GRID_COLOR,
+    GRID_COLOR_DARK,
+    PALETTE_COMPARISON,
+    PALETTE_GENDER,
+    PALETTE_TREND,
+    PAPER_BG,
+    PLOT_BG,
+    POLICY_COLORS,
+    TEXT_MUTED,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+    get_colorbar_defaults,
+    get_default_layout,
+)
+
+__all__ = [
+    # Text colors
+    "TEXT_PRIMARY",
+    "TEXT_SECONDARY",
+    "TEXT_MUTED",
+    # Chart backgrounds
+    "GRID_COLOR",
+    "GRID_COLOR_DARK",
+    "PAPER_BG",
+    "PLOT_BG",
+    # Semantic colors
+    "COLOR_POSITIVE",
+    "COLOR_NEGATIVE",
+    "COLOR_WARNING",
+    "COLOR_ACCENT",
+    # Palettes
+    "CHART_PALETTE",
+    "PALETTE_COMPARISON",
+    "PALETTE_GENDER",
+    "PALETTE_TREND",
+    "POLICY_COLORS",
+    # Utility functions
+    "get_default_layout",
+    "get_colorbar_defaults",
+]

portfolio_app/design/tokens.py

@@ -0,0 +1,162 @@
+"""Centralized design tokens for consistent styling across the application.
+
+This module provides a single source of truth for colors, ensuring:
+- Consistent styling across all Plotly figures and components
+- Accessibility compliance (WCAG color contrast)
+- Easy theme updates without hunting through multiple files
+
+Usage:
+    from portfolio_app.design import TEXT_PRIMARY, CHART_PALETTE
+    fig.update_layout(font_color=TEXT_PRIMARY)
+"""
+
+from typing import Any
+
+# =============================================================================
+# TEXT COLORS (Dark Theme)
+# =============================================================================
+
+TEXT_PRIMARY = "#c9c9c9"
+"""Primary text color for labels, titles, and body text."""
+
+TEXT_SECONDARY = "#888888"
+"""Secondary text color for subtitles, captions, and muted text."""
+
+TEXT_MUTED = "#666666"
+"""Muted text color for disabled states and placeholders."""
+
+
+# =============================================================================
+# CHART BACKGROUND & GRID
+# =============================================================================
+
+GRID_COLOR = "rgba(128, 128, 128, 0.2)"
+"""Standard grid line color with transparency."""
+
+GRID_COLOR_DARK = "rgba(128, 128, 128, 0.3)"
+"""Darker grid for radar charts and polar plots."""
+
+PAPER_BG = "rgba(0, 0, 0, 0)"
+"""Transparent paper background for charts."""
+
+PLOT_BG = "rgba(0, 0, 0, 0)"
+"""Transparent plot background for charts."""
+
+
+# =============================================================================
+# SEMANTIC COLORS
+# =============================================================================
+
+COLOR_POSITIVE = "#40c057"
+"""Positive/success indicator (Mantine green-6)."""
+
+COLOR_NEGATIVE = "#fa5252"
+"""Negative/error indicator (Mantine red-6)."""
+
+COLOR_WARNING = "#fab005"
+"""Warning indicator (Mantine yellow-6)."""
+
+COLOR_ACCENT = "#228be6"
+"""Primary accent color (Mantine blue-6)."""
+
+
+# =============================================================================
+# ACCESSIBLE CHART PALETTE
+# =============================================================================
+
+# Okabe-Ito palette - optimized for all color vision deficiencies
+# Reference: https://jfly.uni-koeln.de/color/
+CHART_PALETTE = [
+    "#0072B2",  # Blue (primary data series)
+    "#E69F00",  # Orange
+    "#56B4E9",  # Sky blue
+    "#009E73",  # Teal/green
+    "#F0E442",  # Yellow
+    "#D55E00",  # Vermillion
+    "#CC79A7",  # Pink
+    "#000000",  # Black (use sparingly)
+]
+"""
+Accessible categorical palette (Okabe-Ito).
+
+Distinguishable for deuteranopia, protanopia, and tritanopia.
+Use indices 0-6 for most charts; index 7 (black) for emphasis only.
+"""
+
+# Semantic subsets for specific use cases
+PALETTE_COMPARISON = [CHART_PALETTE[0], CHART_PALETTE[1]]
+"""Two-color palette for A/B comparisons."""
+
+PALETTE_GENDER = {
+    "male": "#56B4E9",  # Sky blue
+    "female": "#CC79A7",  # Pink
+}
+"""Gender-specific colors (accessible contrast)."""
+
+PALETTE_TREND = {
+    "positive": COLOR_POSITIVE,
+    "negative": COLOR_NEGATIVE,
+    "neutral": TEXT_SECONDARY,
+}
+"""Trend indicator colors for sparklines and deltas."""
+
+
+# =============================================================================
+# POLICY/EVENT MARKERS (Time Series)
+# =============================================================================
+
+POLICY_COLORS = {
+    "policy_change": "#E69F00",  # Orange - policy changes
+    "major_event": "#D55E00",  # Vermillion - major events
+    "data_note": "#56B4E9",  # Sky blue - data annotations
+    "forecast": "#009E73",  # Teal - forecast periods
+    "highlight": "#F0E442",  # Yellow - highlighted regions
+}
+"""Colors for policy markers and event annotations on time series."""
+
+
+# =============================================================================
+# CHART LAYOUT DEFAULTS
+# =============================================================================
+
+
+def get_default_layout() -> dict[str, Any]:
+    """Return default Plotly layout settings with design tokens.
+
+    Returns:
+        dict: Layout configuration for fig.update_layout()
+
+    Example:
+        fig.update_layout(**get_default_layout())
+    """
+    return {
+        "paper_bgcolor": PAPER_BG,
+        "plot_bgcolor": PLOT_BG,
+        "font": {"color": TEXT_PRIMARY},
+        "title": {"font": {"color": TEXT_PRIMARY}},
+        "legend": {"font": {"color": TEXT_PRIMARY}},
+        "xaxis": {
+            "gridcolor": GRID_COLOR,
+            "linecolor": GRID_COLOR,
+            "tickfont": {"color": TEXT_PRIMARY},
+            "title": {"font": {"color": TEXT_PRIMARY}},
+        },
+        "yaxis": {
+            "gridcolor": GRID_COLOR,
+            "linecolor": GRID_COLOR,
+            "tickfont": {"color": TEXT_PRIMARY},
+            "title": {"font": {"color": TEXT_PRIMARY}},
+        },
+    }
+
+
+def get_colorbar_defaults() -> dict[str, Any]:
+    """Return default colorbar settings with design tokens.
+
+    Returns:
+        dict: Colorbar configuration for choropleth/heatmap traces
+    """
+    return {
+        "tickfont": {"color": TEXT_PRIMARY},
+        "title": {"font": {"color": TEXT_PRIMARY}},
+    }

portfolio_app/figures/__init__.py

@@ -1,61 +1,15 @@
-"""Plotly figure factories for data visualization."""
+"""Plotly figure factories for data visualization.
 
-from .bar_charts import (
-    create_horizontal_bar,
-    create_ranking_bar,
-    create_stacked_bar,
-)
-from .choropleth import (
-    create_choropleth_figure,
-    create_zone_map,
-)
-from .demographics import (
-    create_age_pyramid,
-    create_donut_chart,
-    create_income_distribution,
-)
-from .radar import (
-    create_comparison_radar,
-    create_radar_figure,
-)
-from .scatter import (
-    create_bubble_chart,
-    create_scatter_figure,
-)
-from .summary_cards import create_metric_card_figure, create_summary_metrics
-from .time_series import (
-    add_policy_markers,
-    create_market_comparison_chart,
-    create_price_time_series,
-    create_time_series_with_events,
-    create_volume_time_series,
-)
+Figure factories are organized by dashboard domain:
+- toronto/  : Toronto Neighbourhood Dashboard figures
+
+Usage:
+    from portfolio_app.figures.toronto import create_choropleth_figure
+    from portfolio_app.figures.toronto import create_ranking_bar
+"""
+
+from . import toronto
 
 __all__ = [
-    # Choropleth
-    "create_choropleth_figure",
-    "create_zone_map",
-    # Time series
-    "create_price_time_series",
-    "create_volume_time_series",
-    "create_market_comparison_chart",
-    "create_time_series_with_events",
-    "add_policy_markers",
-    # Summary
-    "create_metric_card_figure",
-    "create_summary_metrics",
-    # Bar charts
-    "create_ranking_bar",
-    "create_stacked_bar",
-    "create_horizontal_bar",
-    # Scatter plots
-    "create_scatter_figure",
-    "create_bubble_chart",
-    # Radar charts
-    "create_radar_figure",
-    "create_comparison_radar",
-    # Demographics
-    "create_age_pyramid",
-    "create_donut_chart",
-    "create_income_distribution",
+    "toronto",
 ]

portfolio_app/figures/toronto/__init__.py

@@ -0,0 +1,61 @@
+"""Plotly figure factories for Toronto dashboard visualizations."""
+
+from .bar_charts import (
+    create_horizontal_bar,
+    create_ranking_bar,
+    create_stacked_bar,
+)
+from .choropleth import (
+    create_choropleth_figure,
+    create_zone_map,
+)
+from .demographics import (
+    create_age_pyramid,
+    create_donut_chart,
+    create_income_distribution,
+)
+from .radar import (
+    create_comparison_radar,
+    create_radar_figure,
+)
+from .scatter import (
+    create_bubble_chart,
+    create_scatter_figure,
+)
+from .summary_cards import create_metric_card_figure, create_summary_metrics
+from .time_series import (
+    add_policy_markers,
+    create_market_comparison_chart,
+    create_price_time_series,
+    create_time_series_with_events,
+    create_volume_time_series,
+)
+
+__all__ = [
+    # Choropleth
+    "create_choropleth_figure",
+    "create_zone_map",
+    # Time series
+    "create_price_time_series",
+    "create_volume_time_series",
+    "create_market_comparison_chart",
+    "create_time_series_with_events",
+    "add_policy_markers",
+    # Summary
+    "create_metric_card_figure",
+    "create_summary_metrics",
+    # Bar charts
+    "create_ranking_bar",
+    "create_stacked_bar",
+    "create_horizontal_bar",
+    # Scatter plots
+    "create_scatter_figure",
+    "create_bubble_chart",
+    # Radar charts
+    "create_radar_figure",
+    "create_comparison_radar",
+    # Demographics
+    "create_age_pyramid",
+    "create_donut_chart",
+    "create_income_distribution",
+]

portfolio_app/figures/toronto/bar_charts.py

@@ -6,6 +6,17 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    CHART_PALETTE,
+    COLOR_NEGATIVE,
+    COLOR_POSITIVE,
+    GRID_COLOR,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_ranking_bar(
     data: list[dict[str, Any]],
@@ -14,8 +25,8 @@ def create_ranking_bar(
     title: str | None = None,
     top_n: int = 10,
     bottom_n: int = 10,
-    color_top: str = "#4CAF50",
-    color_bottom: str = "#F44336",
+    color_top: str = COLOR_POSITIVE,
+    color_bottom: str = COLOR_NEGATIVE,
     value_format: str = ",.0f",
 ) -> go.Figure:
     """Create horizontal bar chart showing top and bottom rankings.
@@ -87,10 +98,10 @@ def create_ranking_bar(
         barmode="group",
         showlegend=True,
         legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "title": None},
         yaxis={"autorange": "reversed", "title": None},
         margin={"l": 10, "r": 10, "t": 40, "b": 10},
     )
@@ -126,10 +137,10 @@ def create_stacked_bar(
 
     df = pd.DataFrame(data)
 
-    # Default color scheme
+    # Default color scheme using accessible palette
     if color_map is None:
         categories = df[category_column].unique()
-        colors = px.colors.qualitative.Set2[: len(categories)]
+        colors = CHART_PALETTE[: len(categories)]
         color_map = dict(zip(categories, colors, strict=False))
 
     fig = px.bar(
@@ -147,11 +158,11 @@ def create_stacked_bar(
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "title": None},
+        yaxis={"gridcolor": GRID_COLOR, "title": None},
         legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
         margin={"l": 10, "r": 10, "t": 60, "b": 10},
     )
@@ -164,7 +175,7 @@ def create_horizontal_bar(
     name_column: str,
     value_column: str,
     title: str | None = None,
-    color: str = "#2196F3",
+    color: str = CHART_PALETTE[0],
     value_format: str = ",.0f",
     sort: bool = True,
 ) -> go.Figure:
@@ -204,10 +215,10 @@ def create_horizontal_bar(
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "title": None},
         yaxis={"title": None},
         margin={"l": 10, "r": 10, "t": 40, "b": 10},
     )
@@ -225,13 +236,13 @@ def _create_empty_figure(title: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )

portfolio_app/figures/toronto/choropleth.py

@@ -5,6 +5,13 @@ from typing import Any
 import plotly.express as px
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_choropleth_figure(
     geojson: dict[str, Any] | None,
@@ -55,9 +62,9 @@ def create_choropleth_figure(
             margin={"l": 0, "r": 0, "t": 40, "b": 0},
             title=title or "Toronto Housing Map",
             height=500,
-            paper_bgcolor="rgba(0,0,0,0)",
-            plot_bgcolor="rgba(0,0,0,0)",
-            font_color="#c9c9c9",
+            paper_bgcolor=PAPER_BG,
+            plot_bgcolor=PLOT_BG,
+            font_color=TEXT_PRIMARY,
         )
         fig.add_annotation(
             text="No geometry data available. Complete QGIS digitization to enable map.",
@@ -66,7 +73,7 @@ def create_choropleth_figure(
             x=0.5,
             y=0.5,
             showarrow=False,
-            font={"size": 14, "color": "#888888"},
+            font={"size": 14, "color": TEXT_SECONDARY},
         )
         return fig
 
@@ -98,17 +105,17 @@ def create_choropleth_figure(
         margin={"l": 0, "r": 0, "t": 40, "b": 0},
         title=title,
         height=500,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         coloraxis_colorbar={
             "title": {
                 "text": color_column.replace("_", " ").title(),
-                "font": {"color": "#c9c9c9"},
+                "font": {"color": TEXT_PRIMARY},
             },
             "thickness": 15,
             "len": 0.7,
-            "tickfont": {"color": "#c9c9c9"},
+            "tickfont": {"color": TEXT_PRIMARY},
         },
     )
 

portfolio_app/figures/toronto/demographics.py

@@ -5,6 +5,16 @@ from typing import Any
 import pandas as pd
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    CHART_PALETTE,
+    GRID_COLOR,
+    PALETTE_GENDER,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_age_pyramid(
     data: list[dict[str, Any]],
@@ -52,7 +62,7 @@ def create_age_pyramid(
             x=male_values_neg,
             orientation="h",
             name="Male",
-            marker_color="#2196F3",
+            marker_color=PALETTE_GENDER["male"],
             hovertemplate="%{y}<br>Male: %{customdata:,}<extra></extra>",
             customdata=male_values,
         )
@@ -65,7 +75,7 @@ def create_age_pyramid(
             x=female_values,
             orientation="h",
             name="Female",
-            marker_color="#E91E63",
+            marker_color=PALETTE_GENDER["female"],
             hovertemplate="%{y}<br>Female: %{x:,}<extra></extra>",
         )
     )
@@ -77,12 +87,12 @@ def create_age_pyramid(
         title=title,
         barmode="overlay",
         bargap=0.1,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={
             "title": "Population",
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "range": [-max_val * 1.1, max_val * 1.1],
             "tickvals": [-max_val, -max_val / 2, 0, max_val / 2, max_val],
             "ticktext": [
@@ -93,7 +103,7 @@ def create_age_pyramid(
                 f"{max_val:,.0f}",
             ],
         },
-        yaxis={"title": None, "gridcolor": "rgba(128,128,128,0.2)"},
+        yaxis={"title": None, "gridcolor": GRID_COLOR},
         legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
         margin={"l": 10, "r": 10, "t": 60, "b": 10},
     )
@@ -127,17 +137,9 @@ def create_donut_chart(
 
     df = pd.DataFrame(data)
 
+    # Use accessible palette by default
     if colors is None:
-        colors = [
-            "#2196F3",
-            "#4CAF50",
-            "#FF9800",
-            "#E91E63",
-            "#9C27B0",
-            "#00BCD4",
-            "#FFC107",
-            "#795548",
-        ]
+        colors = CHART_PALETTE
 
     fig = go.Figure(
         go.Pie(
@@ -153,8 +155,8 @@ def create_donut_chart(
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        font_color=TEXT_PRIMARY,
         showlegend=False,
         margin={"l": 10, "r": 10, "t": 60, "b": 10},
     )
@@ -167,7 +169,7 @@ def create_income_distribution(
     bracket_column: str,
     count_column: str,
     title: str | None = None,
-    color: str = "#4CAF50",
+    color: str = CHART_PALETTE[3],  # Teal
 ) -> go.Figure:
     """Create histogram-style bar chart for income distribution.
 
@@ -199,17 +201,17 @@ def create_income_distribution(
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={
             "title": "Income Bracket",
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "tickangle": -45,
         },
         yaxis={
             "title": "Households",
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
         },
         margin={"l": 10, "r": 10, "t": 60, "b": 80},
     )
@@ -227,13 +229,13 @@ def _create_empty_figure(title: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )

portfolio_app/figures/toronto/radar.py

@@ -4,6 +4,14 @@ from typing import Any
 
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    CHART_PALETTE,
+    GRID_COLOR_DARK,
+    PAPER_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_radar_figure(
     data: list[dict[str, Any]],
@@ -32,16 +40,9 @@ def create_radar_figure(
     if not data or not metrics:
         return _create_empty_figure(title or "Radar Chart")
 
-    # Default colors
+    # Use accessible palette by default
     if colors is None:
-        colors = [
-            "#2196F3",
-            "#4CAF50",
-            "#FF9800",
-            "#E91E63",
-            "#9C27B0",
-            "#00BCD4",
-        ]
+        colors = CHART_PALETTE
 
     fig = go.Figure()
 
@@ -78,19 +79,19 @@ def create_radar_figure(
         polar={
             "radialaxis": {
                 "visible": True,
-                "gridcolor": "rgba(128,128,128,0.3)",
-                "linecolor": "rgba(128,128,128,0.3)",
-                "tickfont": {"color": "#c9c9c9"},
+                "gridcolor": GRID_COLOR_DARK,
+                "linecolor": GRID_COLOR_DARK,
+                "tickfont": {"color": TEXT_PRIMARY},
             },
             "angularaxis": {
-                "gridcolor": "rgba(128,128,128,0.3)",
-                "linecolor": "rgba(128,128,128,0.3)",
-                "tickfont": {"color": "#c9c9c9"},
+                "gridcolor": GRID_COLOR_DARK,
+                "linecolor": GRID_COLOR_DARK,
+                "tickfont": {"color": TEXT_PRIMARY},
             },
-            "bgcolor": "rgba(0,0,0,0)",
+            "bgcolor": PAPER_BG,
         },
-        paper_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        font_color=TEXT_PRIMARY,
         showlegend=len(data) > 1,
         legend={"orientation": "h", "yanchor": "bottom", "y": -0.2},
         margin={"l": 40, "r": 40, "t": 60, "b": 40},
@@ -133,7 +134,7 @@ def create_comparison_radar(
         metrics=metrics,
         name_column="__name__",
         title=title,
-        colors=["#4CAF50", "#9E9E9E"],
+        colors=[CHART_PALETTE[3], TEXT_SECONDARY],  # Teal for selected, gray for avg
     )
 
 
@@ -156,11 +157,11 @@ def _create_empty_figure(title: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        font_color=TEXT_PRIMARY,
     )
     return fig

portfolio_app/figures/toronto/scatter.py

@@ -6,6 +6,15 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    CHART_PALETTE,
+    GRID_COLOR,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_scatter_figure(
     data: list[dict[str, Any]],
@@ -72,21 +81,21 @@ def create_scatter_figure(
     if trendline:
         fig.update_traces(
             selector={"mode": "lines"},
-            line={"color": "#FF9800", "dash": "dash", "width": 2},
+            line={"color": CHART_PALETTE[1], "dash": "dash", "width": 2},
         )
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "title": x_title or x_column.replace("_", " ").title(),
             "zeroline": False,
         },
         yaxis={
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "title": y_title or y_column.replace("_", " ").title(),
             "zeroline": False,
         },
@@ -140,19 +149,20 @@ def create_bubble_chart(
         hover_name=name_column,
         size_max=size_max,
         opacity=0.7,
+        color_discrete_sequence=CHART_PALETTE,
     )
 
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "title": x_title or x_column.replace("_", " ").title(),
         },
         yaxis={
-            "gridcolor": "rgba(128,128,128,0.2)",
+            "gridcolor": GRID_COLOR,
             "title": y_title or y_column.replace("_", " ").title(),
         },
         margin={"l": 10, "r": 10, "t": 40, "b": 10},
@@ -171,13 +181,13 @@ def _create_empty_figure(title: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     fig.update_layout(
         title=title,
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )

portfolio_app/figures/toronto/summary_cards.py

@@ -4,6 +4,14 @@ from typing import Any
 
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    COLOR_NEGATIVE,
+    COLOR_POSITIVE,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+)
+
 
 def create_metric_card_figure(
     value: float | int | str,
@@ -59,8 +67,12 @@ def create_metric_card_figure(
             "relative": False,
             "valueformat": ".1f",
             "suffix": delta_suffix,
-            "increasing": {"color": "green" if positive_is_good else "red"},
-            "decreasing": {"color": "red" if positive_is_good else "green"},
+            "increasing": {
+                "color": COLOR_POSITIVE if positive_is_good else COLOR_NEGATIVE
+            },
+            "decreasing": {
+                "color": COLOR_NEGATIVE if positive_is_good else COLOR_POSITIVE
+            },
         }
 
     fig.add_trace(go.Indicator(**indicator_config))
@@ -68,9 +80,9 @@ def create_metric_card_figure(
     fig.update_layout(
         height=120,
         margin={"l": 20, "r": 20, "t": 40, "b": 20},
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font={"family": "Inter, sans-serif", "color": "#c9c9c9"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font={"family": "Inter, sans-serif", "color": TEXT_PRIMARY},
     )
 
     return fig

portfolio_app/figures/toronto/time_series.py

@@ -5,6 +5,15 @@ from typing import Any
 import plotly.express as px
 import plotly.graph_objects as go
 
+from portfolio_app.design import (
+    CHART_PALETTE,
+    GRID_COLOR,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+
 
 def create_price_time_series(
     data: list[dict[str, Any]],
@@ -38,14 +47,14 @@ def create_price_time_series(
             x=0.5,
             y=0.5,
             showarrow=False,
-            font={"color": "#888888"},
+            font={"color": TEXT_SECONDARY},
         )
         fig.update_layout(
             title=title,
             height=350,
-            paper_bgcolor="rgba(0,0,0,0)",
-            plot_bgcolor="rgba(0,0,0,0)",
-            font_color="#c9c9c9",
+            paper_bgcolor=PAPER_BG,
+            plot_bgcolor=PLOT_BG,
+            font_color=TEXT_PRIMARY,
         )
         return fig
 
@@ -59,6 +68,7 @@ def create_price_time_series(
             y=price_column,
             color=group_column,
             title=title,
+            color_discrete_sequence=CHART_PALETTE,
         )
     else:
         fig = px.line(
@@ -67,6 +77,7 @@ def create_price_time_series(
             y=price_column,
             title=title,
         )
+        fig.update_traces(line_color=CHART_PALETTE[0])
 
     fig.update_layout(
         height=350,
@@ -76,11 +87,11 @@ def create_price_time_series(
         yaxis_tickprefix="$",
         yaxis_tickformat=",",
         hovermode="x unified",
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
+        yaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
     )
 
     return fig
@@ -118,14 +129,14 @@ def create_volume_time_series(
             x=0.5,
             y=0.5,
             showarrow=False,
-            font={"color": "#888888"},
+            font={"color": TEXT_SECONDARY},
         )
         fig.update_layout(
             title=title,
             height=350,
-            paper_bgcolor="rgba(0,0,0,0)",
-            plot_bgcolor="rgba(0,0,0,0)",
-            font_color="#c9c9c9",
+            paper_bgcolor=PAPER_BG,
+            plot_bgcolor=PLOT_BG,
+            font_color=TEXT_PRIMARY,
         )
         return fig
 
@@ -140,6 +151,7 @@ def create_volume_time_series(
                 y=volume_column,
                 color=group_column,
                 title=title,
+                color_discrete_sequence=CHART_PALETTE,
             )
         else:
             fig = px.bar(
@@ -148,6 +160,7 @@ def create_volume_time_series(
                 y=volume_column,
                 title=title,
             )
+            fig.update_traces(marker_color=CHART_PALETTE[0])
     else:
         if group_column and group_column in df.columns:
             fig = px.line(
@@ -156,6 +169,7 @@ def create_volume_time_series(
                 y=volume_column,
                 color=group_column,
                 title=title,
+                color_discrete_sequence=CHART_PALETTE,
             )
         else:
             fig = px.line(
@@ -164,6 +178,7 @@ def create_volume_time_series(
                 y=volume_column,
                 title=title,
             )
+            fig.update_traces(line_color=CHART_PALETTE[0])
 
     fig.update_layout(
         height=350,
@@ -172,11 +187,11 @@ def create_volume_time_series(
         yaxis_title=volume_column.replace("_", " ").title(),
         yaxis_tickformat=",",
         hovermode="x unified",
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
+        yaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
     )
 
     return fig
@@ -211,14 +226,14 @@ def create_market_comparison_chart(
             x=0.5,
             y=0.5,
             showarrow=False,
-            font={"color": "#888888"},
+            font={"color": TEXT_SECONDARY},
         )
         fig.update_layout(
             title=title,
             height=400,
-            paper_bgcolor="rgba(0,0,0,0)",
-            plot_bgcolor="rgba(0,0,0,0)",
-            font_color="#c9c9c9",
+            paper_bgcolor=PAPER_BG,
+            plot_bgcolor=PLOT_BG,
+            font_color=TEXT_PRIMARY,
         )
         return fig
 
@@ -230,8 +245,6 @@ def create_market_comparison_chart(
 
     fig = make_subplots(specs=[[{"secondary_y": True}]])
 
-    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
-
     for i, metric in enumerate(metrics[:4]):
         if metric not in df.columns:
             continue
@@ -242,7 +255,7 @@ def create_market_comparison_chart(
                 x=df[date_column],
                 y=df[metric],
                 name=metric.replace("_", " ").title(),
-                line={"color": colors[i % len(colors)]},
+                line={"color": CHART_PALETTE[i % len(CHART_PALETTE)]},
             ),
             secondary_y=secondary,
         )
@@ -252,18 +265,18 @@ def create_market_comparison_chart(
         height=400,
         margin={"l": 40, "r": 40, "t": 50, "b": 40},
         hovermode="x unified",
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
+        yaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
         legend={
             "orientation": "h",
             "yanchor": "bottom",
             "y": 1.02,
             "xanchor": "right",
             "x": 1,
-            "font": {"color": "#c9c9c9"},
+            "font": {"color": TEXT_PRIMARY},
         },
     )
 
@@ -290,13 +303,13 @@ def add_policy_markers(
     if not policy_events:
         return fig
 
-    # Color mapping for policy categories
+    # Color mapping for policy categories using design tokens
     category_colors = {
-        "monetary": "#1f77b4",  # Blue
-        "tax": "#2ca02c",  # Green
-        "regulatory": "#ff7f0e",  # Orange
-        "supply": "#9467bd",  # Purple
-        "economic": "#d62728",  # Red
+        "monetary": CHART_PALETTE[0],  # Blue
+        "tax": CHART_PALETTE[3],  # Teal/green
+        "regulatory": CHART_PALETTE[1],  # Orange
+        "supply": CHART_PALETTE[6],  # Pink
+        "economic": CHART_PALETTE[5],  # Vermillion
     }
 
     # Symbol mapping for expected direction
@@ -313,7 +326,7 @@ def add_policy_markers(
         title = event.get("title", "Policy Event")
         level = event.get("level", "federal")
 
-        color = category_colors.get(category, "#666666")
+        color = category_colors.get(category, TEXT_SECONDARY)
         symbol = direction_symbols.get(direction, "circle")
 
         # Add vertical line for the event
@@ -335,7 +348,7 @@ def add_policy_markers(
                     "symbol": symbol,
                     "size": 12,
                     "color": color,
-                    "line": {"width": 1, "color": "white"},
+                    "line": {"width": 1, "color": TEXT_PRIMARY},
                 },
                 name=title,
                 hovertemplate=(

portfolio_app/pages/contact.py

@@ -2,6 +2,7 @@
 
 import dash
 import dash_mantine_components as dmc
+from dash import html
 from dash_iconify import DashIconify
 
 dash.register_page(__name__, path="/contact", name="Contact")
@@ -51,51 +52,57 @@ def create_intro_section() -> dmc.Stack:
 
 
 def create_contact_form() -> dmc.Paper:
-    """Create the contact form (disabled in Phase 1)."""
+    """Create the contact form with Formspree integration."""
     return dmc.Paper(
         dmc.Stack(
             [
                 dmc.Title("Send a Message", order=2, size="h4"),
-                dmc.Alert(
-                    "Contact form submission is coming soon. Please use the direct contact "
-                    "methods below for now.",
-                    title="Form Coming Soon",
-                    color="blue",
-                    variant="light",
-                ),
+                # Feedback container for success/error messages
+                html.Div(id="contact-feedback"),
                 dmc.TextInput(
+                    id="contact-name",
                     label="Name",
                     placeholder="Your name",
                     leftSection=DashIconify(icon="tabler:user", width=18),
-                    disabled=True,
+                    required=True,
                 ),
                 dmc.TextInput(
+                    id="contact-email",
                     label="Email",
                     placeholder="your.email@example.com",
                     leftSection=DashIconify(icon="tabler:mail", width=18),
-                    disabled=True,
+                    required=True,
                 ),
                 dmc.Select(
+                    id="contact-subject",
                     label="Subject",
                     placeholder="Select a subject",
                     data=SUBJECT_OPTIONS,
                     leftSection=DashIconify(icon="tabler:tag", width=18),
-                    disabled=True,
                 ),
                 dmc.Textarea(
+                    id="contact-message",
                     label="Message",
                     placeholder="Your message...",
                     minRows=4,
-                    disabled=True,
+                    required=True,
+                ),
+                # Honeypot field for spam protection (hidden from users)
+                dmc.TextInput(
+                    id="contact-gotcha",
+                    style={"position": "absolute", "left": "-9999px"},
+                    tabIndex=-1,
+                    autoComplete="off",
                 ),
                 dmc.Button(
                     "Send Message",
+                    id="contact-submit",
                     fullWidth=True,
                     leftSection=DashIconify(icon="tabler:send", width=18),
-                    disabled=True,
                 ),
             ],
             gap="md",
+            style={"position": "relative"},
         ),
         p="xl",
         radius="md",

portfolio_app/pages/toronto/callbacks/chart_callbacks.py

@@ -1,10 +1,19 @@
 """Chart callbacks for supporting visualizations."""
 # mypy: disable-error-code="misc,no-untyped-def,arg-type"
 
+import pandas as pd
 import plotly.graph_objects as go
 from dash import Input, Output, callback
 
-from portfolio_app.figures import (
+from portfolio_app.design import (
+    CHART_PALETTE,
+    GRID_COLOR,
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+from portfolio_app.figures.toronto import (
     create_donut_chart,
     create_horizontal_bar,
     create_radar_figure,
@@ -43,7 +52,24 @@ def update_overview_scatter(year: str) -> go.Figure:
     # Compute safety score (inverse of crime rate)
     if "total_crime_rate" in merged.columns:
         max_crime = merged["total_crime_rate"].max()
-        merged["safety_score"] = 100 - (merged["total_crime_rate"] / max_crime * 100)
+        if max_crime and max_crime > 0:
+            merged["safety_score"] = 100 - (
+                merged["total_crime_rate"] / max_crime * 100
+            )
+        else:
+            merged["safety_score"] = 50  # Default if no crime data
+
+    # Fill NULL population with median or default value for sizing
+    if "population" in merged.columns:
+        median_pop = merged["population"].median()
+        default_pop = median_pop if pd.notna(median_pop) else 10000
+        merged["population"] = merged["population"].fillna(default_pop)
+
+    # Filter rows with required data for scatter plot
+    merged = merged.dropna(subset=["median_household_income", "safety_score"])
+
+    if merged.empty:
+        return _empty_chart("Insufficient data for scatter plot")
 
     data = merged.to_dict("records")
 
@@ -76,12 +102,13 @@ def update_housing_trend(year: str, neighbourhood_id: int | None) -> go.Figure:
         return _empty_chart("No trend data available")
 
     # Placeholder for trend data - would be historical
+    base_rent = averages.get("avg_rent_2bed") or 2000
     data = [
-        {"year": "2019", "avg_rent": averages.get("avg_rent_2bed", 2000) * 0.85},
-        {"year": "2020", "avg_rent": averages.get("avg_rent_2bed", 2000) * 0.88},
-        {"year": "2021", "avg_rent": averages.get("avg_rent_2bed", 2000) * 0.92},
-        {"year": "2022", "avg_rent": averages.get("avg_rent_2bed", 2000) * 0.96},
-        {"year": "2023", "avg_rent": averages.get("avg_rent_2bed", 2000)},
+        {"year": "2019", "avg_rent": base_rent * 0.85},
+        {"year": "2020", "avg_rent": base_rent * 0.88},
+        {"year": "2021", "avg_rent": base_rent * 0.92},
+        {"year": "2022", "avg_rent": base_rent * 0.96},
+        {"year": "2023", "avg_rent": base_rent},
     ]
 
     fig = go.Figure()
@@ -90,18 +117,18 @@ def update_housing_trend(year: str, neighbourhood_id: int | None) -> go.Figure:
             x=[d["year"] for d in data],
             y=[d["avg_rent"] for d in data],
             mode="lines+markers",
-            line={"color": "#2196F3", "width": 2},
+            line={"color": CHART_PALETTE[0], "width": 2},
             marker={"size": 8},
             name="City Average",
         )
     )
 
     fig.update_layout(
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)"},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Avg Rent (2BR)"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR},
+        yaxis={"gridcolor": GRID_COLOR, "title": "Avg Rent (2BR)"},
         showlegend=False,
         margin={"l": 40, "r": 10, "t": 10, "b": 30},
     )
@@ -134,7 +161,7 @@ def update_housing_types(year: str) -> go.Figure:
         data=data,
         name_column="type",
         value_column="percentage",
-        colors=["#4CAF50", "#2196F3"],
+        colors=[CHART_PALETTE[3], CHART_PALETTE[0]],  # Teal for owner, blue for renter
     )
 
 
@@ -159,19 +186,19 @@ def update_safety_trend(year: str) -> go.Figure:
             x=[d["year"] for d in data],
             y=[d["crime_rate"] for d in data],
             mode="lines+markers",
-            line={"color": "#FF5722", "width": 2},
+            line={"color": CHART_PALETTE[5], "width": 2},  # Vermillion
             marker={"size": 8},
             fill="tozeroy",
-            fillcolor="rgba(255,87,34,0.1)",
+            fillcolor="rgba(213, 94, 0, 0.1)",  # Vermillion with opacity
         )
     )
 
     fig.update_layout(
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)"},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Crime Rate per 100K"},
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
+        xaxis={"gridcolor": GRID_COLOR},
+        yaxis={"gridcolor": GRID_COLOR, "title": "Crime Rate per 100K"},
         showlegend=False,
         margin={"l": 40, "r": 10, "t": 10, "b": 30},
     )
@@ -214,7 +241,7 @@ def update_safety_types(year: str) -> go.Figure:
         data=data,
         name_column="category",
         value_column="count",
-        color="#FF5722",
+        color=CHART_PALETTE[5],  # Vermillion for crime
     )
 
 
@@ -245,7 +272,11 @@ def update_demographics_age(year: str) -> go.Figure:
         data=data,
         name_column="age_group",
         value_column="percentage",
-        colors=["#9C27B0", "#673AB7", "#3F51B5"],
+        colors=[
+            CHART_PALETTE[2],
+            CHART_PALETTE[0],
+            CHART_PALETTE[4],
+        ],  # Sky, Blue, Yellow
     )
 
 
@@ -282,7 +313,7 @@ def update_demographics_income(year: str) -> go.Figure:
         data=data,
         name_column="bracket",
         value_column="count",
-        color="#4CAF50",
+        color=CHART_PALETTE[3],  # Teal
         sort=False,
     )
 
@@ -314,7 +345,7 @@ def update_amenities_breakdown(year: str) -> go.Figure:
         data=data,
         name_column="type",
         value_column="count",
-        color="#4CAF50",
+        color=CHART_PALETTE[3],  # Teal
     )
 
 
@@ -330,10 +361,11 @@ def update_amenities_radar(year: str, neighbourhood_id: int | None) -> go.Figure
     # Get city averages
     averages = get_city_averages(year_int)
 
+    amenity_score = averages.get("avg_amenity_score") or 50
     city_data = {
-        "parks_per_1000": averages.get("avg_amenity_score", 50) / 100 * 10,
-        "schools_per_1000": averages.get("avg_amenity_score", 50) / 100 * 5,
-        "childcare_per_1000": averages.get("avg_amenity_score", 50) / 100 * 3,
+        "parks_per_1000": amenity_score / 100 * 10,
+        "schools_per_1000": amenity_score / 100 * 5,
+        "childcare_per_1000": amenity_score / 100 * 3,
         "transit_access": 70,
     }
 
@@ -367,9 +399,9 @@ def _empty_chart(message: str) -> go.Figure:
     """Create an empty chart with a message."""
     fig = go.Figure()
     fig.update_layout(
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )
@@ -380,6 +412,6 @@ def _empty_chart(message: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     return fig

portfolio_app/pages/toronto/callbacks/map_callbacks.py

@@ -4,7 +4,13 @@
 import plotly.graph_objects as go
 from dash import Input, Output, State, callback, no_update
 
-from portfolio_app.figures import create_choropleth_figure, create_ranking_bar
+from portfolio_app.design import (
+    PAPER_BG,
+    PLOT_BG,
+    TEXT_PRIMARY,
+    TEXT_SECONDARY,
+)
+from portfolio_app.figures.toronto import create_choropleth_figure, create_ranking_bar
 from portfolio_app.toronto.services import (
     get_amenities_data,
     get_demographics_data,
@@ -267,8 +273,8 @@ def _empty_map(message: str) -> go.Figure:
             "zoom": 9.5,
         },
         margin={"l": 0, "r": 0, "t": 0, "b": 0},
-        paper_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        font_color=TEXT_PRIMARY,
     )
     fig.add_annotation(
         text=message,
@@ -277,7 +283,7 @@ def _empty_map(message: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     return fig
 
@@ -286,9 +292,9 @@ def _empty_chart(message: str) -> go.Figure:
     """Create an empty chart with a message."""
     fig = go.Figure()
     fig.update_layout(
-        paper_bgcolor="rgba(0,0,0,0)",
-        plot_bgcolor="rgba(0,0,0,0)",
-        font_color="#c9c9c9",
+        paper_bgcolor=PAPER_BG,
+        plot_bgcolor=PLOT_BG,
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )
@@ -299,6 +305,6 @@ def _empty_chart(message: str) -> go.Figure:
         x=0.5,
         y=0.5,
         showarrow=False,
-        font={"size": 14, "color": "#888888"},
+        font={"size": 14, "color": TEXT_SECONDARY},
     )
     return fig

portfolio_app/toronto/loaders/__init__.py

@@ -3,7 +3,12 @@
 from .amenities import load_amenities, load_amenity_counts
 from .base import bulk_insert, get_session, upsert_by_key
 from .census import load_census_data
-from .cmhc import load_cmhc_record, load_cmhc_rentals
+from .cmhc import (
+    ensure_toronto_cma_zone,
+    load_cmhc_record,
+    load_cmhc_rentals,
+    load_statcan_cmhc_data,
+)
 from .cmhc_crosswalk import (
     build_cmhc_neighbourhood_crosswalk,
     disaggregate_zone_value,
@@ -32,6 +37,8 @@ __all__ = [
     # Fact loaders
     "load_cmhc_rentals",
     "load_cmhc_record",
+    "load_statcan_cmhc_data",
+    "ensure_toronto_cma_zone",
     # Phase 3 loaders
     "load_census_data",
     "load_crime_data",

portfolio_app/toronto/loaders/cmhc.py

@@ -1,5 +1,9 @@
 """Loader for CMHC rental data into fact_rentals."""
 
+import logging
+from datetime import date
+from typing import Any
+
 from sqlalchemy.orm import Session
 
 from portfolio_app.toronto.models import DimCMHCZone, DimTime, FactRentals
@@ -8,6 +12,12 @@ from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord
 from .base import get_session, upsert_by_key
 from .dimensions import generate_date_key
 
+logger = logging.getLogger(__name__)
+
+# Toronto CMA zone code for CMA-level data
+TORONTO_CMA_ZONE_CODE = "TORCMA"
+TORONTO_CMA_ZONE_NAME = "Toronto CMA"
+
 
 def load_cmhc_rentals(
     survey: CMHCAnnualSurvey,
@@ -135,3 +145,117 @@ def load_cmhc_record(
         return _load(session)
     with get_session() as sess:
         return _load(sess)
+
+
+def ensure_toronto_cma_zone(session: Session | None = None) -> int:
+    """Ensure Toronto CMA zone exists in dim_cmhc_zone.
+
+    Creates the zone if it doesn't exist.
+
+    Args:
+        session: Optional existing session.
+
+    Returns:
+        The zone_key for Toronto CMA.
+    """
+
+    def _ensure(sess: Session) -> int:
+        zone = (
+            sess.query(DimCMHCZone).filter_by(zone_code=TORONTO_CMA_ZONE_CODE).first()
+        )
+        if zone:
+            return int(zone.zone_key)
+
+        # Create new zone
+        new_zone = DimCMHCZone(
+            zone_code=TORONTO_CMA_ZONE_CODE,
+            zone_name=TORONTO_CMA_ZONE_NAME,
+            geometry=None,  # CMA-level doesn't need geometry
+        )
+        sess.add(new_zone)
+        sess.flush()
+        logger.info(f"Created Toronto CMA zone with zone_key={new_zone.zone_key}")
+        return int(new_zone.zone_key)
+
+    if session:
+        return _ensure(session)
+    with get_session() as sess:
+        result = _ensure(sess)
+        sess.commit()
+        return result
+
+
+def load_statcan_cmhc_data(
+    records: list[Any],  # List of CMHCRentalRecord from statcan_cmhc parser
+    session: Session | None = None,
+) -> int:
+    """Load CMHC rental data from StatCan parser into fact_rentals.
+
+    This function handles CMA-level data from the StatCan API, which provides
+    aggregate Toronto data rather than zone-level HMIP data.
+
+    Args:
+        records: List of CMHCRentalRecord dataclass instances from statcan_cmhc parser.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+    from portfolio_app.toronto.parsers.statcan_cmhc import (
+        CMHCRentalRecord as StatCanRecord,
+    )
+
+    def _load(sess: Session) -> int:
+        # Ensure Toronto CMA zone exists
+        zone_key = ensure_toronto_cma_zone(sess)
+
+        loaded = 0
+        for record in records:
+            if not isinstance(record, StatCanRecord):
+                logger.warning(f"Skipping invalid record type: {type(record)}")
+                continue
+
+            # Generate date key for this record's survey date
+            survey_date = date(record.year, record.month, 1)
+            date_key = generate_date_key(survey_date)
+
+            # Verify time dimension exists
+            time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+            if not time_dim:
+                logger.warning(
+                    f"Time dimension not found for {survey_date}, skipping record"
+                )
+                continue
+
+            # Create fact record
+            fact = FactRentals(
+                date_key=date_key,
+                zone_key=zone_key,
+                bedroom_type=record.bedroom_type,
+                universe=record.universe,
+                avg_rent=float(record.avg_rent) if record.avg_rent else None,
+                median_rent=None,  # StatCan doesn't provide median
+                vacancy_rate=float(record.vacancy_rate)
+                if record.vacancy_rate
+                else None,
+                availability_rate=None,
+                turnover_rate=None,
+                rent_change_pct=None,
+                reliability_code=None,
+            )
+
+            # Upsert
+            inserted, updated = upsert_by_key(
+                sess, FactRentals, [fact], ["date_key", "zone_key", "bedroom_type"]
+            )
+            loaded += inserted + updated
+
+        logger.info(f"Loaded {loaded} CMHC rental records from StatCan")
+        return loaded
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        result = _load(sess)
+        sess.commit()
+        return result

portfolio_app/toronto/models/dimensions.py

@@ -8,11 +8,18 @@ from sqlalchemy.orm import Mapped, mapped_column
 
 from .base import Base
 
+# Schema constants
+RAW_TORONTO_SCHEMA = "raw_toronto"
+
 
 class DimTime(Base):
-    """Time dimension table."""
+    """Time dimension table (shared across all projects).
+
+    Note: Stays in public schema as it's a shared dimension.
+    """
 
     __tablename__ = "dim_time"
+    __table_args__ = {"schema": "public"}
 
     date_key: Mapped[int] = mapped_column(Integer, primary_key=True)
     full_date: Mapped[date] = mapped_column(Date, nullable=False, unique=True)
@@ -27,6 +34,7 @@ class DimCMHCZone(Base):
     """CMHC zone dimension table with PostGIS geometry."""
 
     __tablename__ = "dim_cmhc_zone"
+    __table_args__ = {"schema": RAW_TORONTO_SCHEMA}
 
     zone_key: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     zone_code: Mapped[str] = mapped_column(String(10), nullable=False, unique=True)
@@ -41,6 +49,7 @@ class DimNeighbourhood(Base):
     """
 
     __tablename__ = "dim_neighbourhood"
+    __table_args__ = {"schema": RAW_TORONTO_SCHEMA}
 
     neighbourhood_id: Mapped[int] = mapped_column(Integer, primary_key=True)
     name: Mapped[str] = mapped_column(String(100), nullable=False)
@@ -69,6 +78,7 @@ class DimPolicyEvent(Base):
     """Policy event dimension for time-series annotation."""
 
     __tablename__ = "dim_policy_event"
+    __table_args__ = {"schema": RAW_TORONTO_SCHEMA}
 
     event_id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     event_date: Mapped[date] = mapped_column(Date, nullable=False)

portfolio_app/toronto/models/facts.py

@@ -4,6 +4,7 @@ from sqlalchemy import ForeignKey, Index, Integer, Numeric, String
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 
 from .base import Base
+from .dimensions import RAW_TORONTO_SCHEMA
 
 
 class BridgeCMHCNeighbourhood(Base):
@@ -14,6 +15,11 @@ class BridgeCMHCNeighbourhood(Base):
     """
 
     __tablename__ = "bridge_cmhc_neighbourhood"
+    __table_args__ = (
+        Index("ix_bridge_cmhc_zone", "cmhc_zone_code"),
+        Index("ix_bridge_neighbourhood", "neighbourhood_id"),
+        {"schema": RAW_TORONTO_SCHEMA},
+    )
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     cmhc_zone_code: Mapped[str] = mapped_column(String(10), nullable=False)
@@ -22,11 +28,6 @@ class BridgeCMHCNeighbourhood(Base):
         Numeric(5, 4), nullable=False
     )  # 0.0000 to 1.0000
 
-    __table_args__ = (
-        Index("ix_bridge_cmhc_zone", "cmhc_zone_code"),
-        Index("ix_bridge_neighbourhood", "neighbourhood_id"),
-    )
-
 
 class FactCensus(Base):
     """Census statistics by neighbourhood and year.
@@ -35,6 +36,10 @@ class FactCensus(Base):
     """
 
     __tablename__ = "fact_census"
+    __table_args__ = (
+        Index("ix_fact_census_neighbourhood_year", "neighbourhood_id", "census_year"),
+        {"schema": RAW_TORONTO_SCHEMA},
+    )
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False)
@@ -66,10 +71,6 @@ class FactCensus(Base):
         Numeric(12, 2), nullable=True
     )
 
-    __table_args__ = (
-        Index("ix_fact_census_neighbourhood_year", "neighbourhood_id", "census_year"),
-    )
-
 
 class FactCrime(Base):
     """Crime statistics by neighbourhood and year.
@@ -78,6 +79,11 @@ class FactCrime(Base):
     """
 
     __tablename__ = "fact_crime"
+    __table_args__ = (
+        Index("ix_fact_crime_neighbourhood_year", "neighbourhood_id", "year"),
+        Index("ix_fact_crime_type", "crime_type"),
+        {"schema": RAW_TORONTO_SCHEMA},
+    )
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False)
@@ -86,11 +92,6 @@ class FactCrime(Base):
     count: Mapped[int] = mapped_column(Integer, nullable=False)
     rate_per_100k: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True)
 
-    __table_args__ = (
-        Index("ix_fact_crime_neighbourhood_year", "neighbourhood_id", "year"),
-        Index("ix_fact_crime_type", "crime_type"),
-    )
-
 
 class FactAmenities(Base):
     """Amenity counts by neighbourhood.
@@ -99,6 +100,11 @@ class FactAmenities(Base):
     """
 
     __tablename__ = "fact_amenities"
+    __table_args__ = (
+        Index("ix_fact_amenities_neighbourhood_year", "neighbourhood_id", "year"),
+        Index("ix_fact_amenities_type", "amenity_type"),
+        {"schema": RAW_TORONTO_SCHEMA},
+    )
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False)
@@ -106,11 +112,6 @@ class FactAmenities(Base):
     count: Mapped[int] = mapped_column(Integer, nullable=False)
     year: Mapped[int] = mapped_column(Integer, nullable=False)
 
-    __table_args__ = (
-        Index("ix_fact_amenities_neighbourhood_year", "neighbourhood_id", "year"),
-        Index("ix_fact_amenities_type", "amenity_type"),
-    )
-
 
 class FactRentals(Base):
     """Fact table for CMHC rental market data.
@@ -119,13 +120,16 @@ class FactRentals(Base):
     """
 
     __tablename__ = "fact_rentals"
+    __table_args__ = {"schema": RAW_TORONTO_SCHEMA}
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     date_key: Mapped[int] = mapped_column(
-        Integer, ForeignKey("dim_time.date_key"), nullable=False
+        Integer, ForeignKey("public.dim_time.date_key"), nullable=False
     )
     zone_key: Mapped[int] = mapped_column(
-        Integer, ForeignKey("dim_cmhc_zone.zone_key"), nullable=False
+        Integer,
+        ForeignKey(f"{RAW_TORONTO_SCHEMA}.dim_cmhc_zone.zone_key"),
+        nullable=False,
     )
     bedroom_type: Mapped[str] = mapped_column(String(20), nullable=False)
     universe: Mapped[int | None] = mapped_column(Integer, nullable=True)
@@ -139,6 +143,6 @@ class FactRentals(Base):
     rent_change_pct: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
     reliability_code: Mapped[str | None] = mapped_column(String(2), nullable=True)
 
-    # Relationships
-    time = relationship("DimTime", backref="rentals")
-    zone = relationship("DimCMHCZone", backref="rentals")
+    # Relationships - explicit foreign_keys needed for cross-schema joins
+    time = relationship("DimTime", foreign_keys=[date_key], backref="rentals")
+    zone = relationship("DimCMHCZone", foreign_keys=[zone_key], backref="rentals")

portfolio_app/toronto/parsers/statcan_cmhc.py

@@ -0,0 +1,383 @@
+"""Parser for CMHC rental data via Statistics Canada API.
+
+Downloads rental market data (average rent, vacancy rates, universe)
+from Statistics Canada's Web Data Service.
+
+Data Sources:
+- Table 34-10-0127: Vacancy rates
+- Table 34-10-0129: Rental universe (total units)
+- Table 34-10-0133: Average rent by bedroom type
+"""
+
+import contextlib
+import io
+import logging
+import zipfile
+from dataclasses import dataclass
+from decimal import Decimal
+from pathlib import Path
+from typing import Any
+
+import httpx
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+# StatCan Web Data Service endpoints
+STATCAN_API_BASE = "https://www150.statcan.gc.ca/t1/wds/rest"
+STATCAN_DOWNLOAD_BASE = "https://www150.statcan.gc.ca/n1/tbl/csv"
+
+# CMHC table IDs
+CMHC_TABLES = {
+    "vacancy": "34100127",
+    "universe": "34100129",
+    "rent": "34100133",
+}
+
+# Toronto CMA identifier in StatCan data
+TORONTO_DGUID = "2011S0503535"
+TORONTO_GEO_NAME = "Toronto, Ontario"
+
+
+@dataclass
+class CMHCRentalRecord:
+    """Rental market record for database loading."""
+
+    year: int
+    month: int  # CMHC surveys in October, so month=10
+    zone_name: str
+    bedroom_type: str
+    avg_rent: Decimal | None
+    vacancy_rate: Decimal | None
+    universe: int | None
+
+
+class StatCanCMHCParser:
+    """Parser for CMHC rental data from Statistics Canada.
+
+    Downloads and processes rental market survey data including:
+    - Average rents by bedroom type
+    - Vacancy rates
+    - Rental universe (total units)
+
+    Data is available from 1987 to present, updated annually in January.
+    """
+
+    BEDROOM_TYPE_MAP = {
+        "Bachelor units": "bachelor",
+        "One bedroom units": "1bed",
+        "Two bedroom units": "2bed",
+        "Three bedroom units": "3bed",
+        "Total": "total",
+    }
+
+    STRUCTURE_FILTER = "Apartment structures of six units and over"
+
+    def __init__(
+        self,
+        cache_dir: Path | None = None,
+        timeout: float = 60.0,
+    ) -> None:
+        """Initialize parser.
+
+        Args:
+            cache_dir: Optional directory for caching downloaded files.
+            timeout: HTTP request timeout in seconds.
+        """
+        self._cache_dir = cache_dir
+        self._timeout = timeout
+        self._client: httpx.Client | None = None
+
+    @property
+    def client(self) -> httpx.Client:
+        """Lazy-initialize HTTP client."""
+        if self._client is None:
+            self._client = httpx.Client(
+                timeout=self._timeout,
+                follow_redirects=True,
+            )
+        return self._client
+
+    def close(self) -> None:
+        """Close HTTP client."""
+        if self._client is not None:
+            self._client.close()
+            self._client = None
+
+    def __enter__(self) -> "StatCanCMHCParser":
+        return self
+
+    def __exit__(self, *args: Any) -> None:
+        self.close()
+
+    def _get_download_url(self, table_id: str) -> str:
+        """Get CSV download URL for a StatCan table.
+
+        Args:
+            table_id: StatCan table ID (e.g., "34100133").
+
+        Returns:
+            Direct download URL for the CSV zip file.
+        """
+        api_url = f"{STATCAN_API_BASE}/getFullTableDownloadCSV/{table_id}/en"
+        response = self.client.get(api_url)
+        response.raise_for_status()
+
+        data = response.json()
+        if data.get("status") != "SUCCESS":
+            raise ValueError(f"StatCan API error: {data}")
+
+        return str(data["object"])
+
+    def _download_table(self, table_id: str) -> pd.DataFrame:
+        """Download and extract a StatCan table as DataFrame.
+
+        Args:
+            table_id: StatCan table ID.
+
+        Returns:
+            DataFrame with table data.
+        """
+        # Check cache first
+        if self._cache_dir:
+            cache_file = self._cache_dir / f"{table_id}.csv"
+            if cache_file.exists():
+                logger.debug(f"Loading {table_id} from cache")
+                return pd.read_csv(cache_file)
+
+        # Get download URL and fetch
+        download_url = self._get_download_url(table_id)
+        logger.info(f"Downloading StatCan table {table_id}...")
+
+        response = self.client.get(download_url)
+        response.raise_for_status()
+
+        # Extract CSV from zip
+        with zipfile.ZipFile(io.BytesIO(response.content)) as zf:
+            csv_name = f"{table_id}.csv"
+            with zf.open(csv_name) as f:
+                df = pd.read_csv(f)
+
+        # Cache if directory specified
+        if self._cache_dir:
+            self._cache_dir.mkdir(parents=True, exist_ok=True)
+            df.to_csv(self._cache_dir / f"{table_id}.csv", index=False)
+
+        logger.info(f"Downloaded {len(df)} records from table {table_id}")
+        return df
+
+    def _filter_toronto(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Filter DataFrame to Toronto CMA only.
+
+        Args:
+            df: Full StatCan DataFrame.
+
+        Returns:
+            DataFrame filtered to Toronto.
+        """
+        # Try DGUID first, then GEO name
+        if "DGUID" in df.columns:
+            toronto_df = df[df["DGUID"] == TORONTO_DGUID]
+            if len(toronto_df) > 0:
+                return toronto_df
+
+        if "GEO" in df.columns:
+            return df[df["GEO"] == TORONTO_GEO_NAME]
+
+        raise ValueError("Could not identify Toronto data in DataFrame")
+
+    def get_vacancy_rates(
+        self,
+        years: list[int] | None = None,
+    ) -> dict[int, Decimal]:
+        """Fetch Toronto vacancy rates by year.
+
+        Args:
+            years: Optional list of years to filter.
+
+        Returns:
+            Dictionary mapping year to vacancy rate.
+        """
+        df = self._download_table(CMHC_TABLES["vacancy"])
+        df = self._filter_toronto(df)
+
+        # Filter years if specified
+        if years:
+            df = df[df["REF_DATE"].isin(years)]
+
+        # Extract year -> rate mapping
+        rates = {}
+        for _, row in df.iterrows():
+            year = int(row["REF_DATE"])
+            value = row.get("VALUE")
+            if pd.notna(value):
+                rates[year] = Decimal(str(value))
+
+        logger.info(f"Fetched vacancy rates for {len(rates)} years")
+        return rates
+
+    def get_rental_universe(
+        self,
+        years: list[int] | None = None,
+    ) -> dict[tuple[int, str], int]:
+        """Fetch Toronto rental universe (total units) by year and bedroom type.
+
+        Args:
+            years: Optional list of years to filter.
+
+        Returns:
+            Dictionary mapping (year, bedroom_type) to unit count.
+        """
+        df = self._download_table(CMHC_TABLES["universe"])
+        df = self._filter_toronto(df)
+
+        # Filter to standard apartment structures
+        if "Type of structure" in df.columns:
+            df = df[df["Type of structure"] == self.STRUCTURE_FILTER]
+
+        if years:
+            df = df[df["REF_DATE"].isin(years)]
+
+        universe = {}
+        for _, row in df.iterrows():
+            year = int(row["REF_DATE"])
+            bedroom_raw = row.get("Type of unit", "Total")
+            bedroom = self.BEDROOM_TYPE_MAP.get(bedroom_raw, "other")
+            value = row.get("VALUE")
+
+            if pd.notna(value) and value is not None:
+                universe[(year, bedroom)] = int(str(value))
+
+        logger.info(
+            f"Fetched rental universe for {len(universe)} year/bedroom combinations"
+        )
+        return universe
+
+    def get_average_rents(
+        self,
+        years: list[int] | None = None,
+    ) -> dict[tuple[int, str], Decimal]:
+        """Fetch Toronto average rents by year and bedroom type.
+
+        Args:
+            years: Optional list of years to filter.
+
+        Returns:
+            Dictionary mapping (year, bedroom_type) to average rent.
+        """
+        df = self._download_table(CMHC_TABLES["rent"])
+        df = self._filter_toronto(df)
+
+        # Filter to standard apartment structures (most reliable data)
+        if "Type of structure" in df.columns:
+            df = df[df["Type of structure"] == self.STRUCTURE_FILTER]
+
+        if years:
+            df = df[df["REF_DATE"].isin(years)]
+
+        rents = {}
+        for _, row in df.iterrows():
+            year = int(row["REF_DATE"])
+            bedroom_raw = row.get("Type of unit", "Total")
+            bedroom = self.BEDROOM_TYPE_MAP.get(bedroom_raw, "other")
+            value = row.get("VALUE")
+
+            if pd.notna(value) and str(value) not in ("F", ".."):
+                with contextlib.suppress(Exception):
+                    rents[(year, bedroom)] = Decimal(str(value))
+
+        logger.info(f"Fetched average rents for {len(rents)} year/bedroom combinations")
+        return rents
+
+    def get_all_rental_data(
+        self,
+        start_year: int = 2014,
+        end_year: int | None = None,
+    ) -> list[CMHCRentalRecord]:
+        """Fetch all Toronto rental data and combine into records.
+
+        Args:
+            start_year: First year to include.
+            end_year: Last year to include (defaults to current year + 1).
+
+        Returns:
+            List of CMHCRentalRecord objects ready for database loading.
+        """
+        import datetime
+
+        if end_year is None:
+            end_year = datetime.date.today().year + 1
+
+        years = list(range(start_year, end_year + 1))
+
+        logger.info(
+            f"Fetching CMHC rental data for Toronto ({start_year}-{end_year})..."
+        )
+
+        # Fetch all data types
+        vacancy_rates = self.get_vacancy_rates(years)
+        rents = self.get_average_rents(years)
+        universe = self.get_rental_universe(years)
+
+        # Combine into records
+        records = []
+        bedroom_types = ["bachelor", "1bed", "2bed", "3bed"]
+
+        for year in years:
+            vacancy = vacancy_rates.get(year)
+
+            for bedroom in bedroom_types:
+                avg_rent = rents.get((year, bedroom))
+                units = universe.get((year, bedroom))
+
+                # Skip if no rent data for this year/bedroom
+                if avg_rent is None:
+                    continue
+
+                records.append(
+                    CMHCRentalRecord(
+                        year=year,
+                        month=10,  # CMHC surveys in October
+                        zone_name="Toronto CMA",
+                        bedroom_type=bedroom,
+                        avg_rent=avg_rent,
+                        vacancy_rate=vacancy,
+                        universe=units,
+                    )
+                )
+
+        logger.info(f"Created {len(records)} CMHC rental records")
+        return records
+
+
+def fetch_toronto_rental_data(
+    start_year: int = 2014,
+    end_year: int | None = None,
+    cache_dir: Path | None = None,
+) -> list[CMHCRentalRecord]:
+    """Convenience function to fetch Toronto rental data.
+
+    Args:
+        start_year: First year to include.
+        end_year: Last year to include.
+        cache_dir: Optional cache directory.
+
+    Returns:
+        List of CMHCRentalRecord objects.
+    """
+    with StatCanCMHCParser(cache_dir=cache_dir) as parser:
+        return parser.get_all_rental_data(start_year, end_year)
+
+
+if __name__ == "__main__":
+    # Test the parser
+    logging.basicConfig(level=logging.INFO)
+
+    records = fetch_toronto_rental_data(start_year=2020)
+
+    print(f"\nFetched {len(records)} records")
+    print("\nSample records:")
+    for r in records[:10]:
+        print(
+            f"  {r.year} {r.bedroom_type}: ${r.avg_rent} rent, {r.vacancy_rate}% vacancy"
+        )

portfolio_app/toronto/parsers/toronto_open_data.py

@@ -6,6 +6,7 @@ from the City of Toronto's Open Data Portal.
 API Documentation: https://open.toronto.ca/dataset/
 """
 
+import contextlib
 import json
 import logging
 from decimal import Decimal
@@ -193,6 +194,9 @@ class TorontoOpenDataParser:
     def _fetch_geojson(self, package_id: str) -> dict[str, Any]:
         """Fetch GeoJSON data from a package.
 
+        Handles both pure GeoJSON responses and CSV responses with embedded
+        geometry columns (common in Toronto Open Data).
+
         Args:
             package_id: The package/dataset ID.
 
@@ -212,16 +216,65 @@ class TorontoOpenDataParser:
 
         response = self.client.get(url)
         response.raise_for_status()
-        data = response.json()
 
-        # Cache the response
+        # Try to parse as JSON first
+        try:
+            data = response.json()
+            # If it's already a valid GeoJSON FeatureCollection, return it
+            if isinstance(data, dict) and data.get("type") == "FeatureCollection":
+                if self._cache_dir:
+                    self._cache_dir.mkdir(parents=True, exist_ok=True)
+                    cache_file = self._cache_dir / f"{package_id}.geojson"
+                    with open(cache_file, "w", encoding="utf-8") as f:
+                        json.dump(data, f)
+                return dict(data)
+        except json.JSONDecodeError:
+            pass
+
+        # If JSON parsing failed, it's likely CSV with embedded geometry
+        # Parse CSV and convert to GeoJSON FeatureCollection
+        logger.info("Response is CSV format, converting to GeoJSON...")
+        import csv
+        import io
+
+        # Increase field size limit for large geometry columns
+        csv.field_size_limit(10 * 1024 * 1024)  # 10 MB
+
+        csv_text = response.text
+        reader = csv.DictReader(io.StringIO(csv_text))
+
+        features = []
+        for row in reader:
+            # Extract geometry from the 'geometry' column if present
+            geometry = None
+            if "geometry" in row and row["geometry"]:
+                with contextlib.suppress(json.JSONDecodeError):
+                    geometry = json.loads(row["geometry"])
+
+            # Build properties from all other columns
+            properties = {k: v for k, v in row.items() if k != "geometry"}
+
+            features.append(
+                {
+                    "type": "Feature",
+                    "geometry": geometry,
+                    "properties": properties,
+                }
+            )
+
+        geojson_data: dict[str, Any] = {
+            "type": "FeatureCollection",
+            "features": features,
+        }
+
+        # Cache the converted response
         if self._cache_dir:
             self._cache_dir.mkdir(parents=True, exist_ok=True)
             cache_file = self._cache_dir / f"{package_id}.geojson"
             with open(cache_file, "w", encoding="utf-8") as f:
-                json.dump(data, f)
+                json.dump(geojson_data, f)
 
-        return dict(data)
+        return geojson_data
 
     def _fetch_csv_as_json(self, package_id: str) -> list[dict[str, Any]]:
         """Fetch CSV data as JSON records via CKAN datastore.
@@ -282,29 +335,32 @@ class TorontoOpenDataParser:
             props = feature.get("properties", {})
             geometry = feature.get("geometry")
 
-            # Extract area_id from various possible property names
-            area_id = props.get("AREA_ID") or props.get("area_id")
-            if area_id is None:
-                # Try AREA_SHORT_CODE as fallback
-                short_code = props.get("AREA_SHORT_CODE", "")
-                if short_code:
-                    # Extract numeric part
-                    area_id = int("".join(c for c in short_code if c.isdigit()) or "0")
+            # Use AREA_SHORT_CODE as the primary ID (1-158 range)
+            # AREA_ID is a large internal identifier not useful for our schema
+            short_code = props.get("AREA_SHORT_CODE") or props.get(
+                "area_short_code", ""
+            )
+            if short_code:
+                area_id = int("".join(c for c in str(short_code) if c.isdigit()) or "0")
+            else:
+                # Fallback to _id (row number) if AREA_SHORT_CODE not available
+                area_id = int(props.get("_id", 0))
+
+            if area_id == 0:
+                logger.warning(f"Skipping neighbourhood with no valid ID: {props}")
+                continue
 
             area_name = (
                 props.get("AREA_NAME")
                 or props.get("area_name")
                 or f"Neighbourhood {area_id}"
             )
-            area_short_code = props.get("AREA_SHORT_CODE") or props.get(
-                "area_short_code"
-            )
 
             records.append(
                 NeighbourhoodRecord(
-                    area_id=int(area_id),
+                    area_id=area_id,
                     area_name=str(area_name),
-                    area_short_code=area_short_code,
+                    area_short_code=str(short_code) if short_code else None,
                     geometry=geometry,
                 )
             )
@@ -314,17 +370,17 @@ class TorontoOpenDataParser:
 
     # Mapping of indicator names to CensusRecord fields
     # Keys are partial matches (case-insensitive) found in the "Characteristic" column
+    # Order matters - first match wins, so more specific patterns come first
+    # Note: owner/renter counts are raw numbers, not percentages - calculated in dbt
     CENSUS_INDICATOR_MAPPING: dict[str, str] = {
         "population, 2021": "population",
         "population, 2016": "population",
         "population density per square kilometre": "population_density",
-        "median total income of household": "median_household_income",
-        "average total income of household": "average_household_income",
+        "median total income of households in": "median_household_income",
+        "average total income of households in": "average_household_income",
         "unemployment rate": "unemployment_rate",
         "bachelor's degree or higher": "pct_bachelors_or_higher",
-        "owner": "pct_owner_occupied",
-        "renter": "pct_renter_occupied",
-        "median age": "median_age",
+        "average age": "median_age",
         "average value of dwellings": "average_dwelling_value",
     }
 
@@ -358,17 +414,31 @@ class TorontoOpenDataParser:
         logger.info(f"Fetched {len(raw_records)} census profile rows")
 
         # Find the characteristic/indicator column name
+        # Prioritize "Characteristic" over "Category" since both may exist
         sample_row = raw_records[0]
         char_col = None
-        for col in sample_row:
-            col_lower = col.lower()
-            if "characteristic" in col_lower or "category" in col_lower:
-                char_col = col
-                break
+
+        # First try exact match for Characteristic
+        if "Characteristic" in sample_row:
+            char_col = "Characteristic"
+        else:
+            # Fall back to pattern matching
+            for col in sample_row:
+                col_lower = col.lower()
+                if "characteristic" in col_lower:
+                    char_col = col
+                    break
+
+            # Last resort: try Category
+            if not char_col:
+                for col in sample_row:
+                    if "category" in col.lower():
+                        char_col = col
+                        break
 
         if not char_col:
-            # Try common column names
-            for candidate in ["Characteristic", "Category", "Topic", "_id"]:
+            # Try other common column names
+            for candidate in ["Topic", "_id"]:
                 if candidate in sample_row:
                     char_col = candidate
                     break

portfolio_app/toronto/services/geometry_service.py

@@ -37,7 +37,7 @@ def get_neighbourhoods_geojson(year: int = 2021) -> dict[str, Any]:
             ST_AsGeoJSON(geometry)::json as geom,
             population,
             livability_score
-        FROM mart_neighbourhood_overview
+        FROM public_marts.mart_neighbourhood_overview
         WHERE year = :year
           AND geometry IS NOT NULL
     """

portfolio_app/toronto/services/neighbourhood_service.py

@@ -1,5 +1,6 @@
 """Service layer for querying neighbourhood data from dbt marts."""
 
+import logging
 from functools import lru_cache
 from typing import Any
 
@@ -8,6 +9,8 @@ from sqlalchemy import text
 
 from portfolio_app.toronto.models import get_engine
 
+logger = logging.getLogger(__name__)
+
 
 def _execute_query(sql: str, params: dict[str, Any] | None = None) -> pd.DataFrame:
     """Execute SQL query and return DataFrame.
@@ -23,8 +26,10 @@ def _execute_query(sql: str, params: dict[str, Any] | None = None) -> pd.DataFra
         engine = get_engine()
         with engine.connect() as conn:
             return pd.read_sql(text(sql), conn, params=params)
-    except Exception:
-        # Return empty DataFrame on connection or query error
+    except Exception as e:
+        logger.error(f"Query failed: {e}")
+        logger.debug(f"Failed SQL: {sql}")
+        logger.debug(f"Params: {params}")
         return pd.DataFrame()
 
 
@@ -56,7 +61,7 @@ def get_overview_data(year: int = 2021) -> pd.DataFrame:
             rent_to_income_pct,
             avg_rent_2bed,
             total_amenities_per_1000
-        FROM mart_neighbourhood_overview
+        FROM public_marts.mart_neighbourhood_overview
         WHERE year = :year
         ORDER BY livability_score DESC NULLS LAST
     """
@@ -95,7 +100,7 @@ def get_housing_data(year: int = 2021) -> pd.DataFrame:
             affordability_index,
             rent_yoy_change_pct,
             income_quintile
-        FROM mart_neighbourhood_housing
+        FROM public_marts.mart_neighbourhood_housing
         WHERE year = :year
         ORDER BY affordability_index ASC NULLS LAST
     """
@@ -112,26 +117,22 @@ def get_safety_data(year: int = 2021) -> pd.DataFrame:
 
     Returns:
         DataFrame with columns: neighbourhood_id, neighbourhood_name,
-        total_crime_rate, violent_crime_rate, property_crime_rate, etc.
+        total_crime_rate, violent_crimes, property_crimes, etc.
     """
     sql = """
         SELECT
             neighbourhood_id,
             neighbourhood_name,
             year,
-            total_crimes,
+            total_incidents as total_crimes,
             crime_rate_per_100k as total_crime_rate,
-            violent_crimes,
-            violent_crime_rate,
-            property_crimes,
-            property_crime_rate,
-            theft_crimes,
-            theft_rate,
-            crime_yoy_change_pct,
-            crime_trend
-        FROM mart_neighbourhood_safety
+            assault_count + robbery_count + homicide_count as violent_crimes,
+            break_enter_count + auto_theft_count as property_crimes,
+            theft_over_count as theft_crimes,
+            crime_yoy_change_pct
+        FROM public_marts.mart_neighbourhood_safety
         WHERE year = :year
-        ORDER BY total_crime_rate ASC NULLS LAST
+        ORDER BY crime_rate_per_100k ASC NULLS LAST
     """
     return _execute_query(sql, {"year": year})
 
@@ -152,22 +153,22 @@ def get_demographics_data(year: int = 2021) -> pd.DataFrame:
         SELECT
             neighbourhood_id,
             neighbourhood_name,
-            census_year as year,
+            year,
             population,
             population_density,
-            population_change_pct,
             median_household_income,
             average_household_income,
             income_quintile,
+            income_index,
             median_age,
-            pct_under_18,
-            pct_18_to_64,
-            pct_65_plus,
-            pct_bachelors_or_higher,
+            age_index,
+            pct_owner_occupied,
+            pct_renter_occupied,
+            education_bachelors_pct as pct_bachelors_or_higher,
             unemployment_rate,
-            diversity_index
-        FROM mart_neighbourhood_demographics
-        WHERE census_year = :year
+            tenure_diversity_index as diversity_index
+        FROM public_marts.mart_neighbourhood_demographics
+        WHERE year = :year
         ORDER BY population DESC NULLS LAST
     """
     return _execute_query(sql, {"year": year})
@@ -183,26 +184,26 @@ def get_amenities_data(year: int = 2021) -> pd.DataFrame:
 
     Returns:
         DataFrame with columns: neighbourhood_id, neighbourhood_name,
-        amenity_score, parks_per_capita, schools_per_capita, transit_score, etc.
+        amenity_score, parks_per_1000, schools_per_1000, etc.
     """
     sql = """
         SELECT
             neighbourhood_id,
             neighbourhood_name,
             year,
-            park_count,
+            parks_count as park_count,
             parks_per_1000,
-            school_count,
+            schools_count as school_count,
             schools_per_1000,
-            childcare_count,
-            childcare_per_1000,
+            transit_count as childcare_count,
+            transit_per_1000 as childcare_per_1000,
             total_amenities,
             total_amenities_per_1000,
-            amenity_score,
-            amenity_rank
-        FROM mart_neighbourhood_amenities
+            amenity_index as amenity_score,
+            amenity_tier as amenity_rank
+        FROM public_marts.mart_neighbourhood_amenities
         WHERE year = :year
-        ORDER BY amenity_score DESC NULLS LAST
+        ORDER BY amenity_index DESC NULLS LAST
     """
     return _execute_query(sql, {"year": year})
 
@@ -249,17 +250,17 @@ def get_neighbourhood_details(
             a.park_count,
             a.school_count,
             a.total_amenities
-        FROM mart_neighbourhood_overview o
-        LEFT JOIN mart_neighbourhood_safety s
+        FROM public_marts.mart_neighbourhood_overview o
+        LEFT JOIN public_marts.mart_neighbourhood_safety s
             ON o.neighbourhood_id = s.neighbourhood_id
             AND o.year = s.year
-        LEFT JOIN mart_neighbourhood_housing h
+        LEFT JOIN public_marts.mart_neighbourhood_housing h
             ON o.neighbourhood_id = h.neighbourhood_id
             AND o.year = h.year
-        LEFT JOIN mart_neighbourhood_demographics d
+        LEFT JOIN public_marts.mart_neighbourhood_demographics d
             ON o.neighbourhood_id = d.neighbourhood_id
             AND o.year = d.census_year
-        LEFT JOIN mart_neighbourhood_amenities a
+        LEFT JOIN public_marts.mart_neighbourhood_amenities a
             ON o.neighbourhood_id = a.neighbourhood_id
             AND o.year = a.year
         WHERE o.neighbourhood_id = :neighbourhood_id
@@ -288,7 +289,7 @@ def get_neighbourhood_list(year: int = 2021) -> list[dict[str, Any]]:
             neighbourhood_id,
             neighbourhood_name,
             population
-        FROM mart_neighbourhood_overview
+        FROM public_marts.mart_neighbourhood_overview
         WHERE year = :year
         ORDER BY neighbourhood_name
     """
@@ -317,19 +318,19 @@ def get_rankings(
     """
     # Map metrics to their source tables
     table_map = {
-        "livability_score": "mart_neighbourhood_overview",
-        "safety_score": "mart_neighbourhood_overview",
-        "affordability_score": "mart_neighbourhood_overview",
-        "amenity_score": "mart_neighbourhood_overview",
-        "crime_rate_per_100k": "mart_neighbourhood_safety",
-        "total_crime_rate": "mart_neighbourhood_safety",
-        "avg_rent_2bed": "mart_neighbourhood_housing",
-        "affordability_index": "mart_neighbourhood_housing",
-        "population": "mart_neighbourhood_demographics",
-        "median_household_income": "mart_neighbourhood_demographics",
+        "livability_score": "public_marts.mart_neighbourhood_overview",
+        "safety_score": "public_marts.mart_neighbourhood_overview",
+        "affordability_score": "public_marts.mart_neighbourhood_overview",
+        "amenity_score": "public_marts.mart_neighbourhood_overview",
+        "crime_rate_per_100k": "public_marts.mart_neighbourhood_safety",
+        "total_crime_rate": "public_marts.mart_neighbourhood_safety",
+        "avg_rent_2bed": "public_marts.mart_neighbourhood_housing",
+        "affordability_index": "public_marts.mart_neighbourhood_housing",
+        "population": "public_marts.mart_neighbourhood_demographics",
+        "median_household_income": "public_marts.mart_neighbourhood_demographics",
     }
 
-    table = table_map.get(metric, "mart_neighbourhood_overview")
+    table = table_map.get(metric, "public_marts.mart_neighbourhood_overview")
     year_col = "census_year" if "demographics" in table else "year"
 
     order = "ASC" if ascending else "DESC"
@@ -375,7 +376,7 @@ def get_city_averages(year: int = 2021) -> dict[str, Any]:
             AVG(crime_rate_per_100k) as avg_crime_rate,
             AVG(avg_rent_2bed) as avg_rent_2bed,
             AVG(rent_to_income_pct) as avg_rent_to_income
-        FROM mart_neighbourhood_overview
+        FROM public_marts.mart_neighbourhood_overview
         WHERE year = :year
     """
     df = _execute_query(sql, {"year": year})

pyproject.toml

@@ -34,6 +34,8 @@ dependencies = [
     "pandas>=2.3",
     "geopandas>=1.1",
     "shapely>=2.0",
+    "pyproj>=3.6",
+    "statsmodels>=0.14",
 
     # Visualization
     "dash>=3.3",
@@ -69,6 +71,10 @@ dev = [
     # Pre-commit
     "pre-commit>=4.0",
 
+    # Jupyter
+    "jupyter>=1.0",
+    "ipykernel>=6.29",
+
     # Type stubs
     "pandas-stubs",
     "types-requests",

scripts/data/load_toronto_data.py

@@ -28,8 +28,13 @@ from datetime import date
 from pathlib import Path
 from typing import Any
 
+from dotenv import load_dotenv
+
 # Add project root to path
 PROJECT_ROOT = Path(__file__).parent.parent.parent
+
+# Load .env file so dbt can access POSTGRES_* environment variables
+load_dotenv(PROJECT_ROOT / ".env")
 sys.path.insert(0, str(PROJECT_ROOT))
 
 from portfolio_app.toronto.loaders import (  # noqa: E402
@@ -38,12 +43,16 @@ from portfolio_app.toronto.loaders import (  # noqa: E402
     load_census_data,
     load_crime_data,
     load_neighbourhoods,
+    load_statcan_cmhc_data,
     load_time_dimension,
 )
 from portfolio_app.toronto.parsers import (  # noqa: E402
     TorontoOpenDataParser,
     TorontoPoliceParser,
 )
+from portfolio_app.toronto.parsers.statcan_cmhc import (  # noqa: E402
+    fetch_toronto_rental_data,
+)
 from portfolio_app.toronto.schemas import Neighbourhood  # noqa: E402
 
 # Configure logging
@@ -91,6 +100,9 @@ class DataPipeline:
                 # 5. Load amenities
                 self._load_amenities(session)
 
+                # 6. Load CMHC rental data from StatCan
+                self._load_rentals(session)
+
                 session.commit()
                 logger.info("All data committed to database")
 
@@ -241,6 +253,32 @@ class DataPipeline:
 
         self.stats["amenities"] = total_count
 
+    def _load_rentals(self, session: Any) -> None:
+        """Fetch and load CMHC rental data from StatCan."""
+        logger.info("Fetching CMHC rental data from Statistics Canada...")
+
+        if self.dry_run:
+            logger.info("  [DRY RUN] Would fetch and load CMHC rental data")
+            return
+
+        try:
+            # Fetch rental data (2014-present)
+            rental_records = fetch_toronto_rental_data(start_year=2014)
+
+            if not rental_records:
+                logger.warning("  No rental records fetched")
+                return
+
+            count = load_statcan_cmhc_data(rental_records, session)
+            self.stats["rentals"] = count
+            logger.info(f"  Loaded {count} CMHC rental records")
+        except Exception as e:
+            logger.warning(f"  Failed to load CMHC rental data: {e}")
+            if self.verbose:
+                import traceback
+
+                traceback.print_exc()
+
     def run_dbt(self) -> bool:
         """Run dbt to transform data.
 
@@ -250,30 +288,46 @@ class DataPipeline:
         logger.info("Running dbt transformations...")
 
         dbt_project_dir = PROJECT_ROOT / "dbt"
+        venv_dbt = PROJECT_ROOT / ".venv" / "bin" / "dbt"
+
+        # Use venv dbt if available, otherwise fall back to system dbt
+        dbt_cmd = str(venv_dbt) if venv_dbt.exists() else "dbt"
 
         if not dbt_project_dir.exists():
             logger.error(f"dbt project directory not found: {dbt_project_dir}")
             return False
 
         if self.dry_run:
+            logger.info("  [DRY RUN] Would run: dbt deps")
             logger.info("  [DRY RUN] Would run: dbt run")
             logger.info("  [DRY RUN] Would run: dbt test")
             return True
 
         try:
-            # Run dbt models
-            logger.info("  Running dbt run...")
+            # Install dbt packages if needed
+            logger.info("  Running dbt deps...")
             result = subprocess.run(
-                ["dbt", "run"],
+                [dbt_cmd, "deps", "--profiles-dir", str(dbt_project_dir)],
                 cwd=dbt_project_dir,
                 capture_output=True,
                 text=True,
             )
 
             if result.returncode != 0:
-                logger.error(f"dbt run failed:\n{result.stderr}")
-                if self.verbose:
-                    logger.debug(f"dbt output:\n{result.stdout}")
+                logger.error(f"dbt deps failed:\n{result.stdout}\n{result.stderr}")
+                return False
+
+            # Run dbt models
+            logger.info("  Running dbt run...")
+            result = subprocess.run(
+                [dbt_cmd, "run", "--profiles-dir", str(dbt_project_dir)],
+                cwd=dbt_project_dir,
+                capture_output=True,
+                text=True,
+            )
+
+            if result.returncode != 0:
+                logger.error(f"dbt run failed:\n{result.stdout}\n{result.stderr}")
                 return False
 
             logger.info("  dbt run completed successfully")
@@ -281,14 +335,16 @@ class DataPipeline:
             # Run dbt tests
             logger.info("  Running dbt test...")
             result = subprocess.run(
-                ["dbt", "test"],
+                [dbt_cmd, "test", "--profiles-dir", str(dbt_project_dir)],
                 cwd=dbt_project_dir,
                 capture_output=True,
                 text=True,
             )
 
             if result.returncode != 0:
-                logger.warning(f"dbt test had failures:\n{result.stderr}")
+                logger.warning(
+                    f"dbt test had failures:\n{result.stdout}\n{result.stderr}"
+                )
                 # Don't fail on test failures, just warn
             else:
                 logger.info("  dbt test completed successfully")