Merge branch 'staging' into development

Reviewed-on: #104

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.
 
 ---
@@ -21,21 +64,18 @@ Working context for Claude Code on the Analytics Portfolio project.
 make setup          # Install deps, create .env, init pre-commit
 make docker-up      # Start PostgreSQL + PostGIS (auto-detects x86/ARM)
 make docker-down    # Stop containers
-make docker-logs    # View container logs
 make db-init        # Initialize database schema
 make db-reset       # Drop and recreate database (DESTRUCTIVE)
 
 # Data Loading
-make load-data      # Load Toronto data from APIs, seed dev data
+make load-data      # Load all project data (currently: Toronto)
-make load-data-only # Load Toronto data without dbt or seeding
+make load-toronto   # Load Toronto data from APIs
-make seed-data      # Seed sample development data
 
 # Application
 make run            # Start Dash dev server
 
 # Testing & Quality
 make test           # Run pytest
-make test-cov       # Run pytest with coverage
 make lint           # Run ruff linter
 make format         # Run ruff formatter
 make typecheck      # Run mypy type checker
@@ -46,8 +86,7 @@ make dbt-run        # Run dbt models
 make dbt-test       # Run dbt tests
 make dbt-docs       # Generate and serve dbt documentation
 
-# Maintenance
+# Run `make help` for full target list
-make clean          # Remove build artifacts and caches
 ```
 
 ### Branch Workflow
@@ -71,50 +110,22 @@ make clean          # Remove build artifacts and caches
 
 ### Module Responsibilities
 
-| Directory | Contains | Purpose |
+| Directory | Purpose |
-|-----------|----------|---------|
+|-----------|---------|
-| `schemas/` | Pydantic models | Data validation |
+| `schemas/` | Pydantic models for data validation |
-| `models/` | SQLAlchemy ORM | Database persistence |
+| `models/` | SQLAlchemy ORM for database persistence |
-| `parsers/` | API/CSV extraction | Raw data ingestion |
+| `parsers/` | API/CSV extraction for raw data ingestion |
-| `loaders/` | Database operations | Data loading |
+| `loaders/` | Database operations for data loading |
-| `services/` | Query functions | dbt mart queries, business logic |
+| `services/` | Query functions for dbt mart queries |
-| `figures/` | Chart factories | Plotly figure generation |
+| `figures/` | Chart factories for Plotly figure generation |
-| `callbacks/` | Dash callbacks | In `pages/{dashboard}/callbacks/` |
+| `errors/` | Custom exception classes (see `errors/exceptions.py`) |
-| `errors/` | Exception classes | Custom exceptions |
-| `utils/` | Helper modules | Markdown loading, shared utilities |
-
-### 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."""
-```
 
 ### 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
 
 ---
 
@@ -122,17 +133,21 @@ class LoadError(PortfolioError):
 
 **Entry Point:** `portfolio_app/app.py` (Dash app factory with Pages routing)
 
-| Directory | Purpose | Notes |
+| Directory | Purpose |
-|-----------|---------|-------|
+|-----------|---------|
-| `pages/` | Dash Pages (file-based routing) | URLs match file paths |
+| `pages/` | Dash Pages (file-based routing) |
-| `pages/toronto/` | Toronto Dashboard | `tabs/` for layouts, `callbacks/` for interactions |
+| `pages/toronto/` | Toronto Dashboard (`tabs/` for layouts, `callbacks/` for interactions) |
-| `components/` | Shared UI components | metric_card, sidebar, map_controls, time_slider |
+| `components/` | Shared UI components |
-| `figures/` | Plotly chart factories | choropleth, bar_charts, scatter, radar, time_series |
+| `figures/toronto/` | Toronto chart factories |
-| `toronto/` | Toronto data logic | parsers/, loaders/, schemas/, models/ |
+| `toronto/` | Toronto data logic (parsers, loaders, schemas, models) |
-| `content/blog/` | Markdown blog articles | Processed by `utils/markdown_loader.py` |
-| `notebooks/` | Data documentation | 5 domains: overview, housing, safety, demographics, amenities |
 
-**Key URLs:** `/` (home), `/toronto` (dashboard), `/blog` (listing), `/blog/{slug}` (articles)
+**Key URLs:** `/` (home), `/toronto` (dashboard), `/blog` (listing), `/blog/{slug}` (articles), `/health` (status)
+
+### 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`)
 
 ---
 
@@ -144,44 +159,31 @@ class LoadError(PortfolioError):
 | 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**:
+**Notes**: SQLAlchemy 2.0 + Pydantic 2.0 only. Docker Compose V2 format (no `version` field).
-- SQLAlchemy 2.0 + Pydantic 2.0 only (never mix 1.x APIs)
-- PostGIS extension required in database
-- Docker Compose V2 format (no `version` field)
-- **Multi-architecture support**: `make docker-up` auto-detects CPU architecture and uses the appropriate PostGIS image (x86_64: `postgis/postgis`, ARM64: `imresamu/postgis`)
 
 ---
 
 ## Data Model Overview
 
-### Geographic Reality (Toronto Housing)
+### Database Schemas
 
-```
+| Schema | Purpose |
-City Neighbourhoods (158) - Primary geographic unit for analysis
+|--------|---------|
-CMHC Zones (~20)          - Rental data (Census Tract aligned)
+| `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
+### dbt Project: `portfolio`
-
-| 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
 
 | 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 |
@@ -190,13 +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 |
 
 ---
 
@@ -216,139 +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 |
+| Project reference | `docs/PROJECT_REFERENCE.md` | Architecture decisions |
-| Developer guide | `docs/CONTRIBUTING.md` | How to add pages, blog posts, tabs |
+| 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 staging/production |
+| Deployment runbook | `docs/runbooks/deployment.md` | Deploying to environments |
-| Dashboard runbook | `docs/runbooks/adding-dashboard.md` | Adding new data dashboards |
 
 ---
 
-## 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-plan` | New sprint/feature | Architecture analysis + Gitea issue creation |
-| `/projman:sprint-start` | Beginning implementation work | Load lessons learned (Wiki.js or local), start execution |
+| `/projman:sprint-start` | Begin implementation | Load lessons learned, start execution |
-| `/projman:sprint-status` | Check progress | Review blockers and completion status |
+| `/projman:sprint-status` | Check progress | Review blockers and completion |
-| `/projman:sprint-close` | Sprint completion | Capture lessons learned (Wiki.js or local backup) |
+| `/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
+### Data Platform: data-platform
-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
 
-### Gitea Repository
+Use for dbt, PostgreSQL, and PostGIS operations.
 
-- **Repo**: `personal-projects/personal-portfolio`
+| Skill | Purpose |
-- **Host**: `gitea.hotserv.cloud`
+|-------|---------|
-- **SSH**: `ssh://git@hotserv.tailc9b278.ts.net:2222/personal-projects/personal-portfolio.git`
+| `/data-platform:data-review` | Audit data integrity, schema validity, dbt compliance |
-- **Labels**: 18 repository-level labels configured (Type, Priority, Complexity, Effort)
+| `/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**:
+**MCP tools available:** `pg_connect`, `pg_query`, `pg_tables`, `pg_columns`, `pg_schemas`, `st_*` (PostGIS), `dbt_*` operations.
-- `list_issues`, `get_issue`, `create_issue`, `update_issue`, `add_comment`
-- `get_labels`, `suggest_labels`
 
-**Wiki.js**:
+### Visualization: viz-platform
-- `search_lessons`, `create_lesson`, `search_pages`, `get_page`
 
-### 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:**
+**When to use:** Dashboard development, new visualizations, component prop lookup.
-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
-
-**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
-
-**Migration:** Once Wiki.js is configured, lessons will be migrated there for better searchability.
-
-### Issue Structure
-
-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)
-
----
-
-## Other Available Plugins
 
 ### Code Quality: code-sentinel
 
 Use for security scanning and refactoring analysis.
 
-| Command | Purpose |
+| 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 |
 
-**When to use:** Before major releases, after adding authentication/data handling code, periodic audits.
+**When to use:** Before major releases, after adding auth/data handling code, periodic audits.
 
 ### Documentation: doc-guardian
 
 Use for documentation drift detection and synchronization.
 
-| Command | Purpose |
+| 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, when docs feel stale.
+**When to use:** After significant code changes, before releases.
 
 ### Pull Requests: pr-review
 
 Use for comprehensive PR review with multiple analysis perspectives.
 
-| Command | Purpose |
+| Skill | Purpose |
-|---------|---------|
+|-------|---------|
-| `/pr-review:initial-setup` | Configure PR review for this project |
+| `/pr-review:initial-setup` | Configure PR review for project |
-| `/pr-review:project-init` | Quick project-level setup |
+| 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 git operations assistance.
+Use for standardized git operations.
 
-**When to use:** Complex merge scenarios, branch management questions.
+| 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*

Makefile

@@ -1,11 +1,12 @@
-.PHONY: setup docker-up docker-down db-init load-data seed-data run test dbt-run dbt-test lint format ci deploy clean help logs run-detached etl-toronto
+.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
+VENV := .venv
-PIP := pip
+PYTHON := $(VENV)/bin/python3
+PIP := $(VENV)/bin/pip
 DOCKER_COMPOSE := docker compose
 
 # Architecture detection for Docker images
@@ -79,16 +80,23 @@ db-reset: ## Drop and recreate database (DESTRUCTIVE)
 	@sleep 3
 	$(MAKE) db-init
 
-load-data: ## Load Toronto data from APIs, seed dev data, 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 development data...$(NC)"
+	@echo "$(GREEN)Seeding Toronto development data...$(NC)"
 	$(PYTHON) scripts/data/seed_amenity_data.py
 
-load-data-only: ## Load Toronto data without running dbt or seeding
+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
@@ -119,15 +127,15 @@ test-cov: ## Run pytest with coverage
 
 dbt-run: ## Run dbt models
 	@echo "$(GREEN)Running dbt models...$(NC)"
-	cd dbt && dbt run --profiles-dir .
+	@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 --profiles-dir .
+	@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 --profiles-dir . && dbt docs serve --profiles-dir .
+	@set -a && . ./.env && set +a && cd dbt && dbt docs generate --profiles-dir . && dbt docs serve --profiles-dir .
 
 # =============================================================================
 # Code Quality

README.md

@@ -115,28 +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
+│   ├── shared/            # Cross-domain dimensions
-│   ├── intermediate/      # Business logic
+│   ├── staging/toronto/   # Toronto staging models
-│   └── marts/             # Analytical tables
+│   ├── intermediate/toronto/ # Toronto intermediate models
+│   └── marts/toronto/     # Toronto analytical tables
 
-notebooks/                 # Data documentation (15 notebooks)
+notebooks/
-├── overview/              # Overview tab visualizations
+└── toronto/               # Toronto documentation (15 notebooks)
-├── housing/               # Housing tab visualizations
+    ├── overview/          # Overview tab visualizations
-├── safety/                # Safety tab visualizations
+    ├── housing/           # Housing tab visualizations
-├── demographics/          # Demographics tab visualizations
+    ├── safety/            # Safety tab visualizations
-└── amenities/             # Amenities 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'
+name: 'portfolio'
-version: '1.0.0'
 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
+      toronto:
-      +schema: staging
+        +materialized: view
+        +schema: stg_toronto
     intermediate:
-      +materialized: view
+      toronto:
-      +schema: intermediate
+        +materialized: view
+        +schema: int_toronto
     marts:
-      +materialized: table
+      toronto:
-      +schema: marts
+        +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_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 = '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 = '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 = '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 = '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 = '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 = '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

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 = '2bed' 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 = '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 = '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 = '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/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

@@ -128,7 +128,8 @@ final as (
         -- Component scores (0-100)
         round(safety_score::numeric, 1) as safety_score,
         round(affordability_score::numeric, 1) as affordability_score,
-        -- Amenity score not available at this level, use placeholder
+        -- 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%)

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
+  - name: toronto
-    description: "Toronto housing data loaded from CMHC and City of Toronto sources"
+    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:
+        data_tests:
-          - not_null
-
-  - name: stg_dimensions__time
-    description: "Staged time dimension"
-    columns:
-      - name: date_key
-        description: "Date dimension key (YYYYMMDD)"
-        tests:
-          - unique
           - 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

@@ -6,8 +6,8 @@ with source as (
     select
         f.*,
         t.year as survey_year
-    from {{ source('toronto_housing', 'fact_rentals') }} f
+    from {{ source('toronto', 'fact_rentals') }} f
-    join {{ source('toronto_housing', 'dim_time') }} t on f.date_key = t.date_key
+    join {{ source('shared', 'dim_time') }} t on f.date_key = t.date_key
 ),
 
 staged as (

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:

docs/CONTRIBUTING.md

@@ -290,7 +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
 
 
@@ -339,13 +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
+# figures/toronto/your_chart.py
-"""Your chart type factory."""
+"""Your chart type factory for Toronto dashboard."""
 
 import plotly.express as px
 import plotly.graph_objects as go
@@ -382,7 +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__ = [
@@ -391,6 +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

@@ -116,18 +116,40 @@ erDiagram
 
 ## Schema Layers
 
-### Raw Schema
+### Database Schemas
 
-Raw data is loaded directly from external sources without transformation:
+| 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 |
 |-------|--------|-------------|
-| `raw.neighbourhoods` | City of Toronto API | GeoJSON neighbourhood boundaries |
+| `dim_neighbourhood` | City of Toronto API | 158 neighbourhood boundaries |
-| `raw.census_profiles` | City of Toronto API | Census profile data |
+| `dim_cmhc_zone` | CMHC | ~20 rental market zones |
-| `raw.crime_data` | Toronto Police API | Crime statistics by neighbourhood |
+| `dim_policy_event` | Manual | Policy events for annotation |
-| `raw.cmhc_rentals` | CMHC Data Files | Rental market survey data |
+| `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 |
 
-### Staging Schema (dbt)
+### 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:
 
@@ -142,7 +164,7 @@ Staging models provide 1:1 cleaned representations of source data:
 | `stg_dimensions__cmhc_zones` | raw.cmhc_zones | CMHC zone boundaries |
 | `stg_cmhc__zone_crosswalk` | raw.crosswalk | Zone-neighbourhood mapping |
 
-### Marts Schema (dbt)
+### Marts Schema - mart_toronto (dbt)
 
 Analytical tables ready for dashboard consumption:
 

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
@@ -96,11 +97,21 @@ portfolio_app/
 │   ├── 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
 ```
 
 ---
@@ -144,10 +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` |
@@ -248,7 +269,7 @@ LOG_LEVEL=INFO
 | `db-init` | Initialize database schema |
 | `db-reset` | Drop and recreate database (DESTRUCTIVE) |
 | `load-data` | Load Toronto data from APIs, seed dev data |
-| `load-data-only` | Load Toronto data without dbt or seeding |
+| `load-toronto-only` | Load Toronto data without dbt or seeding |
 | `seed-data` | Seed sample development data |
 | `run` | Start Dash dev server |
 | `test` | Run pytest |

docs/runbooks/adding-dashboard.md

@@ -10,7 +10,9 @@ This runbook describes how to add a new data dashboard to the portfolio applicat
 
 ## Directory Structure
 
-Create the following structure under `portfolio_app/`:
+Create the following structure:
+
+### Application Code (`portfolio_app/`)
 
 ```
 portfolio_app/
@@ -33,8 +35,40 @@ portfolio_app/
 │   │   └── __init__.py
 │   ├── schemas/              # Pydantic models
 │   │   └── __init__.py
-│   └── models/               # SQLAlchemy ORM
+│   └── 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
@@ -47,24 +81,55 @@ portfolio_app/
 - [ ] Create loaders in `{dashboard_name}/loaders/`
 - [ ] Add database migrations if needed
 
-### 2. dbt Models
+### 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/stg_{source}__{entity}.sql` - Raw data cleaning
+- [ ] `staging/{dashboard_name}/_sources.yml` - Source definitions pointing to `raw_{dashboard_name}` schema
-- [ ] `intermediate/int_{domain}__{transform}.sql` - Business logic
+- [ ] `staging/{dashboard_name}/stg_{source}__{entity}.sql` - Raw data cleaning
-- [ ] `marts/mart_{domain}.sql` - Final analytical tables
+- [ ] `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}`
 
-### 3. Visualization Layer
+### 4. Visualization Layer
 
-- [ ] Create figure factories in `figures/` (or reuse existing)
+- [ ] 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`)

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
+└── toronto/               # Toronto Neighbourhood Dashboard
-├── housing/               # Housing tab visualizations
+    ├── overview/          # Overview tab visualizations
-├── safety/                # Safety tab visualizations
+    ├── housing/           # Housing tab visualizations
-├── demographics/          # Demographics tab visualizations
+    ├── safety/            # Safety tab visualizations
-└── amenities/             # Amenities tab visualizations
+    ├── demographics/      # Demographics tab visualizations
+    └── amenities/         # Amenities tab visualizations
 ```
 
 ## Notebook Template

notebooks/amenities/amenity_radar.ipynb

@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Amenity Radar Chart\n",
-    "\n",
-    "Spider/radar chart comparing amenity categories for selected neighbourhoods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Data Reference\n",
-    "\n",
-    "### Source Tables\n",
-    "\n",
-    "| Table | Grain | Key Columns |\n",
-    "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_amenities` | neighbourhood × year | parks_index, schools_index, transit_index |\n",
-    "\n",
-    "### SQL Query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "import pandas as pd\nfrom sqlalchemy import create_engine\nfrom dotenv import load_dotenv\nimport os\n\n# Load .env from project root\nload_dotenv('../../.env')\n\nengine = create_engine(os.environ['DATABASE_URL'])\n\nquery = \"\"\"\nSELECT\n    neighbourhood_name,\n    parks_index,\n    schools_index,\n    transit_index,\n    amenity_index,\n    amenity_tier\nFROM public_marts.mart_neighbourhood_amenities\nWHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\nORDER BY amenity_index DESC\n\"\"\"\n\ndf = pd.read_sql(query, engine)\nprint(f\"Loaded {len(df)} neighbourhoods\")"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transformation Steps\n",
-    "\n",
-    "1. Select top 5 and bottom 5 neighbourhoods by amenity index\n",
-    "2. Reshape for radar chart format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select representative neighbourhoods\n",
-    "top_5 = df.head(5)\n",
-    "bottom_5 = df.tail(5)\n",
-    "\n",
-    "# Prepare radar data\n",
-    "categories = ['Parks', 'Schools', 'Transit']\n",
-    "index_columns = ['parks_index', 'schools_index', 'transit_index']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sample Output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Top 5 Amenity-Rich Neighbourhoods:\")\n",
-    "display(top_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])\n",
-    "print(\"\\nBottom 5 Underserved Neighbourhoods:\")\n",
-    "display(bottom_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Data Visualization\n",
-    "\n",
-    "### Figure Factory\n",
-    "\n",
-    "Uses `create_radar` from `portfolio_app.figures.radar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "import sys\nsys.path.insert(0, '../..')\n\nfrom portfolio_app.figures.radar import create_comparison_radar\n\n# Compare top neighbourhood vs city average (100)\ntop_hood = top_5.iloc[0]\nmetrics = ['parks_index', 'schools_index', 'transit_index']\n\nfig = 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\nfig.show()"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Index Interpretation\n",
-    "\n",
-    "| Value | Meaning |\n",
-    "|-------|--------|\n",
-    "| < 100 | Below city average |\n",
-    "| = 100 | City average |\n",
-    "| > 100 | Above city average |"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

notebooks/toronto/amenities/amenity_index_choropleth.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_amenities` | neighbourhood \u00d7 year | amenity_index, total_amenities_per_1000, amenity_tier, geometry |\n",
+    "| `mart_neighbourhood_amenities` | neighbourhood × year | amenity_index, total_amenities_per_1000, amenity_tier, geometry |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -79,17 +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",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    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\")"
    ]
   },
   {
@@ -105,7 +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)"
    ]
   },
   {
@@ -116,7 +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`."
    ]
   },
   {
@@ -126,18 +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",
+    "    location_key=\"neighbourhood_id\",\n",
-    "    color_column='total_amenities_per_1000',\n",
+    "    color_column=\"total_amenities_per_1000\",\n",
-    "    hover_data=['neighbourhood_name', 'amenity_index', 'parks_per_1000', 'schools_per_1000'],\n",
+    "    hover_data=[\n",
-    "    color_scale='Greens',\n",
+    "        \"neighbourhood_name\",\n",
-    "    title='Toronto Amenities per 1,000 Population',\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

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amenity Radar Chart\n",
+    "\n",
+    "Spider/radar chart comparing amenity categories for selected neighbourhoods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Reference\n",
+    "\n",
+    "### Source Tables\n",
+    "\n",
+    "| Table | Grain | Key Columns |\n",
+    "|-------|-------|-------------|\n",
+    "| `mart_neighbourhood_amenities` | neighbourhood × year | parks_index, schools_index, transit_index |\n",
+    "\n",
+    "### SQL Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\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",
+    "    neighbourhood_name,\n",
+    "    parks_index,\n",
+    "    schools_index,\n",
+    "    transit_index,\n",
+    "    amenity_index,\n",
+    "    amenity_tier\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",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} neighbourhoods\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Select top 5 and bottom 5 neighbourhoods by amenity index\n",
+    "2. Reshape for radar chart format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select representative neighbourhoods\n",
+    "top_5 = df.head(5)\n",
+    "bottom_5 = df.tail(5)\n",
+    "\n",
+    "# Prepare radar data\n",
+    "categories = [\"Parks\", \"Schools\", \"Transit\"]\n",
+    "index_columns = [\"parks_index\", \"schools_index\", \"transit_index\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Top 5 Amenity-Rich Neighbourhoods:\")\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(\n",
+    "    bottom_5[\n",
+    "        [\n",
+    "            \"neighbourhood_name\",\n",
+    "            \"parks_index\",\n",
+    "            \"schools_index\",\n",
+    "            \"transit_index\",\n",
+    "            \"amenity_index\",\n",
+    "        ]\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_radar` from `portfolio_app.figures.toronto.radar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\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",
+    "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",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Index Interpretation\n",
+    "\n",
+    "| Value | Meaning |\n",
+    "|-------|--------|\n",
+    "| < 100 | Below city average |\n",
+    "| = 100 | City average |\n",
+    "| > 100 | Above city average |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/toronto/amenities/transit_accessibility_bar.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_amenities` | neighbourhood \u00d7 year | transit_per_1000, transit_index, transit_count |\n",
+    "| `mart_neighbourhood_amenities` | neighbourhood × year | transit_per_1000, transit_index, transit_count |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -74,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = df.head(20).to_dict('records')"
+    "data = df.head(20).to_dict(\"records\")"
    ]
   },
   {
@@ -90,7 +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",
+    ")"
    ]
   },
   {
@@ -101,7 +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`."
    ]
   },
   {
@@ -111,17 +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",
+    "    name_column=\"neighbourhood_name\",\n",
-    "    value_column='transit_per_1000',\n",
+    "    value_column=\"transit_per_1000\",\n",
-    "    title='Top 20 Neighbourhoods by Transit Accessibility',\n",
+    "    title=\"Top 20 Neighbourhoods by Transit Accessibility\",\n",
-    "    color='#00BCD4',\n",
+    "    color=\"#00BCD4\",\n",
-    "    value_format='.2f',\n",
+    "    value_format=\".2f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -140,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | median_age, age_index, city_avg_age |\n",
+    "| `mart_neighbourhood_demographics` | neighbourhood × year | median_age, age_index, city_avg_age |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -76,13 +77,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "city_avg = df['city_avg_age'].iloc[0]\n",
+    "city_avg = df[\"city_avg_age\"].iloc[0]\n",
-    "df['age_category'] = df['median_age'].apply(\n",
+    "df[\"age_category\"] = df[\"median_age\"].apply(\n",
-    "    lambda x: 'Younger' if x < city_avg else 'Older'\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\")"
    ]
   },
   {
@@ -100,9 +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",
+    ")"
    ]
   },
   {
@@ -113,7 +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`."
    ]
   },
   {
@@ -123,20 +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",
+    "    name_column=\"neighbourhood_name\",\n",
-    "    value_column='median_age',\n",
+    "    value_column=\"median_age\",\n",
-    "    title='Youngest & Oldest Neighbourhoods (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_top=\"#FF9800\",  # Orange for older\n",
-    "    color_bottom='#2196F3',  # Blue for younger\n",
+    "    color_bottom=\"#2196F3\",  # Blue for younger\n",
-    "    value_format='.1f',\n",
+    "    value_format=\".1f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -157,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | median_household_income, income_index, income_quintile, geometry |\n",
+    "| `mart_neighbourhood_demographics` | neighbourhood × year | median_household_income, income_index, income_quintile, geometry |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,19 +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",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    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\")"
    ]
   },
   {
@@ -105,7 +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)"
    ]
   },
   {
@@ -116,7 +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`."
    ]
   },
   {
@@ -126,18 +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",
+    "    location_key=\"neighbourhood_id\",\n",
-    "    color_column='median_household_income',\n",
+    "    color_column=\"median_household_income\",\n",
-    "    hover_data=['neighbourhood_name', 'income_index', 'income_quintile'],\n",
+    "    hover_data=[\"neighbourhood_name\", \"income_index\", \"income_quintile\"],\n",
-    "    color_scale='Viridis',\n",
+    "    color_scale=\"Viridis\",\n",
-    "    title='Toronto Median Household Income by Neighbourhood',\n",
+    "    title=\"Toronto Median Household Income by Neighbourhood\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",
@@ -157,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | population_density, population, land_area_sqkm |\n",
+    "| `mart_neighbourhood_demographics` | neighbourhood × year | population_density, population, land_area_sqkm |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -74,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = df.head(20).to_dict('records')"
+    "data = df.head(20).to_dict(\"records\")"
    ]
   },
   {
@@ -90,7 +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",
+    ")"
    ]
   },
   {
@@ -101,7 +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`."
    ]
   },
   {
@@ -111,17 +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",
+    "    name_column=\"neighbourhood_name\",\n",
-    "    value_column='population_density',\n",
+    "    value_column=\"population_density\",\n",
-    "    title='Top 20 Most Dense Neighbourhoods',\n",
+    "    title=\"Top 20 Most Dense Neighbourhoods\",\n",
-    "    color='#9C27B0',\n",
+    "    color=\"#9C27B0\",\n",
-    "    value_format=',.0f',\n",
+    "    value_format=\",.0f\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -140,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,17 +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",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    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\")"
    ]
   },
   {
@@ -103,7 +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)"
    ]
   },
   {
@@ -114,7 +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",
@@ -128,18 +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",
+    "    location_key=\"neighbourhood_id\",\n",
-    "    color_column='affordability_index',\n",
+    "    color_column=\"affordability_index\",\n",
-    "    hover_data=['neighbourhood_name', 'rent_to_income_pct', 'avg_rent_2bed'],\n",
+    "    hover_data=[\"neighbourhood_name\", \"rent_to_income_pct\", \"avg_rent_2bed\"],\n",
-    "    color_scale='RdYlGn_r',  # Reversed: lower index (affordable) = green\n",
+    "    color_scale=\"RdYlGn_r\",  # Reversed: lower index (affordable) = green\n",
-    "    title='Toronto Housing Affordability Index',\n",
+    "    title=\"Toronto Housing Affordability Index\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/housing/rent_trend_line.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | year, avg_rent_2bed, rent_yoy_change_pct |\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | year, avg_rent_2bed, rent_yoy_change_pct |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,23 +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",
+    "    id_vars=[\"year\", \"date\"],\n",
-    "    value_vars=['avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed'],\n",
+    "    value_vars=[\"avg_rent_bachelor\", \"avg_rent_1bed\", \"avg_rent_2bed\", \"avg_rent_3bed\"],\n",
-    "    var_name='bedroom_type',\n",
+    "    var_name=\"bedroom_type\",\n",
-    "    value_name='avg_rent'\n",
+    "    value_name=\"avg_rent\",\n",
     ")\n",
     "\n",
     "# Clean labels\n",
-    "df_melted['bedroom_type'] = df_melted['bedroom_type'].map({\n",
+    "df_melted[\"bedroom_type\"] = df_melted[\"bedroom_type\"].map(\n",
-    "    'avg_rent_bachelor': 'Bachelor',\n",
+    "    {\n",
-    "    'avg_rent_1bed': '1 Bedroom',\n",
+    "        \"avg_rent_bachelor\": \"Bachelor\",\n",
-    "    'avg_rent_2bed': '2 Bedroom',\n",
+    "        \"avg_rent_1bed\": \"1 Bedroom\",\n",
-    "    'avg_rent_3bed': '3 Bedroom'\n",
+    "        \"avg_rent_2bed\": \"2 Bedroom\",\n",
-    "})"
+    "        \"avg_rent_3bed\": \"3 Bedroom\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -109,7 +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",
+    "]"
    ]
   },
   {
@@ -120,7 +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",
@@ -135,18 +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",
+    "    date_column=\"date\",\n",
-    "    price_column='avg_rent',\n",
+    "    price_column=\"avg_rent\",\n",
-    "    group_column='bedroom_type',\n",
+    "    group_column=\"bedroom_type\",\n",
-    "    title='Toronto Average Rent Trend (5 Years)',\n",
+    "    title=\"Toronto Average Rent Trend (5 Years)\",\n",
     ")\n",
     "\n",
     "fig.show()"
@@ -167,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,18 +78,17 @@
    "source": [
     "# Prepare for stacked bar\n",
     "df_stacked = df.melt(\n",
-    "    id_vars=['neighbourhood_name', 'income_quintile'],\n",
+    "    id_vars=[\"neighbourhood_name\", \"income_quintile\"],\n",
-    "    value_vars=['pct_owner_occupied', 'pct_renter_occupied'],\n",
+    "    value_vars=[\"pct_owner_occupied\", \"pct_renter_occupied\"],\n",
-    "    var_name='tenure_type',\n",
+    "    var_name=\"tenure_type\",\n",
-    "    value_name='percentage'\n",
+    "    value_name=\"percentage\",\n",
     ")\n",
     "\n",
-    "df_stacked['tenure_type'] = df_stacked['tenure_type'].map({\n",
+    "df_stacked[\"tenure_type\"] = df_stacked[\"tenure_type\"].map(\n",
-    "    'pct_owner_occupied': 'Owner',\n",
+    "    {\"pct_owner_occupied\": \"Owner\", \"pct_renter_occupied\": \"Renter\"}\n",
-    "    'pct_renter_occupied': 'Renter'\n",
+    ")\n",
-    "})\n",
     "\n",
-    "data = df_stacked.to_dict('records')"
+    "data = df_stacked.to_dict(\"records\")"
    ]
   },
   {
@@ -105,7 +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)"
    ]
   },
   {
@@ -116,7 +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",
@@ -132,21 +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",
+    "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",
+    "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",
+    "    x_column=\"neighbourhood_name\",\n",
-    "    value_column='percentage',\n",
+    "    value_column=\"percentage\",\n",
-    "    category_column='tenure_type',\n",
+    "    category_column=\"tenure_type\",\n",
-    "    title='Housing Tenure Mix - Top 20 Renter Neighbourhoods',\n",
+    "    title=\"Housing Tenure Mix - Top 20 Renter Neighbourhoods\",\n",
-    "    color_map={'Owner': '#4CAF50', 'Renter': '#2196F3'},\n",
+    "    color_map={\"Owner\": \"#4CAF50\", \"Renter\": \"#2196F3\"},\n",
     "    show_percentages=True,\n",
     ")\n",
     "\n",
@@ -172,7 +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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_overview` | neighbourhood \u00d7 year | neighbourhood_name, median_household_income, safety_score, population |\n",
+    "| `mart_neighbourhood_overview` | neighbourhood × year | neighbourhood_name, median_household_income, safety_score, population |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,10 +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\")"
    ]
   },
   {
@@ -96,7 +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)"
    ]
   },
   {
@@ -107,7 +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 $K)\n",
@@ -124,19 +132,20 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../..')\n",
     "\n",
-    "from portfolio_app.figures.scatter import create_scatter_figure\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.scatter import create_scatter_figure\n",
     "\n",
     "fig = create_scatter_figure(\n",
     "    data=data,\n",
-    "    x_column='income_thousands',\n",
+    "    x_column=\"income_thousands\",\n",
-    "    y_column='safety_score',\n",
+    "    y_column=\"safety_score\",\n",
-    "    name_column='neighbourhood_name',\n",
+    "    name_column=\"neighbourhood_name\",\n",
-    "    size_column='population',\n",
+    "    size_column=\"population\",\n",
-    "    title='Income vs Safety by Neighbourhood',\n",
+    "    title=\"Income vs Safety by Neighbourhood\",\n",
-    "    x_title='Median Household Income ($K)',\n",
+    "    x_title=\"Median Household Income ($K)\",\n",
-    "    y_title='Safety Score (0-100)',\n",
+    "    y_title=\"Safety Score (0-100)\",\n",
     "    trendline=True,\n",
     ")\n",
     "\n",
@@ -166,7 +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

@@ -29,7 +29,38 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "import pandas as pd\nfrom sqlalchemy import create_engine\nfrom dotenv import load_dotenv\nimport os\n\n# Load .env from project root\nload_dotenv('../../.env')\n\nengine = create_engine(os.environ['DATABASE_URL'])\n\nquery = \"\"\"\nSELECT\n    neighbourhood_id,\n    neighbourhood_name,\n    geometry,\n    year,\n    livability_score,\n    safety_score,\n    affordability_score,\n    amenity_score,\n    population,\n    median_household_income\nFROM public_marts.mart_neighbourhood_overview\nWHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\nORDER BY livability_score DESC\n\"\"\"\n\ndf = pd.read_sql(query, engine)\nprint(f\"Loaded {len(df)} neighbourhoods\")"
+   "source": [
+    "import os\n",
+    "\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",
+    "    neighbourhood_id,\n",
+    "    neighbourhood_name,\n",
+    "    geometry,\n",
+    "    year,\n",
+    "    livability_score,\n",
+    "    safety_score,\n",
+    "    affordability_score,\n",
+    "    amenity_score,\n",
+    "    population,\n",
+    "    median_household_income\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",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} neighbourhoods\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -49,21 +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",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    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\")"
    ]
   },
   {
@@ -79,7 +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)"
    ]
   },
   {
@@ -90,7 +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",
@@ -107,18 +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",
+    "    location_key=\"neighbourhood_id\",\n",
-    "    color_column='livability_score',\n",
+    "    color_column=\"livability_score\",\n",
-    "    hover_data=['neighbourhood_name', 'safety_score', 'affordability_score', 'amenity_score'],\n",
+    "    hover_data=[\n",
-    "    color_scale='RdYlGn',\n",
+    "        \"neighbourhood_name\",\n",
-    "    title='Toronto Neighbourhood Livability Score',\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

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_overview` | neighbourhood \u00d7 year | neighbourhood_name, livability_score |\n",
+    "| `mart_neighbourhood_overview` | neighbourhood × year | neighbourhood_name, livability_score |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -76,7 +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\")"
    ]
   },
   {
@@ -106,7 +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",
@@ -123,20 +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",
+    "    name_column=\"neighbourhood_name\",\n",
-    "    value_column='livability_score',\n",
+    "    value_column=\"livability_score\",\n",
-    "    title='Top & Bottom 10 Neighbourhoods by Livability',\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_top=\"#4CAF50\",  # Green for top performers\n",
-    "    color_bottom='#F44336', # Red for bottom performers\n",
+    "    color_bottom=\"#F44336\",  # Red for bottom performers\n",
-    "    value_format='.1f',\n",
+    "    value_format=\".1f\",\n",
     ")\n",
     "\n",
     "fig.show()"

notebooks/toronto/safety/crime_breakdown_bar.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\n",
+    "| `mart_neighbourhood_safety` | neighbourhood × year | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -79,17 +80,25 @@
    "outputs": [],
    "source": [
     "df_melted = df.melt(\n",
-    "    id_vars=['neighbourhood_name', 'total_incidents'],\n",
+    "    id_vars=[\"neighbourhood_name\", \"total_incidents\"],\n",
-    "    value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n",
+    "    value_vars=[\n",
-    "                'robbery_count', 'theft_over_count', 'homicide_count'],\n",
+    "        \"assault_count\",\n",
-    "    var_name='crime_type',\n",
+    "        \"auto_theft_count\",\n",
-    "    value_name='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\")"
    ]
   },
   {
@@ -105,7 +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)"
    ]
   },
   {
@@ -116,7 +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`."
    ]
   },
   {
@@ -126,23 +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",
+    "    x_column=\"neighbourhood_name\",\n",
-    "    value_column='count',\n",
+    "    value_column=\"count\",\n",
-    "    category_column='crime_type',\n",
+    "    category_column=\"crime_type\",\n",
-    "    title='Crime Type Breakdown - Top 15 Neighbourhoods',\n",
+    "    title=\"Crime Type Breakdown - Top 15 Neighbourhoods\",\n",
     "    color_map={\n",
-    "        'Assault': '#d62728',\n",
+    "        \"Assault\": \"#d62728\",\n",
-    "        'Auto Theft': '#ff7f0e',\n",
+    "        \"Auto Theft\": \"#ff7f0e\",\n",
-    "        'Break Enter': '#9467bd',\n",
+    "        \"Break Enter\": \"#9467bd\",\n",
-    "        'Robbery': '#8c564b',\n",
+    "        \"Robbery\": \"#8c564b\",\n",
-    "        'Theft Over': '#e377c2',\n",
+    "        \"Theft Over\": \"#e377c2\",\n",
-    "        'Homicide': '#1f77b4'\n",
+    "        \"Homicide\": \"#1f77b4\",\n",
     "    },\n",
     ")\n",
     "\n",

notebooks/toronto/safety/crime_rate_choropleth.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | crime_rate_per_100k, crime_index, safety_tier, geometry |\n",
+    "| `mart_neighbourhood_safety` | neighbourhood × year | crime_rate_per_100k, crime_index, safety_tier, geometry |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -77,17 +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",
+    "    df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
-    "    geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
-    "    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\")"
    ]
   },
   {
@@ -103,7 +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)"
    ]
   },
   {
@@ -114,7 +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",
@@ -128,18 +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",
+    "    location_key=\"neighbourhood_id\",\n",
-    "    color_column='crime_rate_per_100k',\n",
+    "    color_column=\"crime_rate_per_100k\",\n",
-    "    hover_data=['neighbourhood_name', 'crime_index', 'total_incidents'],\n",
+    "    hover_data=[\"neighbourhood_name\", \"crime_index\", \"total_incidents\"],\n",
-    "    color_scale='RdYlGn_r',\n",
+    "    color_scale=\"RdYlGn_r\",\n",
-    "    title='Toronto Crime Rate per 100,000 Population',\n",
+    "    title=\"Toronto Crime Rate per 100,000 Population\",\n",
     "    zoom=10,\n",
     ")\n",
     "\n",

notebooks/toronto/safety/crime_trend_line.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "| Table | Grain | Key Columns |\n",
     "|-------|-------|-------------|\n",
-    "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | year, crime_rate_per_100k, crime_yoy_change_pct |\n",
+    "| `mart_neighbourhood_safety` | neighbourhood × year | year, crime_rate_per_100k, crime_yoy_change_pct |\n",
     "\n",
     "### SQL Query"
    ]
@@ -30,15 +30,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sqlalchemy import create_engine\n",
-    "from dotenv import load_dotenv\n",
     "import os\n",
     "\n",
-    "# Load .env from project root\n",
+    "import pandas as pd\n",
-    "load_dotenv('../../.env')\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "engine = create_engine(os.environ['DATABASE_URL'])\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",
@@ -76,21 +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",
+    "    id_vars=[\"year\", \"date\"],\n",
-    "    value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n",
+    "    value_vars=[\"avg_assault_rate\", \"avg_auto_theft_rate\", \"avg_break_enter_rate\"],\n",
-    "    var_name='crime_type',\n",
+    "    var_name=\"crime_type\",\n",
-    "    value_name='rate_per_100k'\n",
+    "    value_name=\"rate_per_100k\",\n",
     ")\n",
     "\n",
-    "df_melted['crime_type'] = df_melted['crime_type'].map({\n",
+    "df_melted[\"crime_type\"] = df_melted[\"crime_type\"].map(\n",
-    "    'avg_assault_rate': 'Assault',\n",
+    "    {\n",
-    "    'avg_auto_theft_rate': 'Auto Theft',\n",
+    "        \"avg_assault_rate\": \"Assault\",\n",
-    "    'avg_break_enter_rate': 'Break & Enter'\n",
+    "        \"avg_auto_theft_rate\": \"Auto Theft\",\n",
-    "})"
+    "        \"avg_break_enter_rate\": \"Break & Enter\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -106,7 +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\"]]"
    ]
   },
   {
@@ -127,22 +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",
+    "    date_column=\"date\",\n",
-    "    price_column='rate_per_100k',\n",
+    "    price_column=\"rate_per_100k\",\n",
-    "    group_column='crime_type',\n",
+    "    group_column=\"crime_type\",\n",
-    "    title='Toronto Crime Trends by Type (5 Years)',\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()"
    ]
@@ -161,15 +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",
+    "    date_column=\"date\",\n",
-    "    price_column='total_rate',\n",
+    "    price_column=\"total_rate\",\n",
-    "    title='Toronto Overall Crime Rate Trend',\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/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 (
+Figure factories are organized by dashboard domain:
-    create_horizontal_bar,
+- toronto/  : Toronto Neighbourhood Dashboard figures
-    create_ranking_bar,
+
-    create_stacked_bar,
+Usage:
-)
+    from portfolio_app.figures.toronto import create_choropleth_figure
-from .choropleth import (
+    from portfolio_app.figures.toronto import create_ranking_bar
-    create_choropleth_figure,
+"""
-    create_zone_map,
+
-)
+from . import toronto
-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
+    "toronto",
-    "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/__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_top: str = COLOR_POSITIVE,
-    color_bottom: str = "#F44336",
+    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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        xaxis={"gridcolor": GRID_COLOR, "title": None},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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)",
+            paper_bgcolor=PAPER_BG,
-            plot_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor=PLOT_BG,
-            font_color="#c9c9c9",
+            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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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 = [
+        colors = CHART_PALETTE
-            "#2196F3",
-            "#4CAF50",
-            "#FF9800",
-            "#E91E63",
-            "#9C27B0",
-            "#00BCD4",
-            "#FFC107",
-            "#795548",
-        ]
 
     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)",
+        paper_bgcolor=PAPER_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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 = [
+        colors = CHART_PALETTE
-            "#2196F3",
-            "#4CAF50",
-            "#FF9800",
-            "#E91E63",
-            "#9C27B0",
-            "#00BCD4",
-        ]
 
     fig = go.Figure()
 
@@ -78,19 +79,19 @@ def create_radar_figure(
         polar={
             "radialaxis": {
                 "visible": True,
-                "gridcolor": "rgba(128,128,128,0.3)",
+                "gridcolor": GRID_COLOR_DARK,
-                "linecolor": "rgba(128,128,128,0.3)",
+                "linecolor": GRID_COLOR_DARK,
-                "tickfont": {"color": "#c9c9c9"},
+                "tickfont": {"color": TEXT_PRIMARY},
             },
             "angularaxis": {
-                "gridcolor": "rgba(128,128,128,0.3)",
+                "gridcolor": GRID_COLOR_DARK,
-                "linecolor": "rgba(128,128,128,0.3)",
+                "linecolor": GRID_COLOR_DARK,
-                "tickfont": {"color": "#c9c9c9"},
+                "tickfont": {"color": TEXT_PRIMARY},
             },
-            "bgcolor": "rgba(0,0,0,0)",
+            "bgcolor": PAPER_BG,
         },
-        paper_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor=PAPER_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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"},
+            "increasing": {
-            "decreasing": {"color": "red" if positive_is_good else "green"},
+                "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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font={"family": "Inter, sans-serif", "color": "#c9c9c9"},
+        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)",
+            paper_bgcolor=PAPER_BG,
-            plot_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor=PLOT_BG,
-            font_color="#c9c9c9",
+            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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        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)",
+            paper_bgcolor=PAPER_BG,
-            plot_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor=PLOT_BG,
-            font_color="#c9c9c9",
+            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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        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)",
+            paper_bgcolor=PAPER_BG,
-            plot_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor=PLOT_BG,
-            font_color="#c9c9c9",
+            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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        xaxis={"gridcolor": GRID_COLOR, "linecolor": GRID_COLOR},
-        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        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
+        "monetary": CHART_PALETTE[0],  # Blue
-        "tax": "#2ca02c",  # Green
+        "tax": CHART_PALETTE[3],  # Teal/green
-        "regulatory": "#ff7f0e",  # Orange
+        "regulatory": CHART_PALETTE[1],  # Orange
-        "supply": "#9467bd",  # Purple
+        "supply": CHART_PALETTE[6],  # Pink
-        "economic": "#d62728",  # Red
+        "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/toronto/callbacks/chart_callbacks.py

@@ -5,7 +5,15 @@ 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,
@@ -109,18 +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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)"},
+        xaxis={"gridcolor": GRID_COLOR},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Avg Rent (2BR)"},
+        yaxis={"gridcolor": GRID_COLOR, "title": "Avg Rent (2BR)"},
         showlegend=False,
         margin={"l": 40, "r": 10, "t": 10, "b": 30},
     )
@@ -153,7 +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
     )
 
 
@@ -178,19 +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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
-        xaxis={"gridcolor": "rgba(128,128,128,0.2)"},
+        xaxis={"gridcolor": GRID_COLOR},
-        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Crime Rate per 100K"},
+        yaxis={"gridcolor": GRID_COLOR, "title": "Crime Rate per 100K"},
         showlegend=False,
         margin={"l": 40, "r": 10, "t": 10, "b": 30},
     )
@@ -233,7 +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
     )
 
 
@@ -264,7 +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
     )
 
 
@@ -301,7 +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,
     )
 
@@ -333,7 +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
     )
 
 
@@ -387,9 +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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        font_color=TEXT_PRIMARY,
         xaxis={"visible": False},
         yaxis={"visible": False},
     )
@@ -400,6 +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)",
+        paper_bgcolor=PAPER_BG,
-        font_color="#c9c9c9",
+        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)",
+        paper_bgcolor=PAPER_BG,
-        plot_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor=PLOT_BG,
-        font_color="#c9c9c9",
+        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/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
+    # Relationships - explicit foreign_keys needed for cross-schema joins
-    time = relationship("DimTime", backref="rentals")
+    time = relationship("DimTime", foreign_keys=[date_key], backref="rentals")
-    zone = relationship("DimCMHCZone", backref="rentals")
+    zone = relationship("DimCMHCZone", foreign_keys=[zone_key], backref="rentals")

scripts/db/init_schema.py

@@ -15,6 +15,7 @@ from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 from portfolio_app.toronto.models import create_tables, get_engine  # noqa: E402
+from portfolio_app.toronto.models.dimensions import RAW_TORONTO_SCHEMA  # noqa: E402
 
 
 def main() -> int:
@@ -32,16 +33,30 @@ def main() -> int:
             result.fetchone()
         print("Database connection successful")
 
+        # Create domain-specific schemas
+        with engine.connect() as conn:
+            conn.execute(text(f"CREATE SCHEMA IF NOT EXISTS {RAW_TORONTO_SCHEMA}"))
+            conn.commit()
+        print(f"Created schema: {RAW_TORONTO_SCHEMA}")
+
         # Create all tables
         create_tables()
         print("Schema created successfully")
 
-        # List created tables
+        # List created tables by schema
         from sqlalchemy import inspect
 
         inspector = inspect(engine)
-        tables = inspector.get_table_names()
+
-        print(f"Created tables: {', '.join(tables)}")
+        # Public schema tables
+        public_tables = inspector.get_table_names(schema="public")
+        if public_tables:
+            print(f"Public schema tables: {', '.join(public_tables)}")
+
+        # raw_toronto schema tables
+        toronto_tables = inspector.get_table_names(schema=RAW_TORONTO_SCHEMA)
+        if toronto_tables:
+            print(f"{RAW_TORONTO_SCHEMA} schema tables: {', '.join(toronto_tables)}")
 
         return 0