refactor: multi-dashboard structural migration

- Rename dbt project from toronto_housing to portfolio
- Restructure dbt models into domain subdirectories:
  - shared/ for cross-domain dimensions (dim_time)
  - staging/toronto/, intermediate/toronto/, marts/toronto/
- Update SQLAlchemy models for raw_toronto schema
- Add explicit cross-schema FK relationships for FactRentals
- Namespace figure factories under figures/toronto/
- Namespace notebooks under notebooks/toronto/
- Update Makefile with domain-specific targets and env loading
- Update all documentation for multi-dashboard structure

This enables adding new dashboard projects (e.g., /football, /energy)
without structural conflicts or naming collisions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

CLAUDE.md

@@ -1,5 +1,37 @@
 # 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**
+
+---
+
+
+
 Working context for Claude Code on the Analytics Portfolio project.
 
 ---
@@ -26,8 +58,9 @@ 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 load-toronto-only # Load Toronto data without dbt or seeding
 make seed-data      # Seed sample development data
 
 # Application
@@ -127,13 +160,21 @@ class LoadError(PortfolioError):
 | `pages/` | Dash Pages (file-based routing) | URLs match file paths |
 | `pages/toronto/` | Toronto Dashboard | `tabs/` for layouts, `callbacks/` for interactions |
 | `components/` | Shared UI components | metric_card, sidebar, map_controls, time_slider |
-| `figures/` | Plotly chart factories | choropleth, bar_charts, scatter, radar, time_series |
+| `figures/toronto/` | Toronto chart factories | choropleth, bar_charts, scatter, radar, time_series |
 | `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 |
+| `notebooks/toronto/` | Toronto documentation | 5 domains: overview, housing, safety, demographics, amenities |
 
 **Key URLs:** `/` (home), `/toronto` (dashboard), `/blog` (listing), `/blog/{slug}` (articles)
 
+### Multi-Dashboard Architecture
+
+The codebase is structured to support multiple dashboard projects:
+- **figures/**: Domain-namespaced figure factories (`figures/toronto/`, future: `figures/football/`)
+- **notebooks/**: Domain-namespaced documentation (`notebooks/toronto/`, future: `notebooks/football/`)
+- **dbt models**: Domain subdirectories (`staging/toronto/`, `marts/toronto/`)
+- **Database schemas**: Domain-specific raw data (`raw_toronto`, future: `raw_football`)
+
 ---
 
 ## Tech Stack (Locked)
@@ -161,6 +202,16 @@ class LoadError(PortfolioError):
 
 ## Data Model Overview
 
+### Database Schemas
+
+| Schema | Purpose |
+|--------|---------|
+| `public` | Shared dimensions (dim_time) |
+| `raw_toronto` | Toronto-specific raw/dimension tables |
+| `staging` | dbt staging views |
+| `intermediate` | dbt intermediate views |
+| `marts` | dbt mart tables |
+
 ### Geographic Reality (Toronto Housing)
 
 ```
@@ -168,20 +219,31 @@ City Neighbourhoods (158) - Primary geographic unit for analysis
 CMHC Zones (~20)          - Rental data (Census Tract aligned)
 ```
 
-### Star Schema
+### Star Schema (raw_toronto)
 
 | Table | Type | Keys |
 |-------|------|------|
 | `fact_rentals` | Fact | -> dim_time, dim_cmhc_zone |
-| `dim_time` | Dimension | date_key (PK) |
+| `dim_time` | Dimension (public) | date_key (PK) - shared |
 | `dim_cmhc_zone` | Dimension | zone_key (PK), geometry |
 | `dim_neighbourhood` | Dimension | neighbourhood_id (PK), geometry |
 | `dim_policy_event` | Dimension | event_id (PK) |
 
-### dbt Layers
+### dbt Project: `portfolio`
+
+**Model Structure:**
+```
+dbt/models/
+├── shared/              # Cross-domain dimensions
+│   └── stg_dimensions__time.sql
+├── staging/toronto/     # Toronto staging models
+├── intermediate/toronto/ # Toronto intermediate models
+└── marts/toronto/       # Toronto mart tables
+```
 
 | 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 |
@@ -196,7 +258,6 @@ CMHC Zones (~20)          - Rental data (Census Tract aligned)
 |---------|--------|
 | 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 |
 
 ---
 
@@ -351,4 +412,4 @@ Use for git operations assistance.
 
 ---
 
-*Last Updated: January 2026 (Post-Sprint 9)*
+*Last Updated: February 2026 (Multi-Dashboard Architecture)*

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:
+      toronto:
         +materialized: view
         +schema: staging
     intermediate:
+      toronto:
         +materialized: view
         +schema: intermediate
     marts:
+      toronto:
         +materialized: table
         +schema: marts

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/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

@@ -18,15 +18,6 @@ models:
         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
     description: "Staged CMHC zone dimension"
     columns:

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

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

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

@@ -116,16 +116,38 @@ 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 |
+| `staging` | Staging models | dbt |
+| `intermediate` | Intermediate models | dbt |
+| `marts` | 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 |
+
+### Public Schema
+
+Shared dimensions used across all projects:
+
+| Table | Description |
+|-------|-------------|
+| `dim_time` | Time dimension (monthly grain) |
 
 ### Staging Schema (dbt)
 

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` |

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 +81,47 @@ 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: staging
+```
 
 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/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/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/pages/toronto/callbacks/chart_callbacks.py

@@ -5,7 +5,7 @@ import pandas as pd
 import plotly.graph_objects as go
 from dash import Input, Output, callback
 
-from portfolio_app.figures import (
+from portfolio_app.figures.toronto import (
     create_donut_chart,
     create_horizontal_bar,
     create_radar_figure,

portfolio_app/pages/toronto/callbacks/map_callbacks.py

@@ -4,7 +4,7 @@
 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.figures.toronto import create_choropleth_figure, create_ranking_bar
 from portfolio_app.toronto.services import (
     get_amenities_data,
     get_demographics_data,

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