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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>

2026-02-01 19:08:20 -05:00

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Dashboard Documentation Notebooks

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

Notebook Template

Each notebook follows a standard two-section structure:

Section 1: Data Reference

Documents the data pipeline:

Source Tables: List of dbt marts/tables used
SQL Query: The exact query to fetch data
Transformation Steps: Any pandas/python transformations
Sample Output: First 10 rows of the result

Section 2: Data Visualization

Documents the figure creation:

Figure Factory: Import from portfolio_app.figures
Parameters: Key configuration options
Rendered Output: The actual visualization

Available Figure Factories

Factory	Module	Use Case
`create_choropleth`	`figures.choropleth`	Map visualizations
`create_ranking_bar`	`figures.bar_charts`	Top/bottom N rankings
`create_stacked_bar`	`figures.bar_charts`	Category breakdowns
`create_scatter`	`figures.scatter`	Correlation plots
`create_radar`	`figures.radar`	Multi-metric comparisons
`create_age_pyramid`	`figures.demographics`	Age distributions
`create_time_series`	`figures.time_series`	Trend lines

Usage

Start Jupyter from project root:
```
jupyter notebook notebooks/
```
Ensure database is running:
```
make docker-up
```
Each notebook is self-contained - run all cells top to bottom.

Notebook Naming Convention

{metric}_{chart_type}.ipynb

Examples:

livability_choropleth.ipynb
crime_trend_line.ipynb
age_pyramid.ipynb

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