personal-portfolio/docs/changes/Change-Toronto-Analysis-Reviewed.md

Toronto Neighbourhood Dashboard — Implementation Plan

Document Type: Execution Guide Target: Transition from TRREB-based to Neighbourhood-based Dashboard Version: 2.0 | January 2026

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Overview

Transition from TRREB district-based housing dashboard to a comprehensive Toronto Neighbourhood Dashboard built around the city's 158 official neighbourhoods.

Key Changes:

• Geographic foundation: TRREB districts (~35) → City Neighbourhoods (158)
• Data sources: PDF parsing → Open APIs (Toronto Open Data, Toronto Police, CMHC)
• Scope: Housing-only → 5 thematic tabs (Overview, Housing, Safety, Demographics, Amenities)

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Phase 1: Repository Cleanup

Files to DELETE

File	Reason
portfolio_app/toronto/schemas/trreb.py	TRREB schema obsolete
portfolio_app/toronto/parsers/trreb.py	PDF parsing no longer needed
portfolio_app/toronto/loaders/trreb.py	TRREB loading logic obsolete
dbt/models/staging/stg_trreb__purchases.sql	TRREB staging obsolete
dbt/models/intermediate/int_purchases__monthly.sql	TRREB intermediate obsolete
dbt/models/marts/mart_toronto_purchases.sql	Will rebuild for neighbourhood grain

Files to MODIFY (Remove TRREB References)

File	Action
portfolio_app/toronto/schemas/__init__.py	Remove TRREB imports
portfolio_app/toronto/parsers/__init__.py	Remove TRREB parser imports
portfolio_app/toronto/loaders/__init__.py	Remove TRREB loader imports
portfolio_app/toronto/models/facts.py	Remove FactPurchases model
portfolio_app/toronto/models/dimensions.py	Remove DimTRREBDistrict model
portfolio_app/toronto/demo_data.py	Remove TRREB demo data
dbt/models/sources.yml	Remove TRREB source definitions
dbt/models/schema.yml	Remove TRREB model documentation

Files to KEEP (Reusable)

File	Why
portfolio_app/toronto/schemas/cmhc.py	CMHC data still used
portfolio_app/toronto/parsers/cmhc.py	Reusable with modifications
portfolio_app/toronto/loaders/base.py	Generic database utilities
portfolio_app/toronto/loaders/dimensions.py	Dimension loading patterns
portfolio_app/toronto/models/base.py	SQLAlchemy base class
portfolio_app/figures/*.py	All chart factories reusable
portfolio_app/components/*.py	All UI components reusable

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Phase 2: Documentation Updates

Document	Action
CLAUDE.md	Update data model section, mark transition complete
docs/PROJECT_REFERENCE.md	Update architecture, data sources
docs/toronto_housing_dashboard_spec_v5.md	Archive or delete
docs/wbs_sprint_plan_v4.md	Archive or delete

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Phase 3: New Data Model

Star Schema (Neighbourhood-Centric)

Table	Type	Description
dim_neighbourhood	Central Dimension	158 neighbourhoods with geometry
dim_time	Dimension	Date dimension (keep existing)
dim_cmhc_zone	Bridge Dimension	15 CMHC zones with neighbourhood mapping
bridge_cmhc_neighbourhood	Bridge	Zone-to-neighbourhood area weights
fact_census	Fact	Census indicators by neighbourhood
fact_crime	Fact	Crime stats by neighbourhood
fact_rentals	Fact	Rental data by CMHC zone (keep existing)
fact_amenities	Fact	Amenity counts by neighbourhood

New Schema Files

File	Contains
toronto/schemas/neighbourhood.py	NeighbourhoodRecord, CensusRecord, CrimeRecord
toronto/schemas/amenities.py	AmenityType enum, AmenityRecord

New Parser Files

File	Data Source	API
toronto/parsers/toronto_open_data.py	Neighbourhoods, Census, Parks, Schools, Childcare	Toronto Open Data Portal
toronto/parsers/toronto_police.py	Crime Rates, MCI, Shootings	Toronto Police Portal

New Loader Files

File	Purpose
toronto/loaders/neighbourhoods.py	Load GeoJSON boundaries
toronto/loaders/census.py	Load neighbourhood profiles
toronto/loaders/crime.py	Load crime statistics
toronto/loaders/amenities.py	Load parks, schools, childcare
toronto/loaders/cmhc_crosswalk.py	Build CMHC-neighbourhood bridge

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Phase 4: dbt Restructuring

Staging Layer

Model	Source
stg_toronto__neighbourhoods	dim_neighbourhood
stg_toronto__census	fact_census
stg_toronto__crime	fact_crime
stg_toronto__amenities	fact_amenities
stg_cmhc__rentals	fact_rentals (modify existing)
stg_cmhc__zone_crosswalk	bridge_cmhc_neighbourhood

Intermediate Layer

Model	Purpose
int_neighbourhood__demographics	Combined census demographics
int_neighbourhood__housing	Housing indicators
int_neighbourhood__crime_summary	Aggregated crime by type
int_neighbourhood__amenity_scores	Normalized amenity metrics
int_rentals__neighbourhood_allocated	CMHC rentals allocated to neighbourhoods

Mart Layer (One per Tab)

Model	Tab	Key Metrics
mart_neighbourhood_overview	Overview	Composite livability score
mart_neighbourhood_housing	Housing	Affordability index, rent-to-income
mart_neighbourhood_safety	Safety	Crime rates, YoY change
mart_neighbourhood_demographics	Demographics	Income, age, diversity
mart_neighbourhood_amenities	Amenities	Parks, schools, transit per capita

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Phase 5: Dashboard Implementation

Tab Structure

pages/toronto/
├── dashboard.py          # Main layout with tab navigation
├── tabs/
│   ├── overview.py       # Composite livability
│   ├── housing.py        # Affordability
│   ├── safety.py         # Crime
│   ├── demographics.py   # Population
│   └── amenities.py      # Services
└── callbacks/
    ├── map_callbacks.py
    ├── chart_callbacks.py
    └── selection_callbacks.py

Layout Pattern (All Tabs)

Each tab follows the same structure:

1. Choropleth Map (left) — 158 neighbourhoods, click to select
2. KPI Cards (right) — 3-4 contextual metrics
3. Supporting Charts (bottom) — Trend + comparison visualizations
4. Details Panel (collapsible) — All metrics for selected neighbourhood

Graphs by Tab

Tab	Choropleth Metric	Chart 1	Chart 2
Overview	Livability score	Top/Bottom 10 bar	Income vs Crime scatter
Housing	Affordability index	Rent trend (5yr line)	Dwelling types (pie/bar)
Safety	Crime rate per 100K	Crime breakdown (stacked bar)	Crime trend (5yr line)
Demographics	Median income	Age pyramid	Top languages (bar)
Amenities	Park area per capita	Amenity radar	Transit accessibility (bar)

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Phase 6: Jupyter Notebooks

Purpose

One notebook per graph to document:

1. Data Reference — How the data was built (query, transformation steps, sample output)
2. Data Visualization — Import figure factory, render the graph

Directory Structure

notebooks/
├── README.md
├── overview/
├── housing/
├── safety/
├── demographics/
└── amenities/

Notebook Template

# [Graph Name]

## 1. Data Reference

### Source Tables
- List tables/marts used
- Grain of each table

### Query
```sql
SELECT ... FROM ...

Transformation Steps

1. Step description
2. Step description

Sample Data

df = pd.read_sql(query, engine)
df.head(10)

2. Data Visualization

from portfolio_app.figures.choropleth import create_choropleth_figure
fig = create_choropleth_figure(...)
fig.show()

Create one notebook per graph as each is implemented (15 total across 5 tabs).

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## Phase 7: Final Documentation Review

After all implementation, audit and update:

- [ ] `CLAUDE.md` — Project status, app structure, data model, URL routes
- [ ] `README.md` — Project description, installation, quick start
- [ ] `docs/PROJECT_REFERENCE.md` — Architecture matches implementation
- [ ] Remove or archive legacy spec documents

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## Data Source Reference

| Source | Datasets | URL |
|--------|----------|-----|
| Toronto Open Data | Neighbourhoods, Census Profiles, Parks, Schools, Childcare, TTC | open.toronto.ca |
| Toronto Police | Crime Rates, MCI, Shootings | data.torontopolice.on.ca |
| CMHC | Rental Market Survey | cmhc-schl.gc.ca |

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## CMHC Zone Mapping Note

CMHC uses 15 zones that don't align with 158 neighbourhoods. Strategy:
- Create `bridge_cmhc_neighbourhood` with area weights
- Allocate rental metrics proportionally to overlapping neighbourhoods
- Document methodology in `/toronto/methodology` page

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*Document Version: 2.0*
*Trimmed from v1.0 for execution clarity*