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2026-01-16 12:52:18 +00:00

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Toronto Neighbourhood Dashboard — Deliverables

Project Type: Interactive Data Visualization Dashboard
Geographic Scope: City of Toronto, 158 Official Neighbourhoods
Author: Leo Miranda
Version: 1.0 | January 2026

Executive Summary

Multi-tab analytics dashboard built around Toronto's official neighbourhood boundaries. The core interaction is a choropleth map where users explore the city through different thematic lenses—housing affordability, safety, demographics, amenities—with supporting visualizations that tell a cohesive story per theme.

Primary Goals:

Demonstrate interactive data visualization skills (Plotly/Dash)
Showcase data engineering capabilities (multi-source ETL, dimensional modeling)
Create a portfolio piece with genuine analytical value

Part 1: Geographic Foundation (Required First)

Dataset	Source	Format	Last Updated	Download
Neighbourhoods Boundaries	Toronto Open Data	GeoJSON	2024	Link
Neighbourhood Profiles	Toronto Open Data	CSV	2021 Census	Link

Critical Notes:

Toronto uses 158 official neighbourhoods (updated 2024, was 140)
GeoJSON includes AREA_ID for joining to tabular data
Neighbourhood Profiles has 2,400+ indicators per neighbourhood from Census

Part 2: Tier 1 — MVP Datasets

Dataset	Source	Measures Available	Update Freq	Granularity
Neighbourhoods GeoJSON	Toronto Open Data	Boundary polygons, area IDs	Static	Neighbourhood
Neighbourhood Profiles (full)	Toronto Open Data	2,400+ Census indicators	Every 5 years	Neighbourhood
Neighbourhood Crime Rates	Toronto Police Portal	MCI rates per 100K by year	Annual	Neighbourhood
CMHC Rental Market Survey	CMHC Portal	Avg rent by bedroom, vacancy rate	Annual (Oct)	15 CMHC Zones
Parks	Toronto Open Data	Park locations, area, type	Annual	Point/Polygon

Total API/Download Calls: 5
Data Volume: ~50MB combined

Tier 1 Measures to Extract

From Neighbourhood Profiles:

Population, population density
Median household income
Age distribution (0-14, 15-24, 25-44, 45-64, 65+)
% Immigrants, % Visible minorities
Top languages spoken
Unemployment rate
Education attainment (% with post-secondary)
Housing tenure (own vs rent %)
Dwelling types distribution
Average rent, housing costs as % of income

From Crime Rates:

Total MCI rate per 100K population
Year-over-year crime trend

From CMHC:

Average monthly rent (1BR, 2BR, 3BR)
Vacancy rates

From Parks:

Park count per neighbourhood
Park area per capita

Part 3: Tier 2 — Expansion Datasets

Dataset	Source	Measures Available	Update Freq	Granularity
Major Crime Indicators (MCI)	Toronto Police Portal	Assault, B&E, auto theft, robbery, theft over	Quarterly	Neighbourhood
Shootings & Firearm Discharges	Toronto Police Portal	Shooting incidents, injuries, fatalities	Quarterly	Neighbourhood
Building Permits	Toronto Open Data	New construction, permits by type	Monthly	Address-level
Schools	Toronto Open Data	Public/Catholic, elementary/secondary	Annual	Point
TTC Routes & Stops	Toronto Open Data	Route geometry, stop locations	Static	Route/Stop
Licensed Child Care Centres	Toronto Open Data	Capacity, ages served, locations	Annual	Point

Tier 2 Measures to Extract

From MCI Details:

Breakdown by crime type (assault, B&E, auto theft, robbery, theft over)

From Shootings:

Shooting incidents count
Injuries/fatalities

From Building Permits:

New construction permits (trailing 12 months)
Permit types distribution

From Schools:

Schools per 1000 children
School type breakdown

From TTC:

Transit stops within neighbourhood
Transit accessibility score

From Child Care:

Child care spaces per capita
Coverage by age group

Part 4: Data Sources by Thematic Group

GROUP A: Housing & Affordability

Dataset	Tier	Measures	Update Freq
Neighbourhood Profiles (Housing)	1	Avg rent, ownership %, dwelling types, housing costs as % of income	Every 5 years
CMHC Rental Market Survey	1	Avg rent by bedroom, vacancy rate, rental universe	Annual
Building Permits	2	New construction, permits by type	Monthly

Calculated Metrics:

Rent-to-Income Ratio (CMHC rent ÷ Census income)
Affordability Index (% of income spent on housing)

GROUP B: Safety & Crime

Dataset	Tier	Measures	Update Freq
Neighbourhood Crime Rates	1	MCI rates per 100K pop by year	Annual
Major Crime Indicators (MCI)	2	Assault, B&E, auto theft, robbery, theft over	Quarterly
Shootings & Firearm Discharges	2	Shooting incidents, injuries, fatalities	Quarterly

Calculated Metrics:

Year-over-year crime change %
Crime type distribution

GROUP C: Demographics & Community

Dataset	Tier	Measures	Update Freq
Neighbourhood Profiles (Demographics)	1	Age distribution, household composition, income	Every 5 years
Neighbourhood Profiles (Immigration)	1	Immigration status, visible minorities, languages	Every 5 years
Neighbourhood Profiles (Education)	1	Education attainment, field of study	Every 5 years
Neighbourhood Profiles (Labour)	1	Employment rate, occupation, industry	Every 5 years

GROUP D: Transportation & Mobility

Dataset	Tier	Measures	Update Freq
Commute Mode (Census)	1	% car, transit, walk, bike	Every 5 years
TTC Routes & Stops	2	Route geometry, stop locations	Static

Calculated Metrics:

Transit accessibility (stops within 500m of neighbourhood centroid)

GROUP E: Amenities & Services

Dataset	Tier	Measures	Update Freq
Parks	1	Park locations, area, type	Annual
Schools	2	Public/Catholic, elementary/secondary	Annual
Licensed Child Care Centres	2	Capacity, ages served	Annual

Calculated Metrics:

Park area per capita
Schools per 1000 children (ages 5-17)
Child care spaces per 1000 children (ages 0-4)

Part 5: Tab Structure

Tab Architecture

┌────────────────────────────────────────────────────────────────┐
│  [Overview] [Housing] [Safety] [Demographics] [Amenities]      │
├────────────────────────────────────────────────────────────────┤
│                                                                │
│    ┌─────────────────────────────────┐  ┌────────────────┐    │
│    │                                 │  │  KPI Card 1    │    │
│    │       CHOROPLETH MAP            │  ├────────────────┤    │
│    │   (158 Neighbourhoods)          │  │  KPI Card 2    │    │
│    │                                 │  ├────────────────┤    │
│    │   Click to select               │  │  KPI Card 3    │    │
│    │                                 │  └────────────────┘    │
│    └─────────────────────────────────┘                        │
│                                                                │
│    ┌─────────────────────┐  ┌─────────────────────┐           │
│    │  Supporting Chart 1 │  │  Supporting Chart 2 │           │
│    │  (Context/Trend)    │  │  (Comparison/Rank)  │           │
│    └─────────────────────┘  └─────────────────────┘           │
│                                                                │
│    [Neighbourhood: Selected Name] ────────────────────────    │
│    Details panel with all metrics for selected area           │
└────────────────────────────────────────────────────────────────┘

Tab 1: Overview (Default Landing)

Story: "How do Toronto neighbourhoods compare across key livability metrics?"

Element	Content	Data Source
Map Colour	Composite livability score	Calculated from weighted metrics
KPI Cards	Population, Median Income, Avg Crime Rate	Neighbourhood Profiles, Crime Rates
Chart 1	Top 10 / Bottom 10 by livability score	Calculated
Chart 2	Income vs Crime scatter plot	Neighbourhood Profiles, Crime Rates

Metric Selector: Allow user to change map colour by any single metric.

Tab 2: Housing & Affordability

Story: "Where can you afford to live, and what's being built?"

Element	Content	Data Source
Map Colour	Rent-to-Income Ratio (Affordability Index)	CMHC + Census income
KPI Cards	Median Rent (1BR), Vacancy Rate, New Permits (12mo)	CMHC, Building Permits
Chart 1	Rent trend (5-year line chart by bedroom)	CMHC historical
Chart 2	Dwelling type breakdown (pie/bar)	Neighbourhood Profiles

Metric Selector: Toggle between rent, ownership %, dwelling types.

Tab 3: Safety

Story: "How safe is each neighbourhood, and what crimes are most common?"

Element	Content	Data Source
Map Colour	Total MCI Rate per 100K	Crime Rates
KPI Cards	Total Crimes, YoY Change %, Shooting Incidents	Crime Rates, Shootings
Chart 1	Crime type breakdown (stacked bar)	MCI Details
Chart 2	5-year crime trend (line chart)	Crime Rates historical

Metric Selector: Toggle between total crime, specific crime types, shootings.

Tab 4: Demographics

Story: "Who lives here? Age, income, diversity."

Element	Content	Data Source
Map Colour	Median Household Income	Neighbourhood Profiles
KPI Cards	Population, % Immigrant, Unemployment Rate	Neighbourhood Profiles
Chart 1	Age distribution (population pyramid or bar)	Neighbourhood Profiles
Chart 2	Top languages spoken (horizontal bar)	Neighbourhood Profiles

Metric Selector: Income, immigrant %, age groups, education.

Tab 5: Amenities & Services

Story: "What's nearby? Parks, schools, child care, transit."

Element	Content	Data Source
Map Colour	Park Area per Capita	Parks + Population
KPI Cards	Parks Count, Schools Count, Child Care Spaces	Multiple datasets
Chart 1	Amenity density comparison (radar or bar)	Calculated
Chart 2	Transit accessibility (stops within 500m)	TTC Stops

Metric Selector: Parks, schools, child care, transit access.

Part 6: Data Pipeline Architecture

ETL Flow

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   DATA SOURCES  │     │   STAGING LAYER │     │   MART LAYER    │
│                 │     │                 │     │                 │
│ Toronto Open    │────▶│ stg_geography   │────▶│ dim_neighbourhood│
│ Data Portal     │     │ stg_census      │     │ fact_crime      │
│                 │     │ stg_crime       │     │ fact_housing    │
│ CMHC Portal     │────▶│ stg_rental      │     │ fact_amenities  │
│                 │     │ stg_permits     │     │                 │
│ Toronto Police  │────▶│ stg_amenities   │     │ agg_dashboard   │
│ Portal          │     │ stg_childcare   │     │ (pre-computed)  │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Key Transformations

Transformation	Description
Geography Standardization	Ensure all datasets use `neighbourhood_id` (AREA_ID from GeoJSON)
Census Pivot	Neighbourhood Profiles is wide format — pivot to metrics per neighbourhood
CMHC Zone Mapping	Create crosswalk from 15 CMHC zones to 158 neighbourhoods
Amenity Aggregation	Spatial join point data (schools, parks, child care) to neighbourhood polygons
Rate Calculations	Normalize counts to per-capita or per-100K

Data Refresh Schedule

Layer	Frequency	Trigger
Staging (API pulls)	Weekly	Scheduled job
Marts (transforms)	Weekly	Post-staging
Dashboard cache	On-demand	User refresh button

Part 7: Technical Stack

Core Stack

Component	Technology	Rationale
Frontend	Plotly Dash	Production-ready, rapid iteration
Mapping	Plotly `choropleth_mapbox`	Native Dash integration
Data Store	PostgreSQL + PostGIS	Spatial queries, existing expertise
ETL	Python (Pandas, SQLAlchemy)	Existing stack
Deployment	Render / Railway	Free tier, easy Dash hosting

Alternative (Portfolio Stretch)

Component	Technology	Why Consider
Frontend	React + deck.gl	More "modern" for portfolio
Data Store	DuckDB	Serverless, embeddable
ETL	dbt	Aligns with skills roadmap

Appendix A: Data Source URLs

Source	URL
Toronto Open Data — Neighbourhoods	https://open.toronto.ca/dataset/neighbourhoods/
Toronto Open Data — Neighbourhood Profiles	https://open.toronto.ca/dataset/neighbourhood-profiles/
Toronto Police — Neighbourhood Crime Rates	https://data.torontopolice.on.ca/datasets/neighbourhood-crime-rates-open-data
Toronto Police — MCI	https://data.torontopolice.on.ca/datasets/major-crime-indicators-open-data
Toronto Police — Shootings	https://data.torontopolice.on.ca/datasets/shootings-firearm-discharges-open-data
CMHC Rental Market Survey	https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market
Toronto Open Data — Parks	https://open.toronto.ca/dataset/parks/
Toronto Open Data — Schools	https://open.toronto.ca/dataset/school-locations-all-types/
Toronto Open Data — Building Permits	https://open.toronto.ca/dataset/building-permits-cleared-permits/
Toronto Open Data — Child Care	https://open.toronto.ca/dataset/licensed-child-care-centres/
Toronto Open Data — TTC Routes	https://open.toronto.ca/dataset/ttc-routes-and-schedules/

Appendix B: Colour Palettes

Affordability (Diverging)

Status	Hex	Usage
Affordable (<30% income)	`#2ecc71`	Green
Stretched (30-50%)	`#f1c40f`	Yellow
Unaffordable (>50%)	`#e74c3c`	Red

Safety (Sequential)

Status	Hex	Usage
Safest (lowest crime)	`#27ae60`	Dark green
Moderate	`#f39c12`	Orange
Highest Crime	`#c0392b`	Dark red

Demographics — Income (Sequential)

Level	Hex	Usage
Highest Income	`#1a5276`	Dark blue
Mid Income	`#5dade2`	Light blue
Lowest Income	`#ecf0f1`	Light gray

General Recommendation

Use Viridis or Plasma colorscales for perceptually uniform gradients on continuous metrics.

Appendix C: Glossary

Term	Definition
MCI	Major Crime Indicators — Assault, B&E, Auto Theft, Robbery, Theft Over
CMHC Zone	Canada Mortgage and Housing Corporation rental market survey zones (15 in Toronto)
Rent-to-Income Ratio	Monthly rent ÷ monthly household income; <30% is considered affordable
PostGIS	PostgreSQL extension for geographic data
Choropleth	Thematic map where areas are shaded based on a statistical variable

Appendix D: Interview Talking Points

When discussing this project in interviews, emphasize:

Data Engineering: "I built a multi-source ETL pipeline that standardizes geographic keys across Census data, police data, and CMHC rental surveys—three different granularities I had to reconcile."
Dimensional Modeling: "The data model follows star schema patterns with a central neighbourhood dimension table and fact tables for crime, housing, and amenities."
dbt Patterns: "The transformation layer uses staging → intermediate → mart patterns, which I've documented for maintainability."
Business Value: "The dashboard answers questions like 'Where can a young professional afford to live that's safe and has good transit?' — turning raw data into actionable insights."
Technical Decisions: "I chose Plotly Dash over a React frontend because it let me iterate faster while maintaining production-quality interactivity. For a portfolio piece, speed to working demo matters."

Document Version: 1.0
Created: January 2026
Author: Leo Miranda / Claude

18 KiB Raw Blame History

Toronto Neighbourhood Dashboard — Deliverables

Executive Summary

Part 1: Geographic Foundation (Required First)

Part 2: Tier 1 — MVP Datasets

Tier 1 Measures to Extract

Part 3: Tier 2 — Expansion Datasets

Tier 2 Measures to Extract

Part 4: Data Sources by Thematic Group

GROUP A: Housing & Affordability

GROUP B: Safety & Crime

GROUP C: Demographics & Community

GROUP D: Transportation & Mobility

GROUP E: Amenities & Services

Part 5: Tab Structure

Tab Architecture

Tab 1: Overview (Default Landing)

Tab 2: Housing & Affordability

Tab 3: Safety

Tab 4: Demographics

Tab 5: Amenities & Services

Part 6: Data Pipeline Architecture

ETL Flow

Key Transformations

Data Refresh Schedule

Part 7: Technical Stack

Core Stack

Alternative (Portfolio Stretch)

Appendix A: Data Source URLs

Appendix B: Colour Palettes

Affordability (Diverging)

Safety (Sequential)

Demographics — Income (Sequential)

General Recommendation

Appendix C: Glossary

Appendix D: Interview Talking Points

18 KiB

Raw Blame History