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CLAUDE.md
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@@ -6,8 +6,8 @@ Working context for Claude Code on the Analytics Portfolio project.
## Project Status
**Current Sprint**: 9 (Neighbourhood Dashboard Transition)
**Phase**: Toronto Neighbourhood Dashboard
**Current Sprint**: 9 (Neighbourhood Dashboard Transition) - **COMPLETE**
**Phase**: Toronto Neighbourhood Dashboard - Phase 6 & 7 Done
**Branch**: `development` (feature branches merge here)
---
@@ -129,8 +129,12 @@ portfolio_app/
│ └── time_slider.py # Time range selector
├── figures/ # Shared chart factories
│ ├── choropleth.py # Map visualizations
│ ├── summary_cards.py # KPI figures
│ └── time_series.py # Trend charts
│ ├── bar_charts.py # Ranking, stacked, horizontal bars
│ ├── scatter.py # Scatter and bubble plots
│ ├── radar.py # Radar/spider charts
│ ├── demographics.py # Age pyramids, donut charts
│ ├── time_series.py # Trend lines
│ └── summary_cards.py # KPI figures
├── content/ # Markdown content
│ └── blog/ # Blog articles
├── toronto/ # Toronto data logic
@@ -142,6 +146,14 @@ portfolio_app/
├── utils/ # Utilities
│ └── markdown_loader.py # Markdown processing
└── errors/
notebooks/ # Data documentation (Phase 6)
├── README.md # Template and usage guide
├── overview/ # Overview tab notebooks (3)
├── housing/ # Housing tab notebooks (3)
├── safety/ # Safety tab notebooks (3)
├── demographics/ # Demographics tab notebooks (3)
└── amenities/ # Amenities tab notebooks (3)
```
### URL Routing

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# Toronto Neighbourhood Dashboard - Notebooks
Documentation notebooks for the Toronto Neighbourhood Dashboard visualizations. 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
├── 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
1. Start Jupyter from project root:
```bash
jupyter notebook notebooks/
```
2. Ensure database is running:
```bash
make docker-up
```
3. 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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notebooks/amenities/amenity_index_choropleth.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Amenity Index Choropleth Map\n",
"\n",
"Displays total amenities per 1,000 residents across Toronto's 158 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 | amenity_index, total_amenities_per_1000, amenity_tier, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_id,\n",
" neighbourhood_name,\n",
" geometry,\n",
" year,\n",
" total_amenities_per_1000,\n",
" amenity_index,\n",
" amenity_tier,\n",
" parks_per_1000,\n",
" schools_per_1000,\n",
" transit_per_1000,\n",
" total_amenities,\n",
" population\n",
"FROM mart_neighbourhood_amenities\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
"ORDER BY total_amenities_per_1000 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. Filter to most recent year\n",
"2. Convert geometry to GeoJSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import json\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df,\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'total_amenities_per_1000', 'amenity_index', 'amenity_tier']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key='neighbourhood_id',\n",
" color_column='total_amenities_per_1000',\n",
" hover_data=['neighbourhood_name', 'amenity_index', 'parks_per_1000', 'schools_per_1000'],\n",
" color_scale='Greens',\n",
" title='Toronto Amenities per 1,000 Population',\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Amenity Tier Interpretation\n",
"\n",
"| Tier | Meaning |\n",
"|------|--------|\n",
"| 1 | Best served (top 20%) |\n",
"| 2-4 | Middle tiers |\n",
"| 5 | Underserved (bottom 20%) |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"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\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\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 mart_neighbourhood_amenities\n",
"WHERE year = (SELECT MAX(year) FROM 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(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\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.radar import create_radar_figure\n",
"\n",
"# Compare top neighbourhood vs city average (100)\n",
"top_hood = top_5.iloc[0]\n",
"\n",
"data = [\n",
" {\n",
" 'name': top_hood['neighbourhood_name'],\n",
" 'values': [top_hood['parks_index'], top_hood['schools_index'], top_hood['transit_index']],\n",
" 'categories': categories\n",
" },\n",
" {\n",
" 'name': 'City Average',\n",
" 'values': [100, 100, 100],\n",
" 'categories': categories\n",
" }\n",
"]\n",
"\n",
"fig = create_radar_figure(\n",
" data=data,\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
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transit Accessibility Bar Chart\n",
"\n",
"Shows transit stops per 1,000 residents across Toronto 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 | transit_per_1000, transit_index, transit_count |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" transit_per_1000,\n",
" transit_index,\n",
" transit_count,\n",
" population,\n",
" amenity_tier\n",
"FROM mart_neighbourhood_amenities\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_amenities)\n",
" AND transit_per_1000 IS NOT NULL\n",
"ORDER BY transit_per_1000 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. Sort by transit accessibility\n",
"2. Select top 20 for visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = df.head(20).to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'transit_per_1000', 'transit_index', 'transit_count']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.bar_charts import create_horizontal_bar\n",
"\n",
"fig = create_horizontal_bar(\n",
" data=data,\n",
" name_column='neighbourhood_name',\n",
" value_column='transit_per_1000',\n",
" title='Top 20 Neighbourhoods by Transit Accessibility',\n",
" color='#00BCD4',\n",
" value_format='.2f',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transit Statistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"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",
"print(f\" Best Access: {df['transit_per_1000'].max():.2f} per 1,000\")\n",
"print(f\" Worst Access: {df['transit_per_1000'].min():.2f} per 1,000\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Age Distribution Analysis\n",
"\n",
"Compares median age and age index across Toronto neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_demographics` | neighbourhood × year | median_age, age_index, city_avg_age |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" median_age,\n",
" age_index,\n",
" city_avg_age,\n",
" population,\n",
" income_quintile,\n",
" pct_renter_occupied\n",
"FROM mart_neighbourhood_demographics\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
" AND median_age IS NOT NULL\n",
"ORDER BY median_age DESC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods with age data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter to most recent census year\n",
"2. Calculate deviation from city average\n",
"3. Classify as younger/older than average"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_avg = df['city_avg_age'].iloc[0]\n",
"df['age_category'] = df['median_age'].apply(\n",
" lambda x: 'Younger' if x < city_avg else 'Older'\n",
")\n",
"df['age_deviation'] = df['median_age'] - city_avg\n",
"\n",
"data = df.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"print(\"\\nOldest Neighbourhoods:\")\n",
"display(df.head(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.bar_charts import create_ranking_bar\n",
"\n",
"fig = create_ranking_bar(\n",
" data=data,\n",
" name_column='neighbourhood_name',\n",
" value_column='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_bottom='#2196F3', # Blue for younger\n",
" value_format='.1f',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Age vs Income Correlation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Age by income quintile\n",
"print(\"Median Age by Income Quintile:\")\n",
"df.groupby('income_quintile')['median_age'].mean().round(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Median Income Choropleth Map\n",
"\n",
"Displays median household income across Toronto's 158 neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_demographics` | neighbourhood × year | median_household_income, income_index, income_quintile, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_id,\n",
" neighbourhood_name,\n",
" geometry,\n",
" year,\n",
" median_household_income,\n",
" income_index,\n",
" income_quintile,\n",
" population,\n",
" unemployment_rate\n",
"FROM mart_neighbourhood_demographics\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
"ORDER BY median_household_income 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. Filter to most recent census year\n",
"2. Convert geometry to GeoJSON\n",
"3. Scale income to thousands for readability"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import json\n",
"\n",
"df['income_thousands'] = df['median_household_income'] / 1000\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df,\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'median_household_income', 'income_index', 'income_quintile']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key='neighbourhood_id',\n",
" color_column='median_household_income',\n",
" hover_data=['neighbourhood_name', 'income_index', 'income_quintile'],\n",
" color_scale='Viridis',\n",
" title='Toronto Median Household Income by Neighbourhood',\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Income Quintile Distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.groupby('income_quintile')['median_household_income'].agg(['count', 'mean', 'min', 'max']).round(0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Population Density Bar Chart\n",
"\n",
"Shows population density (people per sq km) across Toronto neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_demographics` | neighbourhood × year | population_density, population, land_area_sqkm |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" population_density,\n",
" population,\n",
" land_area_sqkm,\n",
" median_household_income,\n",
" pct_renter_occupied\n",
"FROM mart_neighbourhood_demographics\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
" AND population_density IS NOT NULL\n",
"ORDER BY population_density 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. Sort by population density\n",
"2. Select top 20 most dense neighbourhoods"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = df.head(20).to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'population_density', 'population', 'land_area_sqkm']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.bar_charts import create_horizontal_bar\n",
"\n",
"fig = create_horizontal_bar(\n",
" data=data,\n",
" name_column='neighbourhood_name',\n",
" value_column='population_density',\n",
" title='Top 20 Most Dense Neighbourhoods',\n",
" color='#9C27B0',\n",
" value_format=',.0f',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Density Statistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"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",
"print(f\" Max Density: {df['population_density'].max():,.0f} per sq km\")\n",
"print(f\" Min Density: {df['population_density'].min():,.0f} per sq km\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Affordability Index Choropleth Map\n",
"\n",
"Displays housing affordability across Toronto's 158 neighbourhoods. Index of 100 = city average."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_id,\n",
" neighbourhood_name,\n",
" geometry,\n",
" year,\n",
" affordability_index,\n",
" rent_to_income_pct,\n",
" avg_rent_2bed,\n",
" median_household_income,\n",
" is_affordable\n",
"FROM mart_neighbourhood_housing\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
"ORDER BY affordability_index ASC\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. Filter to most recent year\n",
"2. Convert geometry to GeoJSON\n",
"3. Lower index = more affordable (inverted for visualization clarity)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import json\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df,\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'affordability_index', 'rent_to_income_pct', 'avg_rent_2bed', 'is_affordable']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
"\n",
"**Key Parameters:**\n",
"- `color_column`: 'affordability_index'\n",
"- `color_scale`: 'RdYlGn_r' (reversed: green=affordable, red=expensive)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key='neighbourhood_id',\n",
" color_column='affordability_index',\n",
" hover_data=['neighbourhood_name', 'rent_to_income_pct', 'avg_rent_2bed'],\n",
" color_scale='RdYlGn_r', # Reversed: lower index (affordable) = green\n",
" title='Toronto Housing Affordability Index',\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Index Interpretation\n",
"\n",
"| Index | Meaning |\n",
"|-------|--------|\n",
"| < 100 | More affordable than city average |\n",
"| = 100 | City average affordability |\n",
"| > 100 | Less affordable than city average |\n",
"\n",
"Affordability calculated as: `rent_to_income_pct / city_avg_rent_to_income * 100`"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rent Trend Line Chart\n",
"\n",
"Shows 5-year rental price trends across Toronto neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | year, avg_rent_2bed, rent_yoy_change_pct |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"# City-wide average rent by year\n",
"query = \"\"\"\n",
"SELECT\n",
" year,\n",
" AVG(avg_rent_bachelor) as avg_rent_bachelor,\n",
" AVG(avg_rent_1bed) as avg_rent_1bed,\n",
" AVG(avg_rent_2bed) as avg_rent_2bed,\n",
" AVG(avg_rent_3bed) as avg_rent_3bed,\n",
" AVG(rent_yoy_change_pct) as avg_yoy_change\n",
"FROM mart_neighbourhood_housing\n",
"WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_housing)\n",
"GROUP BY year\n",
"ORDER BY year\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} years of rent data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Aggregate rent by year (city-wide average)\n",
"2. Convert year to datetime for proper x-axis\n",
"3. Reshape for multi-line chart by bedroom type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create date column from year\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",
" value_vars=['avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed'],\n",
" var_name='bedroom_type',\n",
" value_name='avg_rent'\n",
")\n",
"\n",
"# Clean labels\n",
"df_melted['bedroom_type'] = df_melted['bedroom_type'].map({\n",
" 'avg_rent_bachelor': 'Bachelor',\n",
" 'avg_rent_1bed': '1 Bedroom',\n",
" 'avg_rent_2bed': '2 Bedroom',\n",
" 'avg_rent_3bed': '3 Bedroom'\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['year', 'avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed', 'avg_yoy_change']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_price_time_series` from `portfolio_app.figures.time_series`.\n",
"\n",
"**Key Parameters:**\n",
"- `date_column`: 'date'\n",
"- `price_column`: 'avg_rent'\n",
"- `group_column`: 'bedroom_type' (for multi-line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.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",
" price_column='avg_rent',\n",
" group_column='bedroom_type',\n",
" title='Toronto Average Rent Trend (5 Years)',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### YoY Change Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show year-over-year changes\n",
"print(\"Year-over-Year Rent Change (%)\")\n",
"df[['year', 'avg_yoy_change']].dropna()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Housing Tenure Breakdown Bar Chart\n",
"\n",
"Shows the distribution of owner-occupied vs renter-occupied dwellings across neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" pct_owner_occupied,\n",
" pct_renter_occupied,\n",
" income_quintile,\n",
" total_rental_units,\n",
" average_dwelling_value\n",
"FROM mart_neighbourhood_housing\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
" AND pct_owner_occupied IS NOT NULL\n",
"ORDER BY pct_renter_occupied DESC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods with tenure data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter to most recent year with tenure data\n",
"2. Melt owner/renter columns for stacked bar\n",
"3. Sort by renter percentage (highest first)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare for stacked bar\n",
"df_stacked = df.melt(\n",
" id_vars=['neighbourhood_name', 'income_quintile'],\n",
" value_vars=['pct_owner_occupied', 'pct_renter_occupied'],\n",
" var_name='tenure_type',\n",
" value_name='percentage'\n",
")\n",
"\n",
"df_stacked['tenure_type'] = df_stacked['tenure_type'].map({\n",
" 'pct_owner_occupied': 'Owner',\n",
" 'pct_renter_occupied': 'Renter'\n",
"})\n",
"\n",
"data = df_stacked.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Highest Renter Neighbourhoods:\")\n",
"df[['neighbourhood_name', 'pct_renter_occupied', 'pct_owner_occupied', 'income_quintile']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`.\n",
"\n",
"**Key Parameters:**\n",
"- `x_column`: 'neighbourhood_name'\n",
"- `value_column`: 'percentage'\n",
"- `category_column`: 'tenure_type'\n",
"- `show_percentages`: True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.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",
"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",
" value_column='percentage',\n",
" category_column='tenure_type',\n",
" title='Housing Tenure Mix - Top 20 Renter Neighbourhoods',\n",
" color_map={'Owner': '#4CAF50', 'Renter': '#2196F3'},\n",
" show_percentages=True,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### City-Wide Distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# City-wide averages\n",
"print(f\"City Average Owner-Occupied: {df['pct_owner_occupied'].mean():.1f}%\")\n",
"print(f\"City Average Renter-Occupied: {df['pct_renter_occupied'].mean():.1f}%\")\n",
"\n",
"# By income quintile\n",
"print(\"\\nTenure by Income Quintile:\")\n",
"df.groupby('income_quintile')[['pct_owner_occupied', 'pct_renter_occupied']].mean().round(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Income vs Safety Scatter Plot\n",
"\n",
"Explores the correlation between median household income and safety score across Toronto neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_overview` | neighbourhood × year | neighbourhood_name, median_household_income, safety_score, population |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" median_household_income,\n",
" safety_score,\n",
" population,\n",
" livability_score,\n",
" crime_rate_per_100k\n",
"FROM mart_neighbourhood_overview\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_overview)\n",
" AND median_household_income IS NOT NULL\n",
" AND safety_score IS NOT NULL\n",
"ORDER BY median_household_income DESC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods with income and safety data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter out null values for income and safety\n",
"2. Optionally scale income to thousands for readability\n",
"3. Pass to scatter figure factory with optional trendline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Scale income to thousands for better axis readability\n",
"df['income_thousands'] = df['median_household_income'] / 1000\n",
"\n",
"# Prepare data for figure factory\n",
"data = df.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'median_household_income', 'safety_score', 'crime_rate_per_100k']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_scatter_figure` from `portfolio_app.figures.scatter`.\n",
"\n",
"**Key Parameters:**\n",
"- `x_column`: 'income_thousands' (median household income in $K)\n",
"- `y_column`: 'safety_score' (0-100 percentile rank)\n",
"- `name_column`: 'neighbourhood_name' (hover label)\n",
"- `size_column`: 'population' (optional, bubble size)\n",
"- `trendline`: True (adds OLS regression line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.scatter import create_scatter_figure\n",
"\n",
"fig = create_scatter_figure(\n",
" data=data,\n",
" x_column='income_thousands',\n",
" y_column='safety_score',\n",
" name_column='neighbourhood_name',\n",
" size_column='population',\n",
" title='Income vs Safety by Neighbourhood',\n",
" x_title='Median Household Income ($K)',\n",
" y_title='Safety Score (0-100)',\n",
" trendline=True,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpretation\n",
"\n",
"This scatter plot reveals the relationship between income and safety:\n",
"\n",
"- **Positive correlation**: Higher income neighbourhoods tend to have higher safety scores\n",
"- **Bubble size**: Represents population (larger = more people)\n",
"- **Trendline**: Orange dashed line shows the overall trend\n",
"- **Outliers**: Neighbourhoods far from the trendline are interesting cases\n",
" - Above line: Safer than income would predict\n",
" - Below line: Less safe than income would predict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate correlation coefficient\n",
"correlation = df['median_household_income'].corr(df['safety_score'])\n",
"print(f\"Correlation coefficient (Income vs Safety): {correlation:.3f}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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notebooks/overview/livability_choropleth.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Livability Score Choropleth Map\n",
"\n",
"Displays neighbourhood livability scores on an interactive map of Toronto's 158 neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_overview` | neighbourhood × year | livability_score, safety_score, affordability_score, amenity_score, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"# Connect to database\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\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 mart_neighbourhood_overview\n",
"WHERE year = (SELECT MAX(year) FROM 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",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter to most recent year of data\n",
"2. Extract GeoJSON from PostGIS geometry column\n",
"3. Pass to choropleth figure factory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Transform geometry to GeoJSON\n",
"import geopandas as gpd\n",
"import json\n",
"\n",
"# Convert WKB geometry to GeoDataFrame\n",
"gdf = gpd.GeoDataFrame(\n",
" df,\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'livability_score', 'safety_score', 'affordability_score', 'amenity_score']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
"\n",
"**Key Parameters:**\n",
"- `geojson`: GeoJSON FeatureCollection with neighbourhood boundaries\n",
"- `data`: List of dicts with neighbourhood_id and scores\n",
"- `location_key`: 'neighbourhood_id'\n",
"- `color_column`: 'livability_score' (or safety_score, etc.)\n",
"- `color_scale`: 'RdYlGn' (red=low, yellow=mid, green=high)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key='neighbourhood_id',\n",
" color_column='livability_score',\n",
" hover_data=['neighbourhood_name', 'safety_score', 'affordability_score', 'amenity_score'],\n",
" color_scale='RdYlGn',\n",
" title='Toronto Neighbourhood Livability Score',\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Score Components\n",
"\n",
"The livability score is a weighted composite:\n",
"\n",
"| Component | Weight | Source |\n",
"|-----------|--------|--------|\n",
"| Safety | 30% | Inverse of crime rate per 100K |\n",
"| Affordability | 40% | Inverse of rent-to-income ratio |\n",
"| Amenities | 30% | Amenities per 1,000 residents |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top & Bottom 10 Neighbourhoods Bar Chart\n",
"\n",
"Horizontal bar chart showing the highest and lowest scoring neighbourhoods by livability."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_overview` | neighbourhood × year | neighbourhood_name, livability_score |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" livability_score,\n",
" safety_score,\n",
" affordability_score,\n",
" amenity_score\n",
"FROM mart_neighbourhood_overview\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_overview)\n",
" AND livability_score IS NOT NULL\n",
"ORDER BY livability_score DESC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods with scores\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Sort by livability_score descending\n",
"2. Take top 10 and bottom 10\n",
"3. Pass to ranking bar figure factory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The figure factory handles top/bottom selection internally\n",
"# Just prepare as list of dicts\n",
"data = df.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Top 5:\")\n",
"display(df.head(5))\n",
"print(\"\\nBottom 5:\")\n",
"display(df.tail(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`.\n",
"\n",
"**Key Parameters:**\n",
"- `data`: List of dicts with all neighbourhoods\n",
"- `name_column`: 'neighbourhood_name'\n",
"- `value_column`: 'livability_score'\n",
"- `top_n`: 10 (green bars)\n",
"- `bottom_n`: 10 (red bars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.bar_charts import create_ranking_bar\n",
"\n",
"fig = create_ranking_bar(\n",
" data=data,\n",
" name_column='neighbourhood_name',\n",
" value_column='livability_score',\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_bottom='#F44336', # Red for bottom performers\n",
" value_format='.1f',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpretation\n",
"\n",
"- **Green bars**: Highest livability scores (best combination of safety, affordability, and amenities)\n",
"- **Red bars**: Lowest livability scores (areas that may need targeted investment)\n",
"\n",
"The ranking bar chart provides quick context for which neighbourhoods stand out at either extreme."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Crime Type Breakdown Bar Chart\n",
"\n",
"Stacked bar chart showing crime composition by Major Crime Indicator (MCI) categories."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_safety` | neighbourhood × year | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" assault_count,\n",
" auto_theft_count,\n",
" break_enter_count,\n",
" robbery_count,\n",
" theft_over_count,\n",
" homicide_count,\n",
" total_incidents,\n",
" crime_rate_per_100k\n",
"FROM mart_neighbourhood_safety\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_safety)\n",
"ORDER BY total_incidents DESC\n",
"LIMIT 15\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded top {len(df)} neighbourhoods by crime volume\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Select top 15 neighbourhoods by total incidents\n",
"2. Melt crime type columns into rows\n",
"3. Pass to stacked bar figure factory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_melted = df.melt(\n",
" id_vars=['neighbourhood_name', 'total_incidents'],\n",
" value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n",
" 'robbery_count', 'theft_over_count', 'homicide_count'],\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",
"\n",
"data = df_melted.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'assault_count', 'auto_theft_count', 'break_enter_count', 'total_incidents']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.bar_charts import create_stacked_bar\n",
"\n",
"fig = create_stacked_bar(\n",
" data=data,\n",
" x_column='neighbourhood_name',\n",
" value_column='count',\n",
" category_column='crime_type',\n",
" title='Crime Type Breakdown - Top 15 Neighbourhoods',\n",
" color_map={\n",
" 'Assault': '#d62728',\n",
" 'Auto Theft': '#ff7f0e',\n",
" 'Break Enter': '#9467bd',\n",
" 'Robbery': '#8c564b',\n",
" 'Theft Over': '#e377c2',\n",
" 'Homicide': '#1f77b4'\n",
" },\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MCI Categories\n",
"\n",
"| Category | Description |\n",
"|----------|------------|\n",
"| Assault | Physical attacks |\n",
"| Auto Theft | Vehicle theft |\n",
"| Break & Enter | Burglary |\n",
"| Robbery | Theft with force/threat |\n",
"| Theft Over | Theft > $5,000 |\n",
"| Homicide | Murder/manslaughter |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Crime Rate Choropleth Map\n",
"\n",
"Displays crime rates per 100,000 population across Toronto's 158 neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_safety` | neighbourhood × year | crime_rate_per_100k, crime_index, safety_tier, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_id,\n",
" neighbourhood_name,\n",
" geometry,\n",
" year,\n",
" crime_rate_per_100k,\n",
" crime_index,\n",
" safety_tier,\n",
" total_incidents,\n",
" population\n",
"FROM mart_neighbourhood_safety\n",
"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_safety)\n",
"ORDER BY crime_rate_per_100k 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. Filter to most recent year\n",
"2. Convert geometry to GeoJSON\n",
"3. Use reversed color scale (green=low crime, red=high crime)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import json\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df,\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['neighbourhood_name', 'crime_rate_per_100k', 'crime_index', 'safety_tier', 'total_incidents']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
"\n",
"**Key Parameters:**\n",
"- `color_column`: 'crime_rate_per_100k'\n",
"- `color_scale`: 'RdYlGn_r' (red=high crime, green=low crime)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key='neighbourhood_id',\n",
" color_column='crime_rate_per_100k',\n",
" hover_data=['neighbourhood_name', 'crime_index', 'total_incidents'],\n",
" color_scale='RdYlGn_r',\n",
" title='Toronto Crime Rate per 100,000 Population',\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Safety Tier Interpretation\n",
"\n",
"| Tier | Meaning |\n",
"|------|--------|\n",
"| 1 | Highest crime (top 20%) |\n",
"| 2-4 | Middle tiers |\n",
"| 5 | Lowest crime (bottom 20%) |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Crime Trend Line Chart\n",
"\n",
"Shows 5-year crime rate trends across Toronto."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_safety` | neighbourhood × year | year, crime_rate_per_100k, crime_yoy_change_pct |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine\n",
"import os\n",
"\n",
"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" year,\n",
" AVG(crime_rate_per_100k) as avg_crime_rate,\n",
" AVG(assault_rate_per_100k) as avg_assault_rate,\n",
" AVG(auto_theft_rate_per_100k) as avg_auto_theft_rate,\n",
" AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n",
" SUM(total_incidents) as total_city_incidents,\n",
" AVG(crime_yoy_change_pct) as avg_yoy_change\n",
"FROM mart_neighbourhood_safety\n",
"WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_safety)\n",
"GROUP BY year\n",
"ORDER BY year\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} years of crime data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Aggregate by year (city-wide)\n",
"2. Convert year to datetime\n",
"3. Melt for multi-line by crime type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"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",
" value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n",
" var_name='crime_type',\n",
" value_name='rate_per_100k'\n",
")\n",
"\n",
"df_melted['crime_type'] = df_melted['crime_type'].map({\n",
" 'avg_assault_rate': 'Assault',\n",
" 'avg_auto_theft_rate': 'Auto Theft',\n",
" 'avg_break_enter_rate': 'Break & Enter'\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['year', 'avg_crime_rate', 'total_city_incidents', 'avg_yoy_change']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_price_time_series` (reused for any numeric trend)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../..')\n",
"\n",
"from portfolio_app.figures.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",
" price_column='rate_per_100k',\n",
" group_column='crime_type',\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",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overall Trend"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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",
"\n",
"fig2 = create_price_time_series(\n",
" data=total_data,\n",
" date_column='date',\n",
" price_column='total_rate',\n",
" title='Toronto Overall Crime Rate Trend',\n",
")\n",
"fig2.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
"fig2.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}