docs: Complete Phase 6 notebooks and Phase 7 documentation review
Phase 6 - Jupyter Notebooks (15 total): - Overview tab: livability_choropleth, top_bottom_10_bar, income_safety_scatter - Housing tab: affordability_choropleth, rent_trend_line, tenure_breakdown_bar - Safety tab: crime_rate_choropleth, crime_breakdown_bar, crime_trend_line - Demographics tab: income_choropleth, age_distribution, population_density_bar - Amenities tab: amenity_index_choropleth, amenity_radar, transit_accessibility_bar Phase 7 - Documentation: - Updated CLAUDE.md with Sprint 9 completion status - Added notebooks directory to application structure - Expanded figures directory listing Closes #71, #72, #73, #74, #75, #76, #77 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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notebooks/housing/affordability_choropleth.ipynb
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notebooks/housing/affordability_choropleth.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Affordability Index Choropleth Map\n",
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"\n",
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"Displays housing affordability across Toronto's 158 neighbourhoods. Index of 100 = city average."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Data Reference\n",
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"\n",
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"### Source Tables\n",
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"\n",
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"| Table | Grain | Key Columns |\n",
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"|-------|-------|-------------|\n",
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"| `mart_neighbourhood_housing` | neighbourhood × year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n",
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"\n",
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"### SQL Query"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sqlalchemy import create_engine\n",
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"import os\n",
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"\n",
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"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
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"\n",
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"query = \"\"\"\n",
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"SELECT\n",
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" neighbourhood_id,\n",
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" neighbourhood_name,\n",
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" geometry,\n",
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" year,\n",
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" affordability_index,\n",
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" rent_to_income_pct,\n",
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" avg_rent_2bed,\n",
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" median_household_income,\n",
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" is_affordable\n",
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"FROM mart_neighbourhood_housing\n",
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"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
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"ORDER BY affordability_index ASC\n",
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"\"\"\"\n",
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"\n",
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"df = pd.read_sql(query, engine)\n",
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"print(f\"Loaded {len(df)} neighbourhoods\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Transformation Steps\n",
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"\n",
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"1. Filter to most recent year\n",
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"2. Convert geometry to GeoJSON\n",
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"3. Lower index = more affordable (inverted for visualization clarity)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import geopandas as gpd\n",
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"import json\n",
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"\n",
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"gdf = gpd.GeoDataFrame(\n",
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" df,\n",
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" geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n",
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" crs='EPSG:4326'\n",
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")\n",
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"\n",
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"geojson = json.loads(gdf.to_json())\n",
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"data = df.drop(columns=['geometry']).to_dict('records')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sample Output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df[['neighbourhood_name', 'affordability_index', 'rent_to_income_pct', 'avg_rent_2bed', 'is_affordable']].head(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Data Visualization\n",
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"\n",
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"### Figure Factory\n",
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"\n",
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"Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n",
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"\n",
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"**Key Parameters:**\n",
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"- `color_column`: 'affordability_index'\n",
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"- `color_scale`: 'RdYlGn_r' (reversed: green=affordable, red=expensive)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.insert(0, '../..')\n",
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"\n",
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"from portfolio_app.figures.choropleth import create_choropleth_figure\n",
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"\n",
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"fig = create_choropleth_figure(\n",
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" geojson=geojson,\n",
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" data=data,\n",
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" location_key='neighbourhood_id',\n",
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" color_column='affordability_index',\n",
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" hover_data=['neighbourhood_name', 'rent_to_income_pct', 'avg_rent_2bed'],\n",
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" color_scale='RdYlGn_r', # Reversed: lower index (affordable) = green\n",
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" title='Toronto Housing Affordability Index',\n",
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" zoom=10,\n",
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")\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Index Interpretation\n",
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"\n",
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"| Index | Meaning |\n",
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"|-------|--------|\n",
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"| < 100 | More affordable than city average |\n",
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"| = 100 | City average affordability |\n",
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"| > 100 | Less affordable than city average |\n",
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"\n",
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"Affordability calculated as: `rent_to_income_pct / city_avg_rent_to_income * 100`"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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