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docs: Complete Phase 6 notebooks and Phase 7 documentation review

Browse Source 
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>
This commit is contained in:
Leo Miranda
2026-01-17 12:10:46 -05:00
parent c9cf744d84
commit 1eba95d4d1
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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
}