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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": [
"# 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
}