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docs: Complete Phase 6 notebooks and Phase 7 documentation review

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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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notebooks/overview/income_safety_scatter.ipynb Normal file
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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
}