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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
20 changed files with 2711 additions and 4 deletions
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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
}