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/safety/crime_trend_line.ipynb
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notebooks/safety/crime_trend_line.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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"# Crime Trend Line Chart\n",
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"\n",
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"Shows 5-year crime rate trends across Toronto."
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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_safety` | neighbourhood × year | year, crime_rate_per_100k, crime_yoy_change_pct |\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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" year,\n",
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" AVG(crime_rate_per_100k) as avg_crime_rate,\n",
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" AVG(assault_rate_per_100k) as avg_assault_rate,\n",
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" AVG(auto_theft_rate_per_100k) as avg_auto_theft_rate,\n",
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" AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n",
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" SUM(total_incidents) as total_city_incidents,\n",
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" AVG(crime_yoy_change_pct) as avg_yoy_change\n",
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"FROM mart_neighbourhood_safety\n",
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"WHERE year >= (SELECT MAX(year) - 5 FROM mart_neighbourhood_safety)\n",
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"GROUP BY year\n",
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"ORDER BY year\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)} years of crime data\")"
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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. Aggregate by year (city-wide)\n",
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"2. Convert year to datetime\n",
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"3. Melt for multi-line by crime type"
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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['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n",
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"\n",
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"# Melt for multi-line\n",
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"df_melted = df.melt(\n",
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" id_vars=['year', 'date'],\n",
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" value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n",
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" var_name='crime_type',\n",
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" value_name='rate_per_100k'\n",
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")\n",
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"\n",
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"df_melted['crime_type'] = df_melted['crime_type'].map({\n",
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" 'avg_assault_rate': 'Assault',\n",
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" 'avg_auto_theft_rate': 'Auto Theft',\n",
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" 'avg_break_enter_rate': 'Break & Enter'\n",
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"})"
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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[['year', 'avg_crime_rate', 'total_city_incidents', 'avg_yoy_change']]"
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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_price_time_series` (reused for any numeric trend)."
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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.time_series import create_price_time_series\n",
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"\n",
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"data = df_melted.to_dict('records')\n",
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"\n",
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"fig = create_price_time_series(\n",
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" data=data,\n",
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" date_column='date',\n",
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" price_column='rate_per_100k',\n",
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" group_column='crime_type',\n",
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" title='Toronto Crime Trends by Type (5 Years)',\n",
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")\n",
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"\n",
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"# Remove dollar sign formatting since this is rate data\n",
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"fig.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\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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"### Overall Trend"
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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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"# Total crime rate trend\n",
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"total_data = df[['date', 'avg_crime_rate']].rename(columns={'avg_crime_rate': 'total_rate'}).to_dict('records')\n",
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"\n",
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"fig2 = create_price_time_series(\n",
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" data=total_data,\n",
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" date_column='date',\n",
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" price_column='total_rate',\n",
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" title='Toronto Overall Crime Rate Trend',\n",
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")\n",
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"fig2.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n",
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"fig2.show()"
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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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