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_breakdown_bar.ipynb
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notebooks/safety/crime_breakdown_bar.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 Type Breakdown Bar Chart\n",
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"\n",
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"Stacked bar chart showing crime composition by Major Crime Indicator (MCI) categories."
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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 | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\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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" neighbourhood_name,\n",
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" assault_count,\n",
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" auto_theft_count,\n",
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" break_enter_count,\n",
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" robbery_count,\n",
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" theft_over_count,\n",
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" homicide_count,\n",
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" total_incidents,\n",
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" crime_rate_per_100k\n",
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"FROM mart_neighbourhood_safety\n",
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"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_safety)\n",
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"ORDER BY total_incidents DESC\n",
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"LIMIT 15\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 top {len(df)} neighbourhoods by crime volume\")"
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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. Select top 15 neighbourhoods by total incidents\n",
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"2. Melt crime type columns into rows\n",
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"3. Pass to stacked bar figure factory"
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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_melted = df.melt(\n",
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" id_vars=['neighbourhood_name', 'total_incidents'],\n",
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" value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n",
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" 'robbery_count', 'theft_over_count', 'homicide_count'],\n",
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" var_name='crime_type',\n",
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" value_name='count'\n",
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")\n",
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"\n",
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"# Clean labels\n",
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"df_melted['crime_type'] = df_melted['crime_type'].str.replace('_count', '').str.replace('_', ' ').str.title()\n",
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"\n",
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"data = df_melted.to_dict('records')"
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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[['neighbourhood_name', 'assault_count', 'auto_theft_count', 'break_enter_count', 'total_incidents']].head(10)"
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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_stacked_bar` from `portfolio_app.figures.bar_charts`."
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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.bar_charts import create_stacked_bar\n",
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"\n",
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"fig = create_stacked_bar(\n",
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" data=data,\n",
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" x_column='neighbourhood_name',\n",
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" value_column='count',\n",
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" category_column='crime_type',\n",
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" title='Crime Type Breakdown - Top 15 Neighbourhoods',\n",
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" color_map={\n",
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" 'Assault': '#d62728',\n",
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" 'Auto Theft': '#ff7f0e',\n",
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" 'Break Enter': '#9467bd',\n",
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" 'Robbery': '#8c564b',\n",
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" 'Theft Over': '#e377c2',\n",
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" 'Homicide': '#1f77b4'\n",
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" },\n",
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")\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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"### MCI Categories\n",
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"\n",
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"| Category | Description |\n",
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"|----------|------------|\n",
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"| Assault | Physical attacks |\n",
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"| Auto Theft | Vehicle theft |\n",
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"| Break & Enter | Burglary |\n",
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"| Robbery | Theft with force/threat |\n",
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"| Theft Over | Theft > $5,000 |\n",
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"| Homicide | Murder/manslaughter |"
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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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