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refactor: multi-dashboard structural migration
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- Rename dbt project from toronto_housing to portfolio
- Restructure dbt models into domain subdirectories:
  - shared/ for cross-domain dimensions (dim_time)
  - staging/toronto/, intermediate/toronto/, marts/toronto/
- Update SQLAlchemy models for raw_toronto schema
- Add explicit cross-schema FK relationships for FactRentals
- Namespace figure factories under figures/toronto/
- Namespace notebooks under notebooks/toronto/
- Update Makefile with domain-specific targets and env loading
- Update all documentation for multi-dashboard structure

This enables adding new dashboard projects (e.g., /football, /energy)
without structural conflicts or naming collisions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Leo Miranda
2026-02-01 19:08:20 -05:00
parent a5d6866d63
commit 62d1a52eed
73 changed files with 1114 additions and 623 deletions
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notebooks/toronto/safety/.gitkeep Normal file
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notebooks/toronto/safety/crime_breakdown_bar.ipynb Normal file
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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 os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\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 public_marts.mart_neighbourhood_safety\n",
"WHERE year = (SELECT MAX(year) FROM public_marts.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=[\n",
" \"assault_count\",\n",
" \"auto_theft_count\",\n",
" \"break_enter_count\",\n",
" \"robbery_count\",\n",
" \"theft_over_count\",\n",
" \"homicide_count\",\n",
" ],\n",
" var_name=\"crime_type\",\n",
" value_name=\"count\",\n",
")\n",
"\n",
"# Clean labels\n",
"df_melted[\"crime_type\"] = (\n",
" df_melted[\"crime_type\"].str.replace(\"_count\", \"\").str.replace(\"_\", \" \").str.title()\n",
")\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[\n",
" [\n",
" \"neighbourhood_name\",\n",
" \"assault_count\",\n",
" \"auto_theft_count\",\n",
" \"break_enter_count\",\n",
" \"total_incidents\",\n",
" ]\n",
"].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_stacked_bar` from `portfolio_app.figures.toronto.bar_charts`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.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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notebooks/toronto/safety/crime_rate_choropleth.ipynb Normal file
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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 os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\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 public_marts.mart_neighbourhood_safety\n",
"WHERE year = (SELECT MAX(year) FROM public_marts.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 json\n",
"\n",
"import geopandas as gpd\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), 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[\n",
" [\n",
" \"neighbourhood_name\",\n",
" \"crime_rate_per_100k\",\n",
" \"crime_index\",\n",
" \"safety_tier\",\n",
" \"total_incidents\",\n",
" ]\n",
"].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.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",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.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 os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\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 public_marts.mart_neighbourhood_safety\n",
"WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.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",
" {\n",
" \"avg_assault_rate\": \"Assault\",\n",
" \"avg_auto_theft_rate\": \"Auto Theft\",\n",
" \"avg_break_enter_rate\": \"Break & Enter\",\n",
" }\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",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.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 = (\n",
" df[[\"date\", \"avg_crime_rate\"]]\n",
" .rename(columns={\"avg_crime_rate\": \"total_rate\"})\n",
" .to_dict(\"records\")\n",
")\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
}