refactor: multi-dashboard structural migration

- 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>

notebooks/toronto/safety/crime_breakdown_bar.ipynb

@@ -0,0 +1,200 @@
+{
+ "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
+}