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DBbun_Davis_ML_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4e333c36",
+   "metadata": {},
+   "source": [
+    "# DBbun Davis — Machine Learning Demo\n",
+    "This notebook shows how to build ML models using the synthetic **Davis Square** dataset.\n",
+    "\n",
+    "We’ll do three examples:\n",
+    "1. **Classification:** predict whether a public safety incident is **high severity** using incident, weather, and street features.\n",
+    "2. **Regression:** predict **noise (dB)** from weather and traffic context.\n",
+    "3. **Clustering:** group streets by **mobility signature** (mode mix) using K-Means.\n",
+    "\n",
+    "All **figures, tables, and models** are saved locally.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4f2d9fff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --- User config ---\n",
+    "#DATA_DIR = \"./out_dbbun_davis_medium\"  # Set to your dataset folder\n",
+    "DATA_DIR = \"./\"\n",
+    "FIGS_DIR = \"./ml_figs\"\n",
+    "TABLES_DIR = \"./ml_tables\"\n",
+    "MODELS_DIR = \"./ml_models\"\n",
+    "\n",
+    "# --- Imports ---\n",
+    "import os, json, pickle, re\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, RocCurveDisplay, mean_absolute_error, r2_score\n",
+    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
+    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "os.makedirs(FIGS_DIR, exist_ok=True)\n",
+    "os.makedirs(TABLES_DIR, exist_ok=True)\n",
+    "os.makedirs(MODELS_DIR, exist_ok=True)\n",
+    "\n",
+    "def savefig(name):\n",
+    "    path = os.path.join(FIGS_DIR, name)\n",
+    "    plt.savefig(path, bbox_inches=\"tight\", dpi=144)\n",
+    "    plt.close()\n",
+    "    print(f\"Saved figure: {path}\")\n",
+    "\n",
+    "def savetab(df, name):\n",
+    "    path = os.path.join(TABLES_DIR, name)\n",
+    "    df.to_csv(path, index=False)\n",
+    "    print(f\"Saved table: {path}\")\n",
+    "\n",
+    "def savemodel(model, name):\n",
+    "    path = os.path.join(MODELS_DIR, name)\n",
+    "    with open(path, \"wb\") as f:\n",
+    "        pickle.dump(model, f)\n",
+    "    print(f\"Saved model: {path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fce74eac-3c7b-4f5b-85f5-2e7b0a9fbdb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0fd4f78b-44e7-424d-8f1c-3bbc29468cba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2a2efd9d-75b0-43f4-894b-f7347e3febdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('future.no_silent_downcasting', True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0ac6980b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded tables:\n",
+      " streets: (15, 7) | safety: (12000, 8) | obs: (35000, 7) | weather: (101136, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "def load_csv(name):\n",
+    "    return pd.read_csv(os.path.join(DATA_DIR, name))\n",
+    "\n",
+    "# Helper for WKT point -> (lon,lat)\n",
+    "def wkt_point_to_lonlat(wkt):\n",
+    "    m = re.match(r\"POINT\\(([-0-9\\.]+)\\s+([-0-9\\.]+)\\)\", str(wkt))\n",
+    "    if not m:\n",
+    "        return np.nan, np.nan\n",
+    "    lon, lat = float(m.group(1)), float(m.group(2))\n",
+    "    return lon, lat\n",
+    "\n",
+    "# Tables\n",
+    "streets = load_csv(\"geo_streets.csv\")\n",
+    "parks = load_csv(\"geo_parks.csv\")\n",
+    "poi = load_csv(\"poi_generic.csv\")\n",
+    "households = load_csv(\"households.csv\")\n",
+    "pets = load_csv(\"pets_registry.csv\")\n",
+    "pet_inc = load_csv(\"pet_incidents.csv\")\n",
+    "trips = load_csv(\"mobility_trips.csv\")\n",
+    "safety = load_csv(\"public_safety.csv\")\n",
+    "events = load_csv(\"events_civic.csv\")\n",
+    "obs = load_csv(\"observations.csv\")\n",
+    "lines = load_csv(\"transit_lines.csv\")\n",
+    "stops = load_csv(\"transit_stops.csv\")\n",
+    "ridership = load_csv(\"transit_ridership_daily.csv\")\n",
+    "bike = load_csv(\"bike_infra.csv\")\n",
+    "traffic = load_csv(\"traffic_counts.csv\")\n",
+    "prices = load_csv(\"prices_index.csv\")\n",
+    "weather = load_csv(\"weather_daily.csv\")\n",
+    "trees = load_csv(\"trees_inventory.csv\")\n",
+    "infra = load_csv(\"infrastructure_events.csv\")\n",
+    "bissues = load_csv(\"building_issues.csv\")\n",
+    "\n",
+    "with open(os.path.join(DATA_DIR, \"DATA_DICTIONARY.json\"), \"r\", encoding=\"utf-8\") as f:\n",
+    "    dd = json.load(f)\n",
+    "\n",
+    "# Basic sanity\n",
+    "print(\"Loaded tables:\")\n",
+    "print(\" streets:\", streets.shape, \"| safety:\", safety.shape, \"| obs:\", obs.shape, \"| weather:\", weather.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2a457810",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nearest_year_join(left_df, left_year_col, right_df, right_year_col, on_key_col):\n",
+    "    # For each (on_key_col, year) in left_df, find the right_df row with smallest |year diff|\n",
+    "    # This is a simple but effective approach for decimated time series.\n",
+    "    right_sorted = right_df.sort_values([on_key_col, right_year_col]).copy()\n",
+    "    out_rows = []\n",
+    "    for (k, y), block in left_df.groupby([on_key_col, left_year_col]):\n",
+    "        cand = right_sorted[right_sorted[on_key_col]==k]\n",
+    "        if cand.empty:\n",
+    "            # no match for this key\n",
+    "            tmp = block.copy()\n",
+    "            tmp[\"__ry\"] = np.nan\n",
+    "            tmp[\"__ridx\"] = -1\n",
+    "            out_rows.append(tmp)\n",
+    "            continue\n",
+    "        # pick nearest by year\n",
+    "        ridx = int((cand[right_year_col] - y).abs().idxmin())\n",
+    "        tmp = block.copy()\n",
+    "        tmp[\"__ry\"] = cand.loc[ridx, right_year_col]\n",
+    "        tmp[\"__ridx\"] = ridx\n",
+    "        out_rows.append(tmp)\n",
+    "    joined_left = pd.concat(out_rows, axis=0)\n",
+    "\n",
+    "    # Attach right_df columns by __ridx where available\n",
+    "    joined = joined_left.merge(right_df.add_prefix(\"r__\"), left_on=\"__ridx\", right_index=True, how=\"left\")\n",
+    "    joined = joined.drop(columns=[\"__ridx\"])\n",
+    "    return joined"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff73a6ff",
+   "metadata": {},
+   "source": [
+    "## 1) Classification: Predict high-severity public safety incidents\n",
+    "**Target:** `severity` mapped to binary: *high* vs *not-high*  \n",
+    "**Features:** incident `category`, `reported_by`, street attributes (bike infra presence), daily weather (`temp_c`, `precip_mm`, `snow_cm`, `wind_kph`, `heatwave`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0a9bb0f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.67      0.82      0.74      2026\n",
+      "           1       0.30      0.16      0.21       974\n",
+      "\n",
+      "    accuracy                           0.61      3000\n",
+      "   macro avg       0.49      0.49      0.47      3000\n",
+      "weighted avg       0.55      0.61      0.57      3000\n",
+      "\n",
+      "Confusion matrix:\n",
+      " [[1671  355]\n",
+      " [ 820  154]]\n",
+      "ROC AUC: 0.4951799603106231\n",
+      "Saved figure: ./ml_figs\\clf_roc_curve.png\n",
+      "Saved model: ./ml_models\\incident_severity_rf.pkl\n",
+      "Saved table: ./ml_tables\\clf_metrics_incident_severity.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare safety data\n",
+    "safety['date'] = pd.to_datetime(safety['date'])\n",
+    "safety['year'] = safety['date'].dt.year\n",
+    "safety['month'] = safety['date'].dt.month\n",
+    "safety['day'] = safety['date'].dt.day\n",
+    "\n",
+    "# Target\n",
+    "y = (safety['severity'] == 'high').astype(int)\n",
+    "\n",
+    "# Merge weather on date\n",
+    "weather['date'] = pd.to_datetime(weather['date'])\n",
+    "s_w = safety.merge(weather[['date','temp_c','precip_mm','snow_cm','wind_kph','heatwave']], on='date', how='left')\n",
+    "\n",
+    "# Street-level bike infra presence (binary, ever installed before safety year)\n",
+    "bike_any = bike.copy()\n",
+    "bike_any['has_bike_infra'] = True\n",
+    "s_w = s_w.merge(\n",
+    "    bike_any[['street_id','has_bike_infra']].drop_duplicates('street_id'),\n",
+    "    left_on='location_street_id', right_on='street_id', how='left'\n",
+    ")\n",
+    "s_w['has_bike_infra'] = s_w['has_bike_infra'].fillna(False).infer_objects(copy=False)\n",
+    "\n",
+    "# Assemble features\n",
+    "X = s_w[['category','reported_by','has_bike_infra','temp_c','precip_mm','snow_cm','wind_kph','heatwave','month']].copy()\n",
+    "\n",
+    "# Split numeric/categorical\n",
+    "num_cols = ['temp_c','precip_mm','snow_cm','wind_kph','month']\n",
+    "cat_cols = ['category','reported_by','heatwave','has_bike_infra']\n",
+    "\n",
+    "pre = ColumnTransformer([\n",
+    "    (\"num\", StandardScaler(), num_cols),\n",
+    "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols)\n",
+    "])\n",
+    "\n",
+    "clf = Pipeline([\n",
+    "    (\"pre\", pre),\n",
+    "    (\"rf\", RandomForestClassifier(n_estimators=200, random_state=42, class_weight=\"balanced\"))\n",
+    "])\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluate\n",
+    "pred = clf.predict(X_test)\n",
+    "proba = clf.predict_proba(X_test)[:,1]\n",
+    "\n",
+    "print(classification_report(y_test, pred))\n",
+    "cm = confusion_matrix(y_test, pred)\n",
+    "print(\"Confusion matrix:\\n\", cm)\n",
+    "\n",
+    "# ROC AUC & curve\n",
+    "auc = roc_auc_score(y_test, proba)\n",
+    "print(\"ROC AUC:\", auc)\n",
+    "\n",
+    "disp = RocCurveDisplay.from_predictions(y_test, proba)\n",
+    "plt.title(\"ROC Curve — High Severity Incident Classifier\")\n",
+    "savefig(\"clf_roc_curve.png\")\n",
+    "\n",
+    "# Save model\n",
+    "savemodel(clf, \"incident_severity_rf.pkl\")\n",
+    "\n",
+    "# Save metrics as table\n",
+    "metrics_tbl = pd.DataFrame({\n",
+    "    \"roc_auc\":[auc],\n",
+    "    \"accuracy\":[(pred==y_test).mean()],\n",
+    "    \"n_test\":[len(y_test)]\n",
+    "})\n",
+    "savetab(metrics_tbl, \"clf_metrics_incident_severity.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78159761",
+   "metadata": {},
+   "source": [
+    "## 2) Regression: Predict street noise (dB)\n",
+    "**Target:** `observations` where `metric == \"noise_db\"` → `value` (dB).  \n",
+    "**Features:** weather (temp, wind, rain, snow, heatwave), month, traffic volumes (nearest-year by street).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "bb03cb92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE: 5.521102874867018  R^2: 0.3084211030160505\n",
+      "Saved figure: ./ml_figs\\regr_noise_pred_vs_actual.png\n",
+      "Saved model: ./ml_models\\noise_rf.pkl\n",
+      "Saved table: ./ml_tables\\regr_noise_metrics.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter observations to noise\n",
+    "noise = obs[obs['metric']=='noise_db'].copy()\n",
+    "noise['date'] = pd.to_datetime(noise['date'])\n",
+    "noise['year'] = noise['date'].dt.year\n",
+    "noise['month'] = noise['date'].dt.month\n",
+    "\n",
+    "# Merge weather\n",
+    "weather['date'] = pd.to_datetime(weather['date'])\n",
+    "nz = noise.merge(weather[['date','temp_c','precip_mm','snow_cm','wind_kph','heatwave']], on='date', how='left')\n",
+    "\n",
+    "# Traffic is every ~5 years; map to nearest year by street\n",
+    "traffic['year'] = pd.to_datetime(traffic['date']).dt.year\n",
+    "nz_key = nz[['location_street_id','year']].copy()\n",
+    "nz_key = nz_key.rename(columns={'location_street_id':'street_id'})\n",
+    "traffic_near = nearest_year_join(nz_key, 'year', traffic[['street_id','year','vehicles_per_day','bikes_per_day','peds_per_day']], 'year', 'street_id')\n",
+    "\n",
+    "# Merge traffic back by index alignment\n",
+    "nz = nz.join(traffic_near[['r__street_id','r__year','r__vehicles_per_day','r__bikes_per_day','r__peds_per_day']].reset_index(drop=True))\n",
+    "# If missing traffic (e.g., older years), fill with medians\n",
+    "for c in ['r__vehicles_per_day','r__bikes_per_day','r__peds_per_day']:\n",
+    "    nz[c] = nz[c].fillna(nz[c].median())\n",
+    "\n",
+    "# Prepare features/target\n",
+    "y = nz['value'].astype(float)  # noise dB\n",
+    "X = nz[['temp_c','precip_mm','snow_cm','wind_kph','heatwave','month','r__vehicles_per_day','r__bikes_per_day','r__peds_per_day']].copy()\n",
+    "\n",
+    "num_cols = ['temp_c','precip_mm','snow_cm','wind_kph','month','r__vehicles_per_day','r__bikes_per_day','r__peds_per_day']\n",
+    "cat_cols = ['heatwave']\n",
+    "\n",
+    "pre = ColumnTransformer([\n",
+    "    (\"num\", StandardScaler(), num_cols),\n",
+    "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols)\n",
+    "])\n",
+    "\n",
+    "regr = Pipeline([\n",
+    "    (\"pre\", pre),\n",
+    "    (\"rf\", RandomForestRegressor(n_estimators=200, random_state=42))\n",
+    "])\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
+    "regr.fit(X_train, y_train)\n",
+    "pred = regr.predict(X_test)\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, pred)\n",
+    "r2 = r2_score(y_test, pred)\n",
+    "print(\"MAE:\", mae, \" R^2:\", r2)\n",
+    "\n",
+    "# Scatter: predicted vs actual\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.scatter(y_test, pred, alpha=0.3)\n",
+    "plt.xlabel(\"Actual dB\"); plt.ylabel(\"Predicted dB\")\n",
+    "plt.title(\"Noise Regression — Predicted vs Actual\")\n",
+    "savefig(\"regr_noise_pred_vs_actual.png\")\n",
+    "\n",
+    "# Save model & metrics\n",
+    "savemodel(regr, \"noise_rf.pkl\")\n",
+    "savetab(pd.DataFrame({\"MAE\":[mae], \"R2\":[r2], \"n_test\":[len(y_test)]}), \"regr_noise_metrics.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11a13962",
+   "metadata": {},
+   "source": [
+    "## 3) Clustering: Streets by mobility signature\n",
+    "We compute, for each street, a **mode share vector** based on trips that originate on that street.  \n",
+    "Then we cluster streets using **K-Means** and visualize cluster sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8b77b4d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./ml_figs\\cluster_elbow.png\n",
+      "Saved table: ./ml_tables\\cluster_sizes.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\karto\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>n_streets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cluster  n_streets\n",
+       "0        0          3\n",
+       "1        1          2\n",
+       "2        2          5\n",
+       "3        3          5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./ml_figs\\cluster_sizes_bar.png\n",
+      "Saved table: ./ml_tables\\cluster_assignments.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build mode share per street (origin)\n",
+    "mode_pvt = trips.pivot_table(index='origin_street_id', columns='mode', values='trip_id', aggfunc='count', fill_value=0)\n",
+    "mode_share = mode_pvt.div(mode_pvt.sum(axis=1).replace(0,1), axis=0)\n",
+    "\n",
+    "# Try several k and pick a reasonable one (elbow heuristic)\n",
+    "inertias = []\n",
+    "ks = list(range(2, 9))\n",
+    "for k in ks:\n",
+    "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+    "    km.fit(mode_share)\n",
+    "    inertias.append(km.inertia_)\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "plt.plot(ks, inertias, marker=\"o\")\n",
+    "plt.xlabel(\"k\"); plt.ylabel(\"Inertia\")\n",
+    "plt.title(\"K-Means Elbow — Mobility Mode Share by Street\")\n",
+    "savefig(\"cluster_elbow.png\")\n",
+    "\n",
+    "# Choose k=4 as a reasonable choice for demonstration\n",
+    "k = 4\n",
+    "km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+    "labels = km.fit_predict(mode_share)\n",
+    "mode_share_clusters = mode_share.copy()\n",
+    "mode_share_clusters['cluster'] = labels\n",
+    "\n",
+    "# Summarize clusters\n",
+    "cluster_sizes = mode_share_clusters['cluster'].value_counts().sort_index().reset_index()\n",
+    "cluster_sizes.columns = ['cluster','n_streets']\n",
+    "savetab(cluster_sizes, \"cluster_sizes.csv\")\n",
+    "display(cluster_sizes)\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "plt.bar(cluster_sizes['cluster'], cluster_sizes['n_streets'])\n",
+    "plt.xlabel(\"Cluster\"); plt.ylabel(\"Number of streets\")\n",
+    "plt.title(\"Street Clusters by Mobility Signature\")\n",
+    "savefig(\"cluster_sizes_bar.png\")\n",
+    "\n",
+    "# Save cluster assignments\n",
+    "assignments = mode_share_clusters.reset_index()[['origin_street_id','cluster']]\n",
+    "savetab(assignments, \"cluster_assignments.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dc1e2af",
+   "metadata": {},
+   "source": [
+    "### Bonus: Interpreting models\n",
+    "As a quick interpretability pass, we can look at feature importance from the random forests (MDI)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "73f43632",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>temp_c</td>\n",
+       "      <td>0.302091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wind_kph</td>\n",
+       "      <td>0.219432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>month</td>\n",
+       "      <td>0.188248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>precip_mm</td>\n",
+       "      <td>0.082657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>snow_cm</td>\n",
+       "      <td>0.044882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reported_by_sensor</td>\n",
+       "      <td>0.017501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>reported_by_officer</td>\n",
+       "      <td>0.016649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>reported_by_citizen</td>\n",
+       "      <td>0.016428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>category_animal</td>\n",
+       "      <td>0.012986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>category_theft</td>\n",
+       "      <td>0.012825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>category_disturbance</td>\n",
+       "      <td>0.012002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>category_noise</td>\n",
+       "      <td>0.011956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>category_other</td>\n",
+       "      <td>0.011694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>category_vandalism</td>\n",
+       "      <td>0.010977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>category_traffic</td>\n",
+       "      <td>0.010530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>has_bike_infra_True</td>\n",
+       "      <td>0.010157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>has_bike_infra_False</td>\n",
+       "      <td>0.009653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>heatwave_False</td>\n",
+       "      <td>0.004153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>heatwave_nan</td>\n",
+       "      <td>0.002672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>heatwave_True</td>\n",
+       "      <td>0.002508</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 feature  importance\n",
+       "0                 temp_c    0.302091\n",
+       "1               wind_kph    0.219432\n",
+       "2                  month    0.188248\n",
+       "3              precip_mm    0.082657\n",
+       "4                snow_cm    0.044882\n",
+       "5     reported_by_sensor    0.017501\n",
+       "6    reported_by_officer    0.016649\n",
+       "7    reported_by_citizen    0.016428\n",
+       "8        category_animal    0.012986\n",
+       "9         category_theft    0.012825\n",
+       "10  category_disturbance    0.012002\n",
+       "11        category_noise    0.011956\n",
+       "12        category_other    0.011694\n",
+       "13    category_vandalism    0.010977\n",
+       "14      category_traffic    0.010530\n",
+       "15   has_bike_infra_True    0.010157\n",
+       "16  has_bike_infra_False    0.009653\n",
+       "17        heatwave_False    0.004153\n",
+       "18          heatwave_nan    0.002672\n",
+       "19         heatwave_True    0.002508"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./ml_tables\\clf_top_feature_importances.csv\n",
+      "Saved figure: ./ml_figs\\clf_feature_importances.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>precip_mm</td>\n",
+       "      <td>0.231075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>temp_c</td>\n",
+       "      <td>0.223292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>snow_cm</td>\n",
+       "      <td>0.115154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>wind_kph</td>\n",
+       "      <td>0.098303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>month</td>\n",
+       "      <td>0.075172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>r__vehicles_per_day</td>\n",
+       "      <td>0.069528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>r__bikes_per_day</td>\n",
+       "      <td>0.068437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>r__peds_per_day</td>\n",
+       "      <td>0.066777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>heatwave_nan</td>\n",
+       "      <td>0.048992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>heatwave_False</td>\n",
+       "      <td>0.001682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>heatwave_True</td>\n",
+       "      <td>0.001590</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                feature  importance\n",
+       "0             precip_mm    0.231075\n",
+       "1                temp_c    0.223292\n",
+       "2               snow_cm    0.115154\n",
+       "3              wind_kph    0.098303\n",
+       "4                 month    0.075172\n",
+       "5   r__vehicles_per_day    0.069528\n",
+       "6      r__bikes_per_day    0.068437\n",
+       "7       r__peds_per_day    0.066777\n",
+       "8          heatwave_nan    0.048992\n",
+       "9        heatwave_False    0.001682\n",
+       "10        heatwave_True    0.001590"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./ml_tables\\regr_top_feature_importances.csv\n",
+      "Saved figure: ./ml_figs\\regr_feature_importances.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract MDI importances from pipelines (requires access to transformed feature names)\n",
+    "def get_feature_names(preprocessor, num_cols, cat_cols, X):\n",
+    "    num_feats = num_cols\n",
+    "    # OneHot names\n",
+    "    oh = preprocessor.named_transformers_['cat']\n",
+    "    cat_names = list(oh.get_feature_names_out(cat_cols))\n",
+    "    return list(num_feats) + cat_names\n",
+    "\n",
+    "# Classification importances\n",
+    "try:\n",
+    "    clf_model = clf.named_steps['rf']\n",
+    "    feat_names = get_feature_names(clf.named_steps['pre'], \n",
+    "                                   ['temp_c','precip_mm','snow_cm','wind_kph','month'],\n",
+    "                                   ['category','reported_by','heatwave','has_bike_infra'],\n",
+    "                                   X_train)\n",
+    "    imp = pd.Series(clf_model.feature_importances_, index=feat_names).sort_values(ascending=False)\n",
+    "    top_imp = imp.head(20).reset_index()\n",
+    "    top_imp.columns = ['feature','importance']\n",
+    "    display(top_imp)\n",
+    "    savetab(top_imp, \"clf_top_feature_importances.csv\")\n",
+    "\n",
+    "    plt.figure(figsize=(8,5))\n",
+    "    plt.barh(top_imp['feature'][::-1], top_imp['importance'][::-1])\n",
+    "    plt.title(\"Top Feature Importances — Incident Severity RF\")\n",
+    "    plt.xlabel(\"Importance\")\n",
+    "    savefig(\"clf_feature_importances.png\")\n",
+    "except Exception as e:\n",
+    "    print(\"Could not compute classification importances:\", e)\n",
+    "\n",
+    "# Regression importances\n",
+    "try:\n",
+    "    regr_model = regr.named_steps['rf']\n",
+    "    feat_names = get_feature_names(regr.named_steps['pre'],\n",
+    "                                   ['temp_c','precip_mm','snow_cm','wind_kph','month','r__vehicles_per_day','r__bikes_per_day','r__peds_per_day'],\n",
+    "                                   ['heatwave'],\n",
+    "                                   X_train)\n",
+    "    imp = pd.Series(regr_model.feature_importances_, index=feat_names).sort_values(ascending=False)\n",
+    "    top_imp = imp.head(20).reset_index()\n",
+    "    top_imp.columns = ['feature','importance']\n",
+    "    display(top_imp)\n",
+    "    savetab(top_imp, \"regr_top_feature_importances.csv\")\n",
+    "\n",
+    "    plt.figure(figsize=(8,5))\n",
+    "    plt.barh(top_imp['feature'][::-1], top_imp['importance'][::-1])\n",
+    "    plt.title(\"Top Feature Importances — Noise RF\")\n",
+    "    plt.xlabel(\"Importance\")\n",
+    "    savefig(\"regr_feature_importances.png\")\n",
+    "except Exception as e:\n",
+    "    print(\"Could not compute regression importances:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0963228d",
+   "metadata": {},
+   "source": [
+    "### Next ideas\n",
+    "- Use **time-based splits** (train on early years, test on later) for more realistic forecasting.\n",
+    "- Try **Gradient Boosting** or **XGBoost/LightGBM** for stronger baselines.\n",
+    "- Add **spatial features** (distance to parks, density of POIs) for richer models.\n",
+    "- Package results into an **HTML report** or a small **Streamlit** app."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

DBbun_Davis_analytics_demo.ipynb

@@ -0,0 +1,1591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b72504e7",
+   "metadata": {},
+   "source": [
+    "# DBbun Davis — Medium Demo Notebook\n",
+    "This notebook loads the synthetic **Davis Square** dataset and produces attractive, saved-to-disk figures and tables.\n",
+    "\n",
+    "## How to use\n",
+    "1. Generate the dataset first (recommended **medium** size):  \n",
+    "   ```bash\n",
+    "   python make_davis_square.py --size medium --seed 42 --out ./out_dbbun_davis_medium\n",
+    "   ```\n",
+    "2. Update `DATA_DIR` below to point to your output folder.\n",
+    "3. Run all cells. All figures will be saved to `FIGS_DIR`, and summary tables to `TABLES_DIR`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2ba10144",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All expected files found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- User config ---\n",
+    "#DATA_DIR = \"./out_dbbun_davis_medium\"  # <-- change if needed\n",
+    "DATA_DIR = \"./\"\n",
+    "FIGS_DIR = \"./figs\"\n",
+    "TABLES_DIR = \"./tables\"\n",
+    "\n",
+    "# --- Imports & setup ---\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "\n",
+    "os.makedirs(FIGS_DIR, exist_ok=True)\n",
+    "os.makedirs(TABLES_DIR, exist_ok=True)\n",
+    "\n",
+    "def savefig(name):\n",
+    "    path = os.path.join(FIGS_DIR, name)\n",
+    "    plt.savefig(path, bbox_inches=\"tight\", dpi=144)\n",
+    "    plt.close()\n",
+    "    print(f\"Saved figure: {path}\")\n",
+    "\n",
+    "def savetab(df, name):\n",
+    "    path = os.path.join(TABLES_DIR, name)\n",
+    "    df.to_csv(path, index=False)\n",
+    "    print(f\"Saved table: {path}\")\n",
+    "\n",
+    "# Verify required files exist\n",
+    "required_files = [\n",
+    "    \"geo_streets.csv\",\"geo_parks.csv\",\"poi_generic.csv\",\"households.csv\",\n",
+    "    \"pets_registry.csv\",\"pet_incidents.csv\",\"mobility_trips.csv\",\"public_safety.csv\",\n",
+    "    \"events_civic.csv\",\"observations.csv\",\"transit_lines.csv\",\"transit_stops.csv\",\n",
+    "    \"transit_ridership_daily.csv\",\"bike_infra.csv\",\"traffic_counts.csv\",\n",
+    "    \"prices_index.csv\",\"weather_daily.csv\",\"trees_inventory.csv\",\n",
+    "    \"infrastructure_events.csv\",\"building_issues.csv\",\"DATA_DICTIONARY.json\"\n",
+    "]\n",
+    "missing = [f for f in required_files if not os.path.exists(os.path.join(DATA_DIR, f))]\n",
+    "if missing:\n",
+    "    print(\"WARNING: Missing files (update DATA_DIR or regenerate dataset):\")\n",
+    "    for m in missing:\n",
+    "        print(\" -\", m)\n",
+    "else:\n",
+    "    print(\"All expected files found.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "39136d91-c63f-4e18-b8c5-fd77f95305bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['.ipynb_checkpoints', 'bike_infra.csv', 'building_issues.csv', 'DATA_DICTIONARY.json', 'DBbun_Davis_medium_demo.ipynb', 'events_civic.csv', 'figs', 'geo_parks.csv', 'geo_streets.csv', 'households.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "print(os.listdir(DATA_DIR)[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8bcd1b10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>table</th>\n",
+       "      <th>rows</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>streets</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>parks</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>poi</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>households</td>\n",
+       "      <td>8000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pets</td>\n",
+       "      <td>3213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>pet_inc</td>\n",
+       "      <td>338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>trips</td>\n",
+       "      <td>120000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>safety</td>\n",
+       "      <td>12000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>events</td>\n",
+       "      <td>195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>obs</td>\n",
+       "      <td>35000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>lines</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>stops</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>ridership</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>bike</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>traffic</td>\n",
+       "      <td>315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>prices</td>\n",
+       "      <td>301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>weather</td>\n",
+       "      <td>101136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>trees</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>infra</td>\n",
+       "      <td>253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>bissues</td>\n",
+       "      <td>1614</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         table    rows\n",
+       "0      streets      15\n",
+       "1        parks       4\n",
+       "2          poi      19\n",
+       "3   households    8000\n",
+       "4         pets    3213\n",
+       "5      pet_inc     338\n",
+       "6        trips  120000\n",
+       "7       safety   12000\n",
+       "8       events     195\n",
+       "9          obs   35000\n",
+       "10       lines       4\n",
+       "11       stops       4\n",
+       "12   ridership      92\n",
+       "13        bike       9\n",
+       "14     traffic     315\n",
+       "15      prices     301\n",
+       "16     weather  101136\n",
+       "17       trees      79\n",
+       "18       infra     253\n",
+       "19     bissues    1614"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\dataset_shapes.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "def load_csv(name):\n",
+    "    return pd.read_csv(os.path.join(DATA_DIR, name))\n",
+    "\n",
+    "streets = load_csv(\"geo_streets.csv\")\n",
+    "parks = load_csv(\"geo_parks.csv\")\n",
+    "poi = load_csv(\"poi_generic.csv\")\n",
+    "households = load_csv(\"households.csv\")\n",
+    "pets = load_csv(\"pets_registry.csv\")\n",
+    "pet_inc = load_csv(\"pet_incidents.csv\")\n",
+    "trips = load_csv(\"mobility_trips.csv\")\n",
+    "safety = load_csv(\"public_safety.csv\")\n",
+    "events = load_csv(\"events_civic.csv\")\n",
+    "obs = load_csv(\"observations.csv\")\n",
+    "lines = load_csv(\"transit_lines.csv\")\n",
+    "stops = load_csv(\"transit_stops.csv\")\n",
+    "ridership = load_csv(\"transit_ridership_daily.csv\")\n",
+    "bike = load_csv(\"bike_infra.csv\")\n",
+    "traffic = load_csv(\"traffic_counts.csv\")\n",
+    "prices = load_csv(\"prices_index.csv\")\n",
+    "weather = load_csv(\"weather_daily.csv\")\n",
+    "trees = load_csv(\"trees_inventory.csv\")\n",
+    "infra = load_csv(\"infrastructure_events.csv\")\n",
+    "bissues = load_csv(\"building_issues.csv\")\n",
+    "\n",
+    "with open(os.path.join(DATA_DIR, \"DATA_DICTIONARY.json\"), \"r\", encoding=\"utf-8\") as f:\n",
+    "    dd = json.load(f)\n",
+    "\n",
+    "# Peek sizes\n",
+    "summary_shapes = pd.DataFrame({\n",
+    "    \"table\": [\"streets\",\"parks\",\"poi\",\"households\",\"pets\",\"pet_inc\",\"trips\",\"safety\",\"events\",\"obs\",\n",
+    "              \"lines\",\"stops\",\"ridership\",\"bike\",\"traffic\",\"prices\",\"weather\",\"trees\",\"infra\",\"bissues\"],\n",
+    "    \"rows\": [len(streets),len(parks),len(poi),len(households),len(pets),len(pet_inc),\n",
+    "             len(trips),len(safety),len(events),len(obs),len(lines),len(stops),\n",
+    "             len(ridership),len(bike),len(traffic),len(prices),len(weather),len(trees),len(infra),len(bissues)]\n",
+    "})\n",
+    "display(summary_shapes)\n",
+    "savetab(summary_shapes, \"dataset_shapes.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "54398070",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./figs\\01_geo_scatter.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "def wkt_point_to_lonlat(wkt):\n",
+    "    # expects 'POINT(lon lat)'\n",
+    "    m = re.match(r\"POINT\\(([-0-9\\.]+)\\s+([-0-9\\.]+)\\)\", str(wkt))\n",
+    "    if not m:\n",
+    "        return np.nan, np.nan\n",
+    "    lon, lat = float(m.group(1)), float(m.group(2))\n",
+    "    return lon, lat\n",
+    "\n",
+    "streets[[\"lon\",\"lat\"]] = streets[\"geometry_wkt\"].apply(lambda s: pd.Series(wkt_point_to_lonlat(s)))\n",
+    "parks[[\"lon\",\"lat\"]] = parks[\"geometry_wkt\"].apply(lambda s: pd.Series(wkt_point_to_lonlat(s)))\n",
+    "\n",
+    "# Save quick sample map\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.scatter(streets[\"lon\"], streets[\"lat\"], s=12, alpha=0.7)\n",
+    "plt.scatter(parks[\"lon\"], parks[\"lat\"], s=40, marker=\"^\", alpha=0.9)\n",
+    "plt.title(\"Synthetic Geo Points — Streets & Parks (WKT)\")\n",
+    "plt.xlabel(\"Longitude\"); plt.ylabel(\"Latitude\")\n",
+    "savefig(\"01_geo_scatter.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c3230d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>subcategory</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>community_center</td>\n",
+       "      <td>civic</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>bakery</td>\n",
+       "      <td>retail</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cafe</td>\n",
+       "      <td>coffee_tea</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>hardware</td>\n",
+       "      <td>retail</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>post_office</td>\n",
+       "      <td>civic</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bank</td>\n",
+       "      <td>retail</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>neighborhood</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>clinic</td>\n",
+       "      <td>primary</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>pharmacy</td>\n",
+       "      <td>retail</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>playground</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>school</td>\n",
+       "      <td>public</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>shelter</td>\n",
+       "      <td>animal</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>urgent_care</td>\n",
+       "      <td>clinic</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            category   subcategory  count\n",
+       "5   community_center         civic      3\n",
+       "0             bakery        retail      2\n",
+       "3               cafe    coffee_tea      2\n",
+       "6           hardware        retail      2\n",
+       "9        post_office         civic      2\n",
+       "1               bank        retail      1\n",
+       "2                bar  neighborhood      1\n",
+       "4             clinic       primary      1\n",
+       "7           pharmacy        retail      1\n",
+       "8         playground      standard      1\n",
+       "10            school        public      1\n",
+       "11           shelter        animal      1\n",
+       "12       urgent_care        clinic      1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\poi_mix.csv\n",
+      "Saved figure: ./figs\\02_poi_category_bar.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "poi_mix = poi.groupby([\"category\",\"subcategory\"])[\"poi_id\"].count().reset_index(name=\"count\").sort_values(\"count\", ascending=False)\n",
+    "display(poi_mix.head(20))\n",
+    "savetab(poi_mix, \"poi_mix.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "topcats = poi_mix.groupby(\"category\")[\"count\"].sum().sort_values(ascending=False).head(10)\n",
+    "topcats.plot(kind=\"bar\")\n",
+    "plt.title(\"POI by Category (Top 10)\")\n",
+    "plt.xlabel(\"Category\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"02_poi_category_bar.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ab0b40ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>era</th>\n",
+       "      <th>dwelling_type</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>n</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>own</td>\n",
+       "      <td>185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>own</td>\n",
+       "      <td>174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>single_family</td>\n",
+       "      <td>own</td>\n",
+       "      <td>156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>single_family</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>triple_decker</td>\n",
+       "      <td>own</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1800s</td>\n",
+       "      <td>triple_decker</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>own</td>\n",
+       "      <td>172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>own</td>\n",
+       "      <td>176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>single_family</td>\n",
+       "      <td>own</td>\n",
+       "      <td>145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>single_family</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>triple_decker</td>\n",
+       "      <td>own</td>\n",
+       "      <td>172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1900-1945</td>\n",
+       "      <td>triple_decker</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1946-1999</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>own</td>\n",
+       "      <td>163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1946-1999</td>\n",
+       "      <td>apartment</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1946-1999</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>own</td>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1946-1999</td>\n",
+       "      <td>condo</td>\n",
+       "      <td>rent</td>\n",
+       "      <td>159</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          era  dwelling_type tenure    n\n",
+       "0       1800s      apartment    own  185\n",
+       "1       1800s      apartment   rent  149\n",
+       "2       1800s          condo    own  174\n",
+       "3       1800s          condo   rent  165\n",
+       "4       1800s  single_family    own  156\n",
+       "5       1800s  single_family   rent  160\n",
+       "6       1800s  triple_decker    own  175\n",
+       "7       1800s  triple_decker   rent  139\n",
+       "8   1900-1945      apartment    own  172\n",
+       "9   1900-1945      apartment   rent  171\n",
+       "10  1900-1945          condo    own  176\n",
+       "11  1900-1945          condo   rent  179\n",
+       "12  1900-1945  single_family    own  145\n",
+       "13  1900-1945  single_family   rent  163\n",
+       "14  1900-1945  triple_decker    own  172\n",
+       "15  1900-1945  triple_decker   rent  151\n",
+       "16  1946-1999      apartment    own  163\n",
+       "17  1946-1999      apartment   rent  146\n",
+       "18  1946-1999          condo    own  167\n",
+       "19  1946-1999          condo   rent  159"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\households_profile.csv\n",
+      "Saved figure: ./figs\\03_households_occupants_hist.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "hh_profile = households.groupby([\"era\",\"dwelling_type\",\"tenure\"]).size().reset_index(name=\"n\")\n",
+    "display(hh_profile.head(20))\n",
+    "savetab(hh_profile, \"households_profile.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "households[\"occupants\"].plot(kind=\"hist\", bins=20)\n",
+    "plt.title(\"Household Occupants — Distribution\")\n",
+    "plt.xlabel(\"Occupants\"); plt.ylabel(\"Frequency\")\n",
+    "savefig(\"03_households_occupants_hist.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ecabf3f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>species</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>dog</td>\n",
+       "      <td>2067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cat</td>\n",
+       "      <td>960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>other</td>\n",
+       "      <td>186</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  species  count\n",
+       "0     dog   2067\n",
+       "1     cat    960\n",
+       "2   other    186"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\pets_species_counts.csv\n",
+      "Saved figure: ./figs\\04_pets_species_bar.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "pet_species = pets[\"species\"].value_counts().reset_index()\n",
+    "pet_species.columns = [\"species\",\"count\"]\n",
+    "display(pet_species)\n",
+    "savetab(pet_species, \"pets_species_counts.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "pet_species.set_index(\"species\")[\"count\"].plot(kind=\"bar\")\n",
+    "plt.title(\"Pets by Species\")\n",
+    "plt.xlabel(\"Species\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"04_pets_species_bar.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1478d234",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mode</th>\n",
+       "      <th>share</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>walk</td>\n",
+       "      <td>0.329950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>car</td>\n",
+       "      <td>0.205342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>subway</td>\n",
+       "      <td>0.115433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>bus</td>\n",
+       "      <td>0.096825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>autonomous</td>\n",
+       "      <td>0.084275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>bike</td>\n",
+       "      <td>0.068183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>streetcar</td>\n",
+       "      <td>0.046658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>micromobility</td>\n",
+       "      <td>0.029967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>horse</td>\n",
+       "      <td>0.023367</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            mode     share\n",
+       "0           walk  0.329950\n",
+       "1            car  0.205342\n",
+       "2         subway  0.115433\n",
+       "3            bus  0.096825\n",
+       "4     autonomous  0.084275\n",
+       "5           bike  0.068183\n",
+       "6      streetcar  0.046658\n",
+       "7  micromobility  0.029967\n",
+       "8          horse  0.023367"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\mobility_mode_share_overall.csv\n",
+      "Saved figure: ./figs\\05_trips_modes_overall_bar.png\n",
+      "Saved table: ./tables\\mobility_mode_counts_by_era.csv\n",
+      "Saved table: ./tables\\mobility_mode_share_by_era.csv\n",
+      "Saved figure: ./figs\\06_trips_by_mode_over_eras_line.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "mode_share = trips[\"mode\"].value_counts(normalize=True).reset_index()\n",
+    "mode_share.columns = [\"mode\",\"share\"]\n",
+    "display(mode_share)\n",
+    "savetab(mode_share, \"mobility_mode_share_overall.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "trips[\"mode\"].value_counts().plot(kind=\"bar\")\n",
+    "plt.title(\"Trip Modes — Overall Counts\")\n",
+    "plt.xlabel(\"Mode\"); plt.ylabel(\"Trips\")\n",
+    "savefig(\"05_trips_modes_overall_bar.png\")\n",
+    "\n",
+    "mode_era = trips.groupby([\"era\",\"mode\"]).size().unstack(fill_value=0)\n",
+    "mode_era_pct = mode_era.div(mode_era.sum(axis=1), axis=0)\n",
+    "savetab(mode_era.reset_index(), \"mobility_mode_counts_by_era.csv\")\n",
+    "savetab(mode_era_pct.reset_index(), \"mobility_mode_share_by_era.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "for m in mode_era.columns:\n",
+    "    plt.plot(mode_era.index, mode_era[m], marker=\"o\")\n",
+    "plt.title(\"Trips by Mode over Eras\")\n",
+    "plt.xlabel(\"Era\"); plt.ylabel(\"Count\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "savefig(\"06_trips_by_mode_over_eras_line.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "50bd51c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>traffic</td>\n",
+       "      <td>1740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>theft</td>\n",
+       "      <td>1739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>noise</td>\n",
+       "      <td>1735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>vandalism</td>\n",
+       "      <td>1720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>disturbance</td>\n",
+       "      <td>1702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>animal</td>\n",
+       "      <td>1702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>other</td>\n",
+       "      <td>1662</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      category  count\n",
+       "0      traffic   1740\n",
+       "1        theft   1739\n",
+       "2        noise   1735\n",
+       "3    vandalism   1720\n",
+       "4  disturbance   1702\n",
+       "5       animal   1702\n",
+       "6        other   1662"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\safety_incidents_by_category.csv\n",
+      "Saved figure: ./figs\\07_safety_category_bar.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "safety_cat = safety[\"category\"].value_counts().reset_index()\n",
+    "safety_cat.columns = [\"category\",\"count\"]\n",
+    "display(safety_cat)\n",
+    "savetab(safety_cat, \"safety_incidents_by_category.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "safety_cat.set_index(\"category\")[\"count\"].plot(kind=\"bar\")\n",
+    "plt.title(\"Public Safety Incidents by Category\")\n",
+    "plt.xlabel(\"Category\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"07_safety_category_bar.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c8e99a14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./figs\\08_noise_hist.png\n",
+      "Saved figure: ./figs\\09_aqi_hist.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise = obs[obs[\"metric\"]==\"noise_db\"].copy()\n",
+    "aqi = obs[obs[\"metric\"]==\"aqi\"].copy()\n",
+    "\n",
+    "# Noise distribution\n",
+    "plt.figure(figsize=(7,4))\n",
+    "noise[\"value\"].plot(kind=\"hist\", bins=30)\n",
+    "plt.title(\"Noise (dB) — Distribution\")\n",
+    "plt.xlabel(\"dB\"); plt.ylabel(\"Frequency\")\n",
+    "savefig(\"08_noise_hist.png\")\n",
+    "\n",
+    "# AQI distribution\n",
+    "plt.figure(figsize=(7,4))\n",
+    "aqi[\"value\"].plot(kind=\"hist\", bins=30)\n",
+    "plt.title(\"AQI — Distribution\")\n",
+    "plt.xlabel(\"AQI\"); plt.ylabel(\"Frequency\")\n",
+    "savefig(\"09_aqi_hist.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fc79bee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>attendees_est</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1946</td>\n",
+       "      <td>2999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1947</td>\n",
+       "      <td>2999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1948</td>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1949</td>\n",
+       "      <td>2999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1950</td>\n",
+       "      <td>2999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  attendees_est\n",
+       "0  1946           2999\n",
+       "1  1947           2999\n",
+       "2  1948           3000\n",
+       "3  1949           2999\n",
+       "4  1950           2999"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\events_attendance_by_year.csv\n",
+      "Saved figure: ./figs\\10_events_attendance_by_year.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "events['year'] = pd.to_datetime(events['date']).dt.year\n",
+    "att_year = events.groupby('year')[\"attendees_est\"].sum().reset_index()\n",
+    "display(att_year.head())\n",
+    "savetab(att_year, \"events_attendance_by_year.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(att_year[\"year\"], att_year[\"attendees_est\"])\n",
+    "plt.title(\"Street‑Band Festival — Total Attendance by Year\")\n",
+    "plt.xlabel(\"Year\"); plt.ylabel(\"Estimated attendees\")\n",
+    "savefig(\"10_events_attendance_by_year.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f75741ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\transit_ridership_by_year_pivot.csv\n",
+      "Saved figure: ./figs\\11_transit_ridership_lines.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "ridership['year'] = pd.to_datetime(ridership['date']).dt.year\n",
+    "rid_pvt = ridership.pivot_table(index=\"year\", columns=\"line_id\", values=\"boardings_est\", aggfunc=\"sum\")\n",
+    "savetab(rid_pvt.reset_index(), \"transit_ridership_by_year_pivot.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(9,5))\n",
+    "for col in rid_pvt.columns:\n",
+    "    plt.plot(rid_pvt.index, rid_pvt[col])\n",
+    "plt.title(\"Transit Ridership — Boardings by Line over Time\")\n",
+    "plt.xlabel(\"Year\"); plt.ylabel(\"Boardings (est.)\")\n",
+    "savefig(\"11_transit_ridership_lines.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c857cfce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./figs\\12_traffic_scatter.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>facility_type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>painted_lane</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>protected_lane</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>shared</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    facility_type  count\n",
+       "0    painted_lane      6\n",
+       "1  protected_lane      2\n",
+       "2          shared      1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\bike_facility_counts.csv\n",
+      "Saved figure: ./figs\\13_bike_facility_bar.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,5))\n",
+    "plt.scatter(traffic[\"vehicles_per_day\"], traffic[\"bikes_per_day\"], alpha=0.5)\n",
+    "plt.title(\"Traffic: Vehicles vs Bikes per Day\")\n",
+    "plt.xlabel(\"Vehicles/day\"); plt.ylabel(\"Bikes/day\")\n",
+    "savefig(\"12_traffic_scatter.png\")\n",
+    "\n",
+    "bike_counts = bike[\"facility_type\"].value_counts().reset_index()\n",
+    "bike_counts.columns = [\"facility_type\",\"count\"]\n",
+    "display(bike_counts)\n",
+    "savetab(bike_counts, \"bike_facility_counts.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "bike_counts.set_index(\"facility_type\")[\"count\"].plot(kind=\"bar\")\n",
+    "plt.title(\"Bike Facility Types\")\n",
+    "plt.xlabel(\"Facility type\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"13_bike_facility_bar.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9a238c42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\weather_temp_monthly_mean.csv\n",
+      "Saved figure: ./figs\\14_weather_temp_monthly.png\n",
+      "Saved table: ./tables\\weather_rain_monthly_sum.csv\n",
+      "Saved figure: ./figs\\15_weather_rain_monthly.png\n",
+      "Saved table: ./tables\\weather_snow_monthly_sum.csv\n",
+      "Saved figure: ./figs\\16_weather_snow_monthly.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "weather['month'] = pd.to_datetime(weather['date']).dt.month\n",
+    "temp_month = weather.groupby('month')[\"temp_c\"].mean().reset_index()\n",
+    "savetab(temp_month, \"weather_temp_monthly_mean.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "plt.plot(temp_month[\"month\"], temp_month[\"temp_c\"], marker=\"o\")\n",
+    "plt.title(\"Avg Temperature by Month\")\n",
+    "plt.xlabel(\"Month\"); plt.ylabel(\"Temp (°C)\")\n",
+    "savefig(\"14_weather_temp_monthly.png\")\n",
+    "\n",
+    "rain_month = weather.groupby('month')[\"precip_mm\"].sum().reset_index()\n",
+    "savetab(rain_month, \"weather_rain_monthly_sum.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "plt.bar(rain_month[\"month\"], rain_month[\"precip_mm\"])\n",
+    "plt.title(\"Total Rainfall by Month\")\n",
+    "plt.xlabel(\"Month\"); plt.ylabel(\"Precipitation (mm)\")\n",
+    "savefig(\"15_weather_rain_monthly.png\")\n",
+    "\n",
+    "snow_month = weather.groupby('month')[\"snow_cm\"].sum().reset_index()\n",
+    "savetab(snow_month, \"weather_snow_monthly_sum.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "plt.bar(snow_month[\"month\"], snow_month[\"snow_cm\"])\n",
+    "plt.title(\"Total Snowfall by Month\")\n",
+    "plt.xlabel(\"Month\"); plt.ylabel(\"Snow (cm)\")\n",
+    "savefig(\"16_weather_snow_monthly.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d7b08fa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>water_main_break</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>power_outage</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>road_potholes</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>tree_fall</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               type  count\n",
+       "3  water_main_break     89\n",
+       "0      power_outage     86\n",
+       "1     road_potholes     54\n",
+       "2         tree_fall     24"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\infra_event_counts.csv\n",
+      "Saved figure: ./figs\\17_infra_events_bar.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>hvac_failure</td>\n",
+       "      <td>676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>roof_leak</td>\n",
+       "      <td>662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>plumbing_issue</td>\n",
+       "      <td>276</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             type  count\n",
+       "0    hvac_failure    676\n",
+       "2       roof_leak    662\n",
+       "1  plumbing_issue    276"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved table: ./tables\\building_issue_counts.csv\n",
+      "Saved figure: ./figs\\18_building_issues_bar.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "infra['year'] = pd.to_datetime(infra['date']).dt.year\n",
+    "infra_counts = infra.groupby(['type'])[\"infra_event_id\"].count().reset_index(name=\"count\").sort_values(\"count\", ascending=False)\n",
+    "display(infra_counts)\n",
+    "savetab(infra_counts, \"infra_event_counts.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "infra_counts.set_index(\"type\")[\"count\"].plot(kind=\"bar\")\n",
+    "plt.title(\"Infrastructure Events by Type\")\n",
+    "plt.xlabel(\"Type\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"17_infra_events_bar.png\")\n",
+    "\n",
+    "bissues['year'] = pd.to_datetime(bissues['date']).dt.year\n",
+    "bissues_counts = bissues.groupby(['type'])[\"building_issue_id\"].count().reset_index(name=\"count\").sort_values(\"count\", ascending=False)\n",
+    "display(bissues_counts)\n",
+    "savetab(bissues_counts, \"building_issue_counts.csv\")\n",
+    "\n",
+    "plt.figure(figsize=(7,4))\n",
+    "bissues_counts.set_index(\"type\")[\"count\"].plot(kind=\"bar\")\n",
+    "plt.title(\"Building Issues by Type\")\n",
+    "plt.xlabel(\"Type\"); plt.ylabel(\"Count\")\n",
+    "savefig(\"18_building_issues_bar.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8e57aa50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure: ./figs\\19_prices_housing_line.png\n",
+      "Saved figure: ./figs\\20_prices_food_line.png\n",
+      "Saved figure: ./figs\\21_prices_transit_line.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(prices[\"year\"], prices[\"housing_idx\"])\n",
+    "plt.title(\"Housing Price Index (Synthetic)\")\n",
+    "plt.xlabel(\"Year\"); plt.ylabel(\"Index\")\n",
+    "savefig(\"19_prices_housing_line.png\")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(prices[\"year\"], prices[\"staple_food_idx\"])\n",
+    "plt.title(\"Staple Food Index (Synthetic)\")\n",
+    "plt.xlabel(\"Year\"); plt.ylabel(\"Index\")\n",
+    "savefig(\"20_prices_food_line.png\")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(prices[\"year\"], prices[\"transit_fare_idx\"])\n",
+    "plt.title(\"Transit Fare Index (Synthetic)\")\n",
+    "plt.xlabel(\"Year\"); plt.ylabel(\"Index\")\n",
+    "savefig(\"21_prices_transit_line.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "765e0518",
+   "metadata": {},
+   "source": [
+    "### Outputs\n",
+    "- Figures saved under `FIGS_DIR`\n",
+    "- Summary tables saved under `TABLES_DIR`\n",
+    "\n",
+    "You can zip these folders or publish selected images wherever you like.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}