{ "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": [ "
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tablerows
0streets15
1parks4
2poi19
3households8000
4pets3213
5pet_inc338
6trips120000
7safety12000
8events195
9obs35000
10lines4
11stops4
12ridership92
13bike9
14traffic315
15prices301
16weather101136
17trees79
18infra253
19bissues1614
\n", "
" ], "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": [ "
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categorysubcategorycount
5community_centercivic3
0bakeryretail2
3cafecoffee_tea2
6hardwareretail2
9post_officecivic2
1bankretail1
2barneighborhood1
4clinicprimary1
7pharmacyretail1
8playgroundstandard1
10schoolpublic1
11shelteranimal1
12urgent_careclinic1
\n", "
" ], "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": [ "
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eradwelling_typetenuren
01800sapartmentown185
11800sapartmentrent149
21800scondoown174
31800scondorent165
41800ssingle_familyown156
51800ssingle_familyrent160
61800striple_deckerown175
71800striple_deckerrent139
81900-1945apartmentown172
91900-1945apartmentrent171
101900-1945condoown176
111900-1945condorent179
121900-1945single_familyown145
131900-1945single_familyrent163
141900-1945triple_deckerown172
151900-1945triple_deckerrent151
161946-1999apartmentown163
171946-1999apartmentrent146
181946-1999condoown167
191946-1999condorent159
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" ], "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": [ "
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speciescount
0dog2067
1cat960
2other186
\n", "
" ], "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": [ "
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modeshare
0walk0.329950
1car0.205342
2subway0.115433
3bus0.096825
4autonomous0.084275
5bike0.068183
6streetcar0.046658
7micromobility0.029967
8horse0.023367
\n", "
" ], "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": [ "
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categorycount
0traffic1740
1theft1739
2noise1735
3vandalism1720
4disturbance1702
5animal1702
6other1662
\n", "
" ], "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": [ "
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yearattendees_est
019462999
119472999
219483000
319492999
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" ], "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": [ "
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facility_typecount
0painted_lane6
1protected_lane2
2shared1
\n", "
" ], "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": [ "
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typecount
3water_main_break89
0power_outage86
1road_potholes54
2tree_fall24
\n", "
" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
typecount
0hvac_failure676
2roof_leak662
1plumbing_issue276
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" ], "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 }