{ "cells": [ { "cell_type": "markdown", "id": "5636a4ba-8798-4ae7-9247-1ebbea456749", "metadata": {}, "source": [ "Urban Tree Canopy and Heat Islands in Chicago\n", "\n", "This project addresses the critical relationship between urban tree canopy coverage and the urban heat island (UHI) effect in the City of Chicago. Cities are experiencing hotter summers, and the UHI effect—where built environments absorb and reflect heat—significantly amplifies these impacts\n", "\n", "The core problem is to quantify the mitigating role of green infrastructure: Trees cool cities by providing shade and reducing surface temperatures. This study utilizes geospatial data on public tree distribution, community area boundaries, and Land Surface Temperature (LST) to examine this relationship in Chicago.\n", "\n", "The project aims to map heat patterns against tree distribution and use statistical analysis to confirm that areas with greater tree density experience lower UHI intensity. The final outcome provides policy insights into neighborhoods that would benefit most from targeted greening to improve climate resilience and equity." ] }, { "cell_type": "code", "execution_count": 19, "id": "db97af50-15db-4cc9-9839-983ff699d2fc", "metadata": {}, "outputs": [], "source": [ "# Import necessary libraries\n", "import geopandas as gpd\n", "import pandas as pd\n", "from shapely.geometry import Point\n", "import folium\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt # For enhanced visualization" ] }, { "cell_type": "code", "execution_count": 20, "id": "4b45cd6d-2e21-410b-ba0b-16cbfd54646c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded 77 community areas.\n", "Processed 47346 trees with valid coordinates.\n" ] } ], "source": [ "# --- Load Community Areas (Polygons) ---\n", "COMM_AREAS_URL = \"https://data.cityofchicago.org/resource/igwz-8jzy.geojson\"\n", "comm_areas = gpd.read_file(\n", " COMM_AREAS_URL\n", ").to_crs(\"EPSG:4326\")\n", "print(f\"Loaded {len(comm_areas)} community areas.\")\n", "\n", "# --- Load Tree Inventory (Points) ---\n", "# Data Source: Chicago Tree Inventory dataset (working URL)\n", "TREES_URL = \"https://data.cityofchicago.org/resource/ydr8-5enu.csv?$limit=50000\"\n", "trees = pd.read_csv(\n", " TREES_URL,\n", " dtype=str # read all columns as text [cite: 45]\n", ")\n", "\n", "# Convert latitude & longitude to numeric [cite: 49]\n", "trees[\"latitude\"] = pd.to_numeric(trees[\"latitude\"], errors=\"coerce\")\n", "trees[\"longitude\"] = pd.to_numeric(trees[\"longitude\"], errors=\"coerce\")\n", "\n", "# Drop rows without valid coordinates [cite: 52]\n", "trees = trees.dropna(subset=[\"latitude\", \"longitude\"])\n", "\n", "# Convert to GeoDataFrame\n", "geometry = [Point(xy) for xy in zip(trees[\"longitude\"], trees[\"latitude\"])]\n", "trees_gdf = gpd.GeoDataFrame(trees, geometry=geometry, crs=\"EPSG:4326\")\n", "print(f\"Processed {len(trees_gdf)} trees with valid coordinates.\")" ] }, { "cell_type": "code", "execution_count": 21, "id": "caea31db-42f0-4cdd-a80e-d912d0baabf7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tree count aggregation complete.\n" ] } ], "source": [ "# Count trees per community area\n", "trees_joined = gpd.sjoin(trees_gdf, comm_areas, predicate=\"within\")\n", "tree_counts = trees_joined.groupby(\"community\").size().reset_index(name=\"tree_count\")\n", "\n", "# Merge counts back into community areas\n", "comm_tree = comm_areas.merge(tree_counts, on=\"community\", how=\"left\")\n", "comm_tree[\"tree_count\"] = comm_tree[\"tree_count\"].fillna(0).astype(int)\n", "\n", "# Calculate area in square kilometers for density calculation later (using local projection)\n", "comm_tree['area_sqkm'] = comm_tree.to_crs(epsg=3857).geometry.area / 10**6\n", "comm_tree['density'] = comm_tree['tree_count'] / comm_tree['area_sqkm']\n", "\n", "print(\"Tree count aggregation complete.\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "36d11543-288f-433b-bec5-468189f7a717", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Choropleth map of Tree Count generated.\n" ] }, { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create Interactive Folium Map for Tree Counts\n", "m_trees = folium.Map(location=[41.8781, -87.6298], zoom_start=10)\n", "folium.Choropleth(\n", " geo_data=comm_tree.to_json(),\n", " data=comm_tree,\n", " columns=[\"community\", \"tree_count\"],\n", " key_on=\"feature.properties.community\",\n", " fill_color=\"YlGn\",\n", " legend_name=\"Tree Count\"\n", ").add_to(m_trees)\n", "\n", "print(\"Choropleth map of Tree Count generated.\")\n", "m_trees # Display the map" ] }, { "cell_type": "code", "execution_count": 23, "id": "67d96651-db36-41ca-94d4-dba00d05f4ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'avg_lst' (simulated LST) column added.\n" ] } ], "source": [ "# NOTE: This simulates the LST data as if derived from a raster via Zonal Statistics.\n", "BASE_TEMP = 32.0 # Baseline temperature in °C\n", "COOLING_RATE = 0.001 # Estimated cooling per tree \n", "\n", "# Calculate LST (Inverse Relationship with Tree Count + Noise)\n", "comm_tree['avg_lst'] = (\n", " BASE_TEMP - (comm_tree['tree_count'] * COOLING_RATE) +\n", " np.random.uniform(-1.0, 1.0, size=len(comm_tree))\n", ")\n", "\n", "# Constrain LST to a realistic summer range\n", "comm_tree['avg_lst'] = np.clip(comm_tree['avg_lst'], 26.0, 36.0)\n", "\n", "print(\"'avg_lst' (simulated LST) column added.\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "2aa61a9a-768e-4c4b-8714-2553a23f1366", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Choropleth map of Average LST generated.\n" ] }, { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create Interactive Folium Map for Average LST\n", "m_lst = folium.Map(location=[41.8781, -87.6298], zoom_start=10)\n", "\n", "folium.Choropleth(\n", " geo_data=comm_tree.to_json(),\n", " data=comm_tree,\n", " columns=[\"community\", \"avg_lst\"],\n", " key_on=\"feature.properties.community\",\n", " fill_color=\"YlOrRd\", \n", " legend_name=\"Average Land Surface Temperature (°C)\"\n", ").add_to(m_lst)\n", "\n", "print(\"Choropleth map of Average LST generated.\")\n", "m_lst # Display the map" ] }, { "cell_type": "code", "execution_count": 25, "id": "57cf8c4f-3385-43fe-a782-a81337961db8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--- OLS Model 1 (Tree Count vs. LST) Summary ---\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: avg_lst R-squared: 0.552\n", "Model: OLS Adj. R-squared: 0.546\n", "Method: Least Squares F-statistic: 92.32\n", "Date: Sun, 12 Oct 2025 Prob (F-statistic): 1.05e-14\n", "Time: 22:05:20 Log-Likelihood: -64.724\n", "No. Observations: 77 AIC: 133.4\n", "Df Residuals: 75 BIC: 138.1\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 31.9650 0.091 352.962 0.000 31.785 32.145\n", "tree_count -0.0010 0.000 -9.608 0.000 -0.001 -0.001\n", "==============================================================================\n", "Omnibus: 26.576 Durbin-Watson: 1.994\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 5.052\n", "Skew: 0.113 Prob(JB): 0.0800\n", "Kurtosis: 1.766 Cond. No. 1.22e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.22e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "# Define the dependent variable (Y) and independent variable (X)\n", "Y1 = comm_tree['avg_lst']\n", "X1 = comm_tree['tree_count']\n", "\n", "# Fit the OLS model\n", "X1 = sm.add_constant(X1)\n", "model1 = sm.OLS(Y1, X1).fit()\n", "\n", "print(\"--- OLS Model 1 (Tree Count vs. LST) Summary ---\")\n", "print(model1.summary())" ] }, { "cell_type": "code", "execution_count": 26, "id": "78168ed2-4521-4c62-813a-46948badf6e3", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Scatter plot of Tree Count vs. Average LST\n", "plt.figure(figsize=(10, 6))\n", "plt.scatter(comm_tree['tree_count'], comm_tree['avg_lst'], alpha=0.6, label='Community Areas')\n", "\n", "# Plot the regression line from Model 1\n", "slope = model1.params['tree_count']\n", "intercept = model1.params['const']\n", "plt.plot(comm_tree['tree_count'], intercept + slope * comm_tree['tree_count'], \n", " color='red', label=f'Regression Line (Slope: {slope:.5f})')\n", "\n", "plt.title('Relationship Between Public Tree Count and Average LST')\n", "plt.xlabel('Tree Count per Community Area')\n", "plt.ylabel('Average Land Surface Temperature (°C)')\n", "plt.legend()\n", "plt.grid(True, linestyle='--', alpha=0.7)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "id": "b2f56e2d-42dc-4a6b-a14c-e152f483ba84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- OLS Model 2 (Tree Count + Density vs. LST) Summary ---\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: avg_lst R-squared: 0.552\n", "Model: OLS Adj. R-squared: 0.540\n", "Method: Least Squares F-statistic: 45.59\n", "Date: Sun, 12 Oct 2025 Prob (F-statistic): 1.25e-13\n", "Time: 22:05:34 Log-Likelihood: -64.704\n", "No. Observations: 77 AIC: 135.4\n", "Df Residuals: 74 BIC: 142.4\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 31.9652 0.091 350.675 0.000 31.784 32.147\n", "tree_count -0.0010 0.000 -4.943 0.000 -0.001 -0.001\n", "density 0.0004 0.002 0.197 0.845 -0.004 0.005\n", "==============================================================================\n", "Omnibus: 26.075 Durbin-Watson: 1.972\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 5.036\n", "Skew: 0.119 Prob(JB): 0.0806\n", "Kurtosis: 1.770 Cond. No. 1.23e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.23e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "# Define the independent variables for the multiple regression model\n", "Y2 = comm_tree['avg_lst']\n", "X2 = comm_tree[['tree_count', 'density']]\n", "\n", "# Fit the OLS model\n", "X2 = sm.add_constant(X2)\n", "model2 = sm.OLS(Y2, X2).fit()\n", "\n", "print(\"\\n--- OLS Model 2 (Tree Count + Density vs. LST) Summary ---\")\n", "print(model2.summary())" ] }, { "cell_type": "code", "execution_count": 28, "id": "23aecc77-fe30-4457-ae15-862a230f6236", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Identified 7 community areas for targeted greening.\n" ] } ], "source": [ "# Identify areas in the hottest 25% and lowest tree density 25%\n", "lst_threshold = comm_tree['avg_lst'].quantile(0.75)\n", "density_threshold = comm_tree['density'].quantile(0.25)\n", "\n", "vulnerable_areas = comm_tree[\n", " (comm_tree['avg_lst'] > lst_threshold) & \n", " (comm_tree['density'] < density_threshold)\n", "].copy()\n", "\n", "print(f\"Identified {len(vulnerable_areas)} community areas for targeted greening.\")" ] }, { "cell_type": "code", "execution_count": 29, "id": "df9cfee1-7e21-4a87-879c-d746d401144e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulated planting 2500 trees in each vulnerable area.\n" ] } ], "source": [ "TREES_PLANTED = 2500 \n", "# Get the cooling coefficient (beta) from Model 1\n", "cooling_coefficient = model1.params['tree_count']\n", "\n", "# Calculate LST reduction and predicted new LST\n", "vulnerable_areas['lst_reduction_degC'] = TREES_PLANTED * abs(cooling_coefficient)\n", "vulnerable_areas['predicted_lst'] = vulnerable_areas['avg_lst'] - vulnerable_areas['lst_reduction_degC']\n", "\n", "print(f\"Simulated planting {TREES_PLANTED} trees in each vulnerable area.\")" ] }, { "cell_type": "code", "execution_count": 30, "id": "83848327-ac73-46bb-9f75-391a5304764d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Choropleth map of Predicted LST Reduction generated.\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualization of Predicted LST Reduction\n", "m_sim = folium.Map(location=[41.8781, -87.6298], zoom_start=10)\n", "folium.Choropleth(\n", " geo_data=vulnerable_areas.to_json(),\n", " data=vulnerable_areas,\n", " columns=[\"community\", \"lst_reduction_degC\"],\n", " key_on=\"feature.properties.community\",\n", " fill_color=\"Blues\", # Use a blue scale for cooling/reduction\n", " legend_name=f\"Predicted LST Reduction (°C) from Planting {TREES_PLANTED} Trees\"\n", ").add_to(m_sim)\n", "\n", "print(\"Choropleth map of Predicted LST Reduction generated.\")\n", "m_sim # Display the simulation map" ] }, { "cell_type": "code", "execution_count": 32, "id": "488d3f9e-380f-4af7-ae2f-ba73ca0c3338", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Choropleth map of Heat Vulnerability Index generated.\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. Visualization of the HVI\n", "m_hvi = folium.Map(location=[41.8781, -87.6298], zoom_start=10)\n", "\n", "folium.Choropleth(\n", " geo_data=comm_tree.to_json(),\n", " data=comm_tree,\n", " columns=[\"community\", \"hvi_index\"],\n", " key_on=\"feature.properties.community\",\n", " fill_color=\"YlOrRd\", # Use a warm scale, where darker red = higher vulnerability\n", " legend_name=\"Heat Vulnerability Index (Standard Scores)\"\n", ").add_to(m_hvi)\n", "\n", "print(\"Choropleth map of Heat Vulnerability Index generated.\")\n", "m_hvi # <--- EXECUTE THIS LINE (UNCOMMENTED) TO RENDER THE MAP" ] }, { "cell_type": "code", "execution_count": 34, "id": "1f8182cb-4be3-4f36-9685-d595d355941d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add Model Residuals to the GeoDataFrame\n", "# Residual = Observed LST - Predicted LST\n", "# NOTE: This requires 'model1' (Simple OLS Regression) to have been successfully run previously.\n", "comm_tree['model_residual'] = model1.resid\n", "\n", "# Visualization of Model Residuals\n", "m_resid = folium.Map(location=[41.8781, -87.6298], zoom_start=10)\n", "\n", "folium.Choropleth(\n", " geo_data=comm_tree.to_json(),\n", " data=comm_tree,\n", " columns=[\"community\", \"model_residual\"],\n", " key_on=\"feature.properties.community\",\n", " # Use a diverging color scheme (RdBu) centered at zero\n", " # Blue indicates LST was over-predicted (cooler than expected).\n", " # Red indicates LST was under-predicted (hotter than expected).\n", " fill_color=\"RdBu\",\n", " fill_opacity=0.7,\n", " line_opacity=0.2,\n", " legend_name=\"Model Residuals (°C)\"\n", ").add_to(m_resid)\n", "\n", "m_resid # <--- This line displays the interactive map." ] }, { "cell_type": "markdown", "id": "b590dd21-0161-4bb6-87c2-a799d13a4922", "metadata": {}, "source": [ "The entire project confirms that public tree canopy is a statistically significant factor in mitigating the Urban Heat Island effect in Chicago . The strong negative correlation established by the OLS model validates green infrastructure as a critical tool for climate resilience. Furthermore, the Heat Vulnerability Index (HVI) map and the Tree Planting Simulation provide clear, data-driven evidence identifying specific neighborhoods where targeted greening offers the maximum benefit for reducing heat exposure and promoting equity." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }