{ "cells": [ { "cell_type": "markdown", "id": "9d9329cb-b520-46d3-9fc3-dd491c6e95f4", "metadata": {}, "source": [ "##GGIS 407 Project: Mapping Crime Rate Statistics in the US." ] }, { "cell_type": "markdown", "id": "006853a5-7630-484a-ae06-bfbbe62bd713", "metadata": {}, "source": [ "##Introduction\n", "\n", "Crime is a complex social issue that varies widely across regions, influenced by economic conditions, population density, urban design, \n", "and law-enforcement practices. Mapping crime statistics at the U.S. state level provides a spatial understanding of where and what types of crimes \n", "occur most frequently. This project analyzes and visualizes state-level crime data using the CyberGISX platform, integrating crime statistics with \n", "geospatial data to explore spatial patterns and relationships across the country." ] }, { "cell_type": "markdown", "id": "dbf7ecb8-a905-4be5-a3fa-5d362800afdb", "metadata": {}, "source": [ "##Purpose and Importance of Crime Mapping\n", "\n", "The main goal of this study is to examine how crime rates differ among U.S. states and how violent and property crimes compare spatially.\n", "Mapping crime is essential because:\n", "\n", "Revealing spatial patterns: Visualizing crime rates shows regional differences and highlights states with unusually high or low crime intensities.\n", "\n", "Supporting policy and planning: Law-enforcement agencies and policymakers can identify target areas for intervention and resource allocation.\n", "\n", "Encouraging transparency and awareness: Publicly accessible crime maps promote data-driven discussions on community safety and social equity.\n", "\n", "Integrating urban-planning insight: Comparing crime distributions with demographic and built-environment factors helps planners design safer \n", "and more equitable urban spaces.\n", "\n", "By mapping different crime types—violent (e.g., assault, murder, robbery) and property (e.g., burglary, larceny, motor-vehicle theft)\n", "this project emphasizes that not all crimes follow the same spatial logic or share the same underlying causes." ] }, { "cell_type": "markdown", "id": "fa11a910-7dac-4d71-86ee-1af741332661", "metadata": {}, "source": [ "##3. Data and Methodology\n", "\n", "The dataset used is a state-level U.S. crime CSV containing annual data on total, violent, and property crime rates along with population counts. \n", "For each state, the most recent year of data was extracted in excel.\n", "\n", "Using GeoPandas and Folium within the CyberGISX platform:\n", "\n", "The crime dataset was joined with a U.S. state GeoJSON boundary file.\n", "\n", "Choropleth maps were produced to display the distribution of violent, property, and total crime rates.\n", "\n", "Bubble maps were generated, where circle sizes represented total crime intensity.\n", "\n", "Hover labels and popups were added for interactivity and readability.\n", "\n", "All visualizations used a consistent state-level scale, making cross-comparison intuitive and spatially meaningful." ] }, { "cell_type": "code", "execution_count": null, "id": "452949cd-5810-418f-8b00-d9dae4cb0fa1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "d4baa034-0f89-4439-acd5-24114ab67aed", "metadata": {}, "source": [ "##MAP 1: Population in each state" ] }, { "cell_type": "code", "execution_count": 39, "id": "91a0cf5f-1604-4a8a-8fe1-094df62664cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "import folium\n", "\n", "# Paths to files\n", "geo_path = \"/home/jovyan/shared_data/data/geog407/exam1/us-states.json\" # GeoJSON for US states\n", "crime_path = \"DATASET.csv\" # Replace with your dataset path\n", "\n", "# Load data\n", "gdf = gpd.read_file(geo_path)\n", "df = pd.read_csv(crime_path)\n", "\n", "# Merge by state name\n", "merged = gdf.merge(df, left_on='name', right_on='State')\n", "\n", "# Create choropleth map\n", "m = folium.Map(location=[37.8, -96], zoom_start=4)\n", "folium.Choropleth(\n", " geo_data=merged,\n", " name=\"choropleth\",\n", " data=merged,\n", " columns=[\"State\", \"Data.Population\"],\n", " key_on=\"feature.properties.name\",\n", " fill_color=\"YlOrRd\",\n", " fill_opacity=0.7,\n", " line_opacity=0.2,\n", " legend_name=\"Population in US States\"\n", ").add_to(m)\n", "\n", "m" ] }, { "cell_type": "markdown", "id": "7d767065-adf8-4dee-8607-99a48b3e81f0", "metadata": {}, "source": [ "Analysis: Population in US States:\n", "The population distribution across US states shows a strong concentration in coastal and southern states, particularly California, Texas, Florida, and New York. The Midwest and Mountain regions have relatively lower populations, with states like Wyoming, Vermont, and the Dakotas showing sparse settlement. This uneven distribution reflects urbanization trends, economic opportunities, and migration patterns toward warmer climates and larger metropolitan areas." ] }, { "cell_type": "code", "execution_count": null, "id": "7fade909-fa1a-4ab5-9fd2-9aa3ddd1eed3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "4cf9f6f0-3109-4d79-8c49-af7082bb5059", "metadata": {}, "source": [ "##MAP 2 = Bubble map to show Total Crime Rate in all the states" ] }, { "cell_type": "code", "execution_count": 25, "id": "94cdd3fd-5220-40b3-b3f8-0ca013002928", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_156/2162162779.py:20: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", "\n", " merged[\"lon\"] = merged.centroid.x\n", "/tmp/ipykernel_156/2162162779.py:21: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", "\n", " merged[\"lat\"] = merged.centroid.y\n" ] }, { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import geopandas as gpd\n", "import folium\n", "\n", "crime_path = \"DATASET.csv\" \n", "crime_latest = pd.read_csv(crime_path)\n", "\n", "\n", "crime_latest[\"Total_Crime_Rate\"] = (\n", " crime_latest[\"Data.Rates.Violent.All\"] + crime_latest[\"Data.Rates.Property.All\"]\n", ")\n", "\n", "geo_path = \"/home/jovyan/shared_data/data/geog407/exam1/us-states.json\"\n", "gdf = gpd.read_file(geo_path)\n", "merged = gdf.merge(crime_latest, left_on=\"name\", right_on=\"State\")\n", "\n", "merged = merged.to_crs(epsg=5070) # Projected CRS\n", "merged[\"centroid\"] = merged.geometry.centroid\n", "merged = merged.to_crs(epsg=4326) # Back to geographic CRS\n", "merged[\"lon\"] = merged.centroid.x\n", "merged[\"lat\"] = merged.centroid.y\n", "\n", "\n", "m_bubbles = folium.Map(location=[37.8, -96], zoom_start=4, tiles=\"cartodbpositron\")\n", "\n", "\n", "scale = 0.004 \n", "for _, row in merged.iterrows():\n", " folium.CircleMarker(\n", " location=[row[\"lat\"], row[\"lon\"]],\n", " radius=row[\"Total_Crime_Rate\"] * scale,\n", " color=\"darkblue\",\n", " fill=True,\n", " fill_opacity=0.6,\n", " popup=f\"{row['State']}
Total Crime Rate: {row['Total_Crime_Rate']:.1f}\",\n", " ).add_to(m_bubbles)\n", "\n", "for _, row in merged.iterrows():\n", " folium.map.Marker(\n", " [row[\"lat\"], row[\"lon\"]],\n", " icon=folium.DivIcon(html=f\"\"\"\n", "
\n", " {row['State']}\n", "
\n", " \"\"\")\n", " ).add_to(m_bubbles)\n", "\n", "legend_html = \"\"\"\n", "
\n", " Legend: Total Crime Rate
\n", " \n", "   Circle size = Total Crime Rate
\n", "
\n", "\"\"\"\n", "m_bubbles.get_root().html.add_child(folium.Element(legend_html))\n", "\n", "m_bubbles\n" ] }, { "cell_type": "markdown", "id": "cd704728-9c13-4cca-9eac-cb89a0398413", "metadata": {}, "source": [ "Analysis: Total Crime Rate:\n", "The total crime rate map reveals that higher crime rates are concentrated in more densely populated or urbanized states such as New Mexico, California, Texas, and Lousiana, while rural states like Maine, Vermont, and Idaho report much lower crime levels. The spatial pattern suggests a correlation between urban density and crime occurrence, as areas with larger populations often experience more social and economic disparities contributing to higher crime activity." ] }, { "cell_type": "code", "execution_count": null, "id": "20a1ef6e-8817-4b3f-9aac-ae74a4b5bbec", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "726b6c69-f141-42f3-baa2-f8407bc324b4", "metadata": {}, "source": [ "SCATTER PLOT SHOWING POPULATION VS TOTAL CRIME RATE" ] }, { "cell_type": "code", "execution_count": 50, "id": "9b2255c6-f3d9-4dec-a1db-df55d122730b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Example data for all states (simplified demo)\n", "data = {\n", " 'State': ['Alabama','Alaska','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Florida','Georgia',\n", " 'Hawaii','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky','Louisiana','Maine','Maryland',\n", " 'Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New Hampshire','New Jersey',\n", " 'New Mexico','New York','North Carolina','North Dakota','Ohio','Oklahoma','Oregon','Pennsylvania','Rhode Island','South Carolina',\n", " 'South Dakota','Tennessee','Texas','Utah','Vermont','Virginia','Washington','West Virginia','Wisconsin','Wyoming'],\n", " \n", " 'Population': [5074296, 733391, 7421401, 3042017, 39237836, 5845526, 3655278, 1018396, 22355552, 11071651,\n", " 1455271, 1900923, 12582032, 6887080, 3200517, 2940865, 4512310, 4627000, 1362359, 6164660,\n", " 7033469, 10077331, 5743724, 2966786, 6169270, 1122870, 1961504, 3201896, 1395231, 9267130,\n", " 2113344, 19440469, 10698973, 786576, 11756000, 3986639, 4237256, 12813671, 1097379, 5341890,\n", " 919318, 7074700, 29945493, 3403183, 647064, 8728250, 7818819, 1794070, 5892539, 581381],\n", " \n", " 'Total_Crime': [3900, 4370, 4100, 4230, 2650, 3150, 2100, 2900, 2550, 2750,\n", " 2200, 1950, 2700, 2600, 2300, 2400, 2500, 4500, 1600, 2800,\n", " 1900, 2750, 2000, 4200, 3700, 1850, 2100, 3800, 1600, 1900,\n", " 4400, 2550, 3000, 1550, 2700, 3400, 2900, 2300, 2000, 3700,\n", " 1700, 3800, 2700, 1900, 1200, 2300, 2500, 2200, 2100, 1650]\n", "}\n", "\n", "df = pd.DataFrame(data)\n", "\n", "# Create scatter plot\n", "plt.figure(figsize=(10,7))\n", "plt.scatter(df['Population'], df['Total_Crime'], color='darkorange', alpha=0.7, edgecolors='black', label='States')\n", "\n", "# Calculate and plot trend line (linear regression)\n", "m, b = np.polyfit(df['Population'], df['Total_Crime'], 1)\n", "plt.plot(df['Population'], m * df['Population'] + b, color='red', linewidth=2, label='Trend Line')\n", "\n", "# Labels and title\n", "plt.title('Population vs Total Crime Rate (All U.S. States)')\n", "plt.xlabel('Population')\n", "plt.ylabel('Total Crime Rate (per 100,000)')\n", "plt.legend()\n", "\n", "# Optionally label a few outlier states\n", "for i, row in df.iterrows():\n", " if row['Total_Crime'] > 4000 or row['Population'] > 20000000:\n", " plt.text(row['Population']+200000, row['Total_Crime'], row['State'], fontsize=8)\n", "\n", "plt.grid(True, linestyle='--', alpha=0.6)\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6addee5e-39eb-4c3c-9cc3-39ca1c501896", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "e546fbe9-ae55-4e4f-948d-13b0632b60e2", "metadata": {}, "source": [ "##MAP 3: Voilent crimes VS Property Crimes" ] }, { "cell_type": "code", "execution_count": 55, "id": "6a62933a-b7b4-4205-8f6f-f39e774e351e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "import geopandas as gpd\n", "import pandas as pd\n", "\n", "\n", "geo_path = \"/home/jovyan/shared_data/data/geog407/exam1/us-states.json\"\n", "gdf_poly = gpd.read_file(geo_path)\n", "\n", "\n", "\n", "merged_poly = gdf_poly.merge(crime_latest, left_on=\"name\", right_on=\"State\")\n", "\n", "\n", "m_compare = folium.Map(location=[37.8, -96], zoom_start=4, tiles=\"cartodbpositron\")\n", "\n", "\n", "folium.Choropleth(\n", " geo_data=merged_poly,\n", " data=merged_poly,\n", " columns=[\"State\", \"Data.Rates.Violent.All\"],\n", " key_on=\"feature.properties.name\",\n", " fill_color=\"Reds\",\n", " fill_opacity=0.7,\n", " line_opacity=0.3,\n", " name=\"Violent Crimes (Red)\"\n", ").add_to(m_compare)\n", "\n", "folium.Choropleth(\n", " geo_data=merged_poly,\n", " data=merged_poly,\n", " columns=[\"State\", \"Data.Rates.Property.All\"],\n", " key_on=\"feature.properties.name\",\n", " fill_color=\"Blues\",\n", " fill_opacity=0.7,\n", " line_opacity=0.3,\n", " name=\"Property Crimes (Blue)\"\n", ").add_to(m_compare)\n", "\n", "\n", "for _, row in merged_poly.iterrows():\n", " centroid = row[\"geometry\"].centroid\n", " folium.map.Marker(\n", " [centroid.y, centroid.x],\n", " icon=folium.DivIcon(html=f\"\"\"\n", "
\n", " {row['State']}\n", "
\n", " \"\"\")\n", " ).add_to(m_compare)\n", "\n", "folium.LayerControl().add_to(m_compare)\n", "\n", "\n", "legend_html = \"\"\"\n", "
\n", " Legend: Crime Type by State
\n", " \n", " Violent Crimes (Red)
\n", " \n", " Property Crimes (Blue)\n", "
\n", "\"\"\"\n", "m_compare.get_root().html.add_child(folium.Element(legend_html))\n", "\n", "\n", "m_compare\n" ] }, { "cell_type": "markdown", "id": "e10d44de-a994-404e-8b28-6fe858d6ce2b", "metadata": {}, "source": [ "Analysis: Comparison of Violent and Property Crimes\n", "When comparing violent and property crimes, it becomes evident that property crimes generally outnumber violent crimes across most states. States with larger metropolitan areas—such as California, Florida, and New York—tend to exhibit both high violent and property crime rates. However, the variation between the two indicates that while violent crimes are more localized, property crimes are more widespread and often tied to economic factors like unemployment and poverty." ] }, { "cell_type": "markdown", "id": "fb640f60-4c42-4ece-a7f6-9c664b1c4ef9", "metadata": {}, "source": [ "Comparison bar chart for a few States" ] }, { "cell_type": "code", "execution_count": 44, "id": "e95c1b46-ba5b-4386-af2d-fbe025005434", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Example data\n", "states = ['California', 'Texas', 'Florida', 'Illinois', 'New York']\n", "violent = [400, 380, 420, 390, 370]\n", "property_ = [2200, 2100, 2300, 2000, 1950]\n", "\n", "x = np.arange(len(states)) # bar positions\n", "width = 0.35 # width of each bar\n", "\n", "# Create the bar chart\n", "plt.figure(figsize=(9,5))\n", "plt.bar(x - width/2, violent, width, color='red', label='Violent Crime')\n", "plt.bar(x + width/2, property_, width, color='blue', label='Property Crime')\n", "\n", "# Add labels and title\n", "plt.xlabel('State')\n", "plt.ylabel('Crimes per 100,000')\n", "plt.title('Comparison of Violent and Property Crimes')\n", "plt.xticks(x, states)\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "f6433d74-2662-4a24-9650-da88b74e59ca", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "60ea4fc5-5624-408a-b2ce-1c9e585b1aa6", "metadata": {}, "source": [ "##Ratio MAP 4= Violent Crimes as % of Total" ] }, { "cell_type": "code", "execution_count": 15, "id": "7c1d5bdd-c15e-47a6-93bb-1b161b3b487e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged[\"Violent_Share\"] = (merged[\"Data.Rates.Violent.All\"] / merged[\"Data.Totals.Property.All\"]) * 100\n", "\n", "m3 = folium.Map(location=[37.8, -96], zoom_start=4)\n", "folium.Choropleth(\n", " geo_data=merged,\n", " data=merged,\n", " columns=[\"State\", \"Data.Rates.Violent.All\"],\n", " key_on=\"feature.properties.name\",\n", " fill_color=\"Purples\",\n", " legend_name=\"Violent Crimes (%) of Total\"\n", ").add_to(m3)\n", "m3" ] }, { "cell_type": "markdown", "id": "9fdd7e92-62fc-4358-94cc-6eb1f0f46d0d", "metadata": {}, "source": [ "Analysis: \n", "The combined map of violent and property crimes gives a comprehensive view of overall crime intensity. States with large populations and major cities—particularly in the South and West—stand out with higher combined crime values. This aggregation highlights how population size, economic inequality, and urban concentration collectively influence total crime rates, emphasizing the need for region-specific law enforcement and community safety strategies.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "da0de847-3a3f-4595-86ec-9e3c110b13ca", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "976504c5-3ade-4b52-b86e-74526c79fd19", "metadata": {}, "source": [ "##Conclusion\n", "This project demonstrates how geospatial visualization can transform numerical crime data into actionable spatial insights. \n", "Mapping different crime types highlights important contrasts—violent crimes often relate to socioeconomic stress and urban density, \n", "while property crimes tend to correlate with opportunity and accessibility.\n", "\n", "The findings support the idea that spatial analysis is critical for evidence-based policy and planning.\n", "Future work could expand this analysis to a time-series perspective or integrate socioeconomic variables such as unemployment, income, or education \n", "levels to better explain spatial variation.\n", "\n", "By applying CyberGIS tools to real-world social data, this project exemplifies how geospatial science contributes to understanding and improving \n", "public safety at a national scale." ] }, { "cell_type": "markdown", "id": "7cafb7ee-98a9-4843-b89e-03b5b2bf155e", "metadata": {}, "source": [ "- Ruchi Pathak. ruchirp2@illinois.edu" ] } ], "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 }