Author: J Benolkin, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): urban geography, gisRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 6. Geographic factors influence the distribution, functions, growth and patterns of cities and human settlements.Objectives: Students will be able to:

1. Create a custom map using Google Maps.
2. Apply one of three urban models to a U.S. city or urban area.Summary: This lesson follows an introduction to Urban Models. It asks students to apply the urban models of Concentric Zone, Sector, and Multi-Nuclei to U.S. cities.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Tacoma 1990 - USGS 1 meter Aerials for ArcGIS Online/Bing Maps/Google Maps, etc. This layer includes UP, Fircrest, Fife, and some of Federal Way.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: U.S. Geological SurveyFlight Time: July, 1990Metadata (Internal use only)Earth Explorer Full Display of Record 1 (Internal use only)Original ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.632392 East longitude: -122.304303 North latitude: 47.380453 South latitude: 47.118196Extent in the item's coordinate system: West longitude: 1112120.835383 East longitude: 1191291.333557 South latitude: 658000.509741 North latitude: 751710.870268

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

The global digital HD map market is booming, projected to reach $7.18 billion in 2025, with a robust CAGR fueling growth through 2033. Driven by autonomous vehicles and smart cities, this comprehensive analysis explores market segmentation, key players (ESRI, Google, TomTom), and regional trends. Discover insights into this rapidly expanding sector.

The professional map services market is booming, projected to reach $120 billion by 2033, driven by autonomous vehicles, LBS, and smart city initiatives. Explore market trends, key players (Google, TomTom, Esri), and regional growth in this in-depth analysis.

The real-time maps market is experiencing robust growth, driven by the increasing adoption of connected vehicles, the proliferation of smartphones with advanced location services, and the rising demand for precise navigation and location-based services across various sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. Key growth drivers include the integration of real-time maps into autonomous driving systems, the expansion of smart city initiatives reliant on accurate location data, and the growing popularity of location-based mobile applications. Companies like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Sanborn, and Baidu are key players in this dynamic market, continually innovating to provide enhanced map features and data accuracy. Competitive pressures are high, with a focus on data quality, coverage, and the integration of advanced technologies like AI and machine learning for improved traffic prediction and route optimization. While the market presents significant opportunities, challenges remain. Data security and privacy concerns, the need for continuous map updates to account for dynamic road conditions, and the high infrastructure costs associated with data collection and processing are some of the key restraints. Market segmentation is primarily based on technology (cloud-based vs. on-premise), application (automotive, navigation, logistics), and geography. North America and Europe currently hold a significant market share, but the Asia-Pacific region is poised for rapid growth fueled by increased smartphone penetration and burgeoning e-commerce activities that heavily rely on accurate location data. The future of the real-time maps market hinges on the continuous improvement of map accuracy, the integration of advanced technologies, and the effective addressal of data privacy and security concerns.

The Digital Geologic-GIS Map of City of Rocks National Reserve and Vicinity, Idaho is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (ciro_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ciro_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (ciro_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (ciro_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ciro_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ciro_geology_metadata_faq.pdf). Please read the ciro_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ciro_geology_metadata.txt or ciro_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Professional Map Services market is experiencing robust growth, projected to reach $1003.7 million in 2025. While the exact CAGR isn't provided, considering the rapid technological advancements in GIS, AI-powered mapping, and the increasing reliance on location-based services across various sectors, a conservative estimate of the CAGR for the forecast period (2025-2033) would be between 8% and 12%. This growth is fueled by several key drivers. The burgeoning adoption of smart city initiatives necessitates detailed and accurate mapping solutions. Furthermore, the increasing demand for precise navigation systems in the transportation and logistics industries, coupled with the rising popularity of location-based marketing and advertising, significantly contribute to market expansion. The integration of advanced technologies like AI and machine learning into mapping solutions is further enhancing accuracy, efficiency, and functionality, driving market growth. The market is segmented by service type (consulting and advisory, deployment and integration, support and maintenance) and application (utilities, construction, transportation, government, automotive, others), reflecting the diverse needs of various industries. The competitive landscape is characterized by a mix of established players like Esri, Google, TomTom, and Mapbox, alongside emerging innovative companies. Geographic expansion, particularly in developing economies with rapidly urbanizing populations, presents a significant opportunity for growth. However, challenges such as data security concerns and the high cost of advanced mapping technologies could act as potential restraints. The market's future growth hinges on continuous technological advancements and the expansion of data accessibility. The increasing adoption of cloud-based mapping solutions is streamlining data management and improving collaboration. Furthermore, the growing integration of map data into various applications, such as autonomous vehicles and augmented reality experiences, is creating new market avenues. Regulatory changes and data privacy regulations will play a crucial role in shaping the market landscape in the coming years. The diverse application segments ensure market resilience, as growth in one sector can offset potential slowdowns in another. The ongoing expansion into new geographical territories, particularly in Asia-Pacific and other developing regions, presents substantial growth opportunities for market participants.

Discover the booming digital map ecosystem market, projected to reach $450 billion by 2033. Explore key drivers, regional trends, and leading companies shaping this rapidly evolving landscape, including autonomous vehicle integration and LBS advancements. Learn more about market size, CAGR, and segmentation analysis in this comprehensive report.

The Navigation Electronic Map market is booming, projected to reach $3021 million by 2025 with a 25.4% CAGR. This report analyzes market drivers, trends, restraints, and key players, offering insights into 2D/3D maps across personal, commercial, and military applications. Explore regional market shares and future growth projections.

The Digital Geologic-GIS Map of Grant's Headquarters at City Point, Petersburg National Battlefield, Virginia is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (cipo_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (cipo_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (cipo_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (pete_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (pete_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cipo_geology_metadata_faq.pdf). Please read the pete_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cipo_geology_metadata.txt or cipo_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Explore the booming HD Live Map market, projected to surge to $26 billion by 2033 with a 22% CAGR. Discover key drivers, trends, restraints, and regional insights for autonomous driving, ADAS, and smart city applications.

This point feature layer represents the U.S.-Mexico border region sister cities. There are a total of 15 pairs of sister cities (31 total features) recognized in the U.S.-Mexico Border Program that span the entire length of the U.S.-Mexico border. These sister cities highlight the areas along the border that contain the most dense populations. Data was gathered by Jacoup Roiz from Google Maps GPS coordinates in 2019. These data support the U.S.-Mexico Border Program Map, which highlights the projects funded through the U.S.-Mexico Border Program (2013-2020) in both Region 9 and Region 6 of the U.S. EPA, including U.S. Federally recognized Tribal communities and states of Texas, New Mexico, Chihuahua, Nuevo Leon, Tamaulipas, Coahuila, California, Baja California, Sonora, and Arizona within 62 miles (100 kilometers) of the U.S.-Mexico Border. The projects stem from the Border 2020 framework that has five goals to reduce air pollution, improve access to clean water, promote materials and waste management, improve emergency preparedness, and enhance environmental stewardship, and fundamental strategies that includes children's health and environmental education and outreach. For more information about Border 2020 and/or current U.S.-Mexico Border program visit this website: https://www.epa.gov/usmexicoborder

Citywide streets and other features (pierhead, bulkhead, etc.) shown on the official City Map of New York City. Street lines unmapped streets that are NOT found on the City Map are included in this dataset for context and informational purposes only. An exhaustive record of mapped city streets, parks and public places, pierhead and bulkhead lines and borough and city boundaries digitized from georeferenced Borough Final Section maps (except Staten Island), alteration maps, zoning maps, and other reference data including: LION, Digital Tax Map (DTM), United States Army Corps of Engineers (USACE), pierhead and bulkhead line maps, aerial images and Google Streetview. Record streets and unmapped streets are included in this dataset for context and informational purposes only. This dataset is featured on the Department of City Planning's Street Map application: https:/streets.planning.nyc.gov/

Recording and processing a survey using an eye tracker The eye-tracker used is a Pupil Core from Pupil Labs. The basic eye tracker configuration, i.e. a fixation time of 80 ms to 200 ms, is kept for this experiment. The aim of the experiment is to understand what a person looks at to find their way around a multi-scale map and to understand the different strategies used. To do this, the user will be free to use the map as he wishes, i.e. he can use pan and zoom at will. Four types of tasks will be asked in order to have a maximum of types of use of multi-scale map.The first task is to simulate that a user is using an application like map or Google map and is looking for a specific address. The map application will then zoom in very strongly on the address. The user has little spatial context and it often takes some time to find his way around. To simulate the application, a point is placed on Paris or its surroundings and the display is very zoomed (Paris was chosen because most people have a more or less detailed mental map of Paris). The user is then asked to interact with the map (zooming and panning) until he feels he is sufficiently located, as he would if he had to search for a place on his mobile phone. When he is located, he just needs to move on to the next stage without asking for validation. This stage is carried out in four locations. The four points are located near Montmartre, at the entrance to the catacombs of Paris, in Vincennes and finally at Porte d'Asnières The second task is to find a place from an aerial image. The aerial image of a specific area is displayed and the map is zoomed out to the city where the location is located. The user must then try to find the location in the image. Unlike the first task, the user must request validation before proceeding to the next stage. This task is repeated in two different cities. The two images are the tête d'or park in Lyon and a building block next to a railway in Dijon. The third task also consists of finding a precise location using textual indications. The user still has to ask for validation to go to the next stage . This task is repeated in two different cities.The first was "to find the town hall which is just south of the town centre and next to the library" and the second was "to find the stadium east of the town centre and north of the river Vilaine with a north/south orientation. The last task builds on tasks 2 and 3. The map is again zoomed out, an aerial image appears and textual indications are given. This task is repeated on two different cities. The first image is of a building in beauvais with the indication: "the building is in the north west of sqare next to the SNCF station". The second one is a picture of a stadium in lyon with the indication: "the stadium is west of the confluence of lyon". data format : Coord_fixation_on_map_x_y: geolocated fixation point with x the survey type 1 or 2 and y the candidate number (id_fixation,x,y,zoom,etape) Pan: pan on the map during the survey Pan_fixation_on_map : fixation during a pan zoom: zoom on the map during the survey zoom_fixation_on_map: fixation during a zoom stat: number of zoom, pan and fixation per step result_map_x = map status every 100 ms during the survey x

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. This global map of Local Climate Zones, at 100m pixel size and representative for the nominal year …

Unlock insights into Moroccan banking customer experiences! 🇲🇦

This dataset contains scraped and cleaned Google Maps reviews for banks across all cities in Morocco. Collected as part of a collaborative student/freelancer project, it’s perfect for sentiment analysis, market research, or academic projects.

What’s Inside?

• 2 Versions:
  • Raw Data: As scraped from Google Maps.
  • Cleaned Data: Filtered to exclude non-bank businesses (e.g., cash services, unrelated entries).
• Columns:
  City, Business Name, Address, Phone Number, Website, Google Map ID, Review Text, Timestamp, Stars.
• Cities Sourced from data.gov.ma: Ensured comprehensive coverage of Moroccan regions.

Methodology:

1. City Identification: Used official data from data.gov.ma to target cities with banks.
2. Search Strategy: Queried “bank in [city name]” on Google Maps to compile business links.
3. Scraping: Extracted business details (name, address, etc.) and latest reviews using Python + Playwright (automation) and BeautifulSoup (parsing).
4. Cleaning: Removed duplicates and non-bank entries for accuracy.

Potential Use Cases:

• 📈 Sentiment Analysis: Analyze customer satisfaction trends.
• 🗺️ Geospatial Visualization: Map bank ratings by city/region.
• 🔍 Competitor Analysis: Compare bank reputations.
• 🎓 Academic Projects: Practice NLP, data cleaning, or visualization.

Tech Stack:

• Python 🐍
• Playwright (for browser automation)
• BeautifulSoup (HTML parsing)
• Pandas (data cleaning)

Why This Dataset?

• First-of-its-kind: Focused on Moroccan banks.
• Ready-to-use: Cleaned version requires minimal preprocessing.
• Transparent: Raw data included for reproducibility.

License: CC0: Public Domain (Free to use, modify, and share).

KML files of police districts in Chicago. To view or use these files, special GIS software such as Google Earth is required.