This world cities layer presents the locations of many cities of the world, both major cities and many provincial capitals.Population estimates are provided for those cities listed in open source data from the United Nations and US Census.

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

2015, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. Because some questions are only asked every other year in the BRFSS, there are 7 measures in this 2017 release from the 2014 BRFSS that were the same as the 2016 release.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

This layer presents the locations of major cities within the United States with populations of approximately 10,000 or greater, state capitals, and the national capital. Major Cities are locations containing population totals from the 2020 Census.The points represent U.S. Census Places polygons sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State). Attribute fields include 2020 total population from the U.S. Census Public Law 94 data that symbolize the city points using these six classifications: Class Population Range 5 2,500 – 9,999 6 10,000 – 49,999 7 50,000 – 99,999 8 100,000 – 249,999 9 250,000 – 499,999 10 500,000 and overThis ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

City boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_CITY: CDTFA incorporated city name
• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to

12/06/2024 - Updates to Ellendale, Fargo, Kindred, Lincoln, Mandan, Rugby and Tappen.12/06/2024 - Update to Lincoln and Bismarck Corporate Boundaries based on requests from Lincoln.6/27/2024 - Update to the Valley City and Dickinson Corporate Boundary based on requests from their GIS personal.4/8/2024 - Update to the Valley City Corporate Boundary12/04/2023 - Update to Fargo City Boundary7/23/2023 - Removed Church’s Ferry due to proclamation and notice of dissolution.7/01/2023 - Changes to Binford - Ordinance 51; Lidgerwood - Ordinance 2022-1; Killdeer Golf Course annexation; Bismarck based on current City of Bismarck GIS boundary9/26/2022 - Changes to Steele boundary per Kidder County 911 coordinator.9/23/2022 - Updates to Grand Forks, Mandan and Fargo7/01/2022 - Updates to Killdeer, Mandan and Williston per State Tax Dept changes. 2/14/2022- Updates to Minot -13th ST SE/31st AVE SE, Updates to Elgin, Horace and St. John.11/16/2021 -Updates to Bismarck, Fargo and Killdeer based on city ordinances.7/2/21 – Changes were made to the City of Bismarck, Fargo and Hillsboro to include local taxing jurisdiction boundary changes from the State Tax Commissioner.5/4/21 - Updates were made to the City of Wahpeton due to an annexation.4/29/21 - Updated Minot and Makoti3/5/21 - Updated an annexation to Arnegard that was submitted to the DOT by Mackenzie's County Public Works GIS Coordinator.1/21/21 - Update to Sentinel Butte per Golden Valley 911 Coordinator7/17/20 - Updates to Bismarck, Linton and Stanley6/1/20 - Updates to Killdeer, New Town and Surrey1/17/2020 - Boundary changes have been updated for Bismarck, Bowman Fargo, Garrison, Linton, and New Salem.3/5/19 - The corporate boundary of Surrey has been updated.12/26/18 - The following corporate boundaries have been updated: Bismarck, Lincoln, Grand Forks, Horace, Casselton, Fargo, Oxbow, Tioga and Stanley.6/19/18 - City of Maza is not incorporated based on the 2011-2013 North Dakota Blue book. Removed Maza.5/14/18 - Updated Dickinson, Watford City, Berthold, Minnewauken, and Cavalier.1/31/18 - Updated Dickinson, Mandan, Minot, Tioga, Devils Lake, Belfield, Washburn, Mohall, Minnewauken, Lincoln, Bismarck and Casselton. 10/24/17 - Updated Watford City and Makoti10/16/17 - The following cities have been updated: Jamestown, Milnor, Bismarck, Carrington, Casselton, Mandan, Minot, Stanley, Larimore, Crosby, and Watford City.1/10/17 - The following cities have been updated: Lehr, Grand Forks, Langdon, Drayton, Flasher, Glen Ullin, Watford City, Zap, Lignite, Hankinson, Beach, Underwood, South Heart, Devils Lake, all cities in Ward County, Cavalier, Bismarck, Lincoln, Fargo, West Fargo, Ayr, Briarwood, Casselton, Davenport, Enderlin, Grandin, Horace, and North River.9/19/16 - Updated the following cities: Watford City, Steele, Richardton, Berthold, Carpio, Burlington, Des Lacs, Donnybrook, Douglas, Kenmare, Makoti, Ryder, Sawyer, and Surrey.6/23/16 - Updated cities are as follows: All cities in Pembina, Morton, Richland, and Williams Counties. The cities of Bismarck, West Fargo, Harwood, Oxbow, Beach, Minot, Stanley, Jamestown, Fargo, Dickinson and New Town.9/28/15 - The following cites have had annexation: Stanley, Bottineau, Minot, Casselton, Belfield and Watford City.7/24/15 - Updated Grafton, Stanley, Bismarck, Williston, Horace, Fargo, Grand Forks, Watford City, Turtle Lake, Leeds, Maxbass and Medora1/16/15 - Updated Grafton, Stanley and Bismarck.11/3/2014 - Updated Bismarck, Mandan, Minot, Stanley, and Watford City7/16/14 - Corporate limits updated include: Mandan, Towner, Fargo, West Fargo, Grand Forks, Bismarck, Bowman, Watford City, Stanley, Tioga, Kenmare, Casselton, Minot, Carrington, Kindred, and Killdeer. The corporate limit updates consisted of receiving from the cities, shape files, CADD files, scanned images of annexations or by converting pdf files into images, rectifying them within ArcGIS, then heads-up digitizing. 7/29/13 - updated Stanley, Williston, Minot, and Bismarck.4/30/13 - updated Williston, Hazen, Minot, Dickinson, Valley City, Velva, Rugby, Bismarck, and Lincoln1/28/13 - updated Valley City, Grand Forks, Bismarck, Williston, Jamestown, Harvey, Mohall, Park River, Ray, Rugby, Stanley, Tioga, Mayville and Glenfield10/9/12 - updated Williston and Dickinson6/20/12 - updated Williston via shapefile from city.3/20/12 - updated Bismarck and Minot10/3/2011 - Edited corporate limits for Bottineau, Grand Forks, Bismarck, Grafton, Fargo, West Fargo, Horace, Dickinson, Williston, Valley City and Devils Lake.2/4/11 - Removed urban areas so only corporate boundaries remain. Removed boolean field named URBAN_AREA. Updated corporate limist in Dickinson and cities with Cass county. 6/24/10 - Stanley, Lincoln, Oakes, Hankinson, Enderlin, Ellendale, Linton, Carrington, Minot, and Kulm corportate limits were changed 6/18/09 - Stanley, Wahpeton, Center, Watford City, Williston, Grand Forks, Killdeer, Beulah, Beach, Hazen, Garrison, Washburn, Bismarck and Lincoln corporate limits were changed 3/24/08 - Added Milton, Drayton, and Cavalier Boudaries updated: Park River 1/16/08 - Boundaries updated: Devils Lake, Glen Ullin, Langdon, Minnewaukan, Northwood, Thompson 2/13/07 - Boundaries updated: Amenia, Arthur, Bismarck, Bottineau, Buffalo, Casstleton, Davenport, Dickinson, Enderlin, Gardner, Grand Forks, Grandin, Harvey, Harvey, Hillsboro, Horace, Hunter, Jamestown, Kindred, Mapleton, Mayville, New Rockford, Oxbox, Page, Prairie Rose, Relies Acres, Tappen, Towner City 1/10/06 - Boundaries updated: Wishek, Fargo, Lincoln, Bottineau, Williston, Grand Forks, Granville, Velva, Stanley, urban areas in Fargo, West Fargo, Bismarck and Mandan. Deleted Larson This data came from the NDDOT's Mapping Section. The original data was digitized from hand scribed maps and registered to the 1:24000 USGS PLSS data. It was converted from a projection (NAD 1983 UTM Zone 14N) to a Geographic coordinate system.

This data set consists of 6 classes of zoning features: zoning districts, special purpose districts, special purpose district subdistricts, limited height districts, commercial overlay districts, and zoning map amendments.

All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

2014, 2013. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level.

Analysis of ‘500 Cities: City-level Data (GIS Friendly Format), 2016 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2d5749f7-4fca-46d0-8550-b5eddc851aad on 26 January 2022.

--- Dataset description provided by original source is as follows ---

2014, 2013. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level.

--- Original source retains full ownership of the source dataset ---

City of Quincy, MA GIS Viewer

Analysis of ‘500 Cities: City-level Data (GIS Friendly Format), 2017 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3ce84b26-03d1-4be3-aea0-fa629a51ec48 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

2015, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. Because some questions are only asked every other year in the BRFSS, there are 7 measures in this 2017 release from the 2014 BRFSS that were the same as the 2016 release.

--- Original source retains full ownership of the source dataset ---

Geospatial data about US Cities and Towns (Local). Export to CAD, GIS, PDF, CSV and access via API.

This feature service includes change areas for city boundaries and county line adjustments filed in accordance with Government Code 54900. The boundaries in this map are based on the State Board of Equalization's tax rate area maps for the assessment roll year specified in the COFILE field. The information is updated regularly within 10 business days of the most recent BOE acknowledgement date. Some differences may occur between actual recorded boundaries and boundary placement in the tax rate area GIS map. Tax rate area boundaries are representations of taxing jurisdictions for the purpose of determining property tax assessments and should not be used to determine precise city or county boundary line locations. BOE_CityAnx Data Dictionary: COFILE = county number - assessment roll year - file number; CHANGE = affected city, unincorporated county, or boundary correction; EFFECTIVE = date the change was effective by resolution or ordinance; RECEIVED = date the change was received at the BOE; ACKNOWLEDGED = date the BOE accepted the filing for inclusion into the tax rate area system; NOTES: additional clarifying information about the action. BOE_CityCounty Data Dictionary: COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the BOE's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).

The digital filing is created from urban planning announcement data provided by the urban development bureau. The fields include number, administrative district, use zone, zone abbreviation, urban planning name, establishment date, area, building coverage ratio, floor area ratio, maximum volume ratio, urban planning area, detailed planning area, remarks, drawing revision date, publication document number, and project name.

Complete accounting of all incorporated cities, including the boundary and name of each individual city. From 2009 to 2022 CAL FIRE maintained this dataset by processing and digitally capturing annexations sent by the state Board of Equalization (BOE). In 2022 CAL FIRE began sourcing data directly from BOE, in order to allow the authoritative department provide data directly. This data is then adjusted so it resembles the previous formats.Processing includes:• Clipping the dataset to traditional state boundaries• Erasing areas that span the Bay Area (derived from calw221.gdb)• Querying for incorporated areas only• Dissolving each incorporated polygon into a single feature• Calculating the COUNTY field to remove the word 'County'Version 24_1 is based on BOE_CityCounty_20240315, and includes all annexations present in BOE_CityAnx2023_20240315. Note: The Board of Equalization represents incorporated city boundaries as extending significantly into waterways, including beyond coastal boundaries. To see the representation in its original form please reference the datasets listed above.Note: The Board of Equalization represents incorporated city boundaries is extending significantly into waterways, including beyond coastal boundaries. To see the representation in its original form please reference the datasets listed above.

City of Pittsfield, MA GIS Viewer

2015, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. Because some questions are only asked every other year in the BRFSS, there are 7 measures in this 2017 release from the 2014 BRFSS that were the same as the 2016 release.

2016, 2015. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) in this 2018 release from the 2015 BRFSS that were the same as the 2017 release.