This dataset contains a listing of incorporated places (cities and towns) and counties within the United States including the GNIS code, FIPS code, name, entity type and primary point (location) for the entity. The types of entities listed in this dataset are based on codes provided by the U.S. Census Bureau, and include the following: C1 - An active incorporated place that does not serve as a county subdivision equivalent; C2 - An active incorporated place legally coextensive with a county subdivision but treated as independent of any county subdivision; C3 - A consolidated city; C4 - An active incorporated place with an alternate official common name; C5 - An active incorporated place that is independent of any county subdivision and serves as a county subdivision equivalent; C6 - An active incorporated place that partially is independent of any county subdivision and serves as a county subdivision equivalent or partially coextensive with a county subdivision but treated as independent of any county subdivision; C7 - An incorporated place that is independent of any county; C8 - The balance of a consolidated city excluding the separately incorporated place(s) within that consolidated government; C9 - An inactive or nonfunctioning incorporated place; H1 - An active county or statistically equivalent entity; H4 - A legally defined inactive or nonfunctioning county or statistically equivalent entity; H5 - A census areas in Alaska, a statistical county equivalent entity; and H6 - A county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.

The Counties dataset was updated on October 31, 2023 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are mostly as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

This dataset contains geographic information concerning cities and towns in the United States, Puerto Rico, and the U.S. Virgin Islands. A city or town is a place with a recorded population, usually with at least one central area that provides commercial activities. Cities are generally larger than towns; no distinction is made between cities and towns in this map layer.

This data set describes metropolitan areas in the conterminous United States, developed from U.S. Bureau of the Census boundaries of Consolidated Metropolitan Statistical Areas (CMSA) and Metropolitan Statistical Areas (MSA), that have been processed to extract the largest contiguous urban area within each MSA or CMSA.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for counties and equivalent entities in United States of America. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). 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. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, 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) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

The Census data utilized for developing the Community Layer used 2010 TIGER/Line shapefile datasets (TIGER = Topologically Integrated Geographic Encoding and Referencing). TIGER/Line shapefiles are available for free download from the US Census Bureau and include various legal and statistical geographic areas for which the Census tabulates data. The shapefiles are designed to be used in a GIS environment, with the ability to directly link the geographic areas to Census data via a unique GEOID number.The following TIGER/Line datasets should be used: - Counties and Equivalent Entities –primary legal divisions within each state (counties, parishes, etc)- County Subdivisions –includes both legal areas (Minor Civil Divisions or MCDs) and various statistical areas- Places –includes both legal areas (Incorporated Places) and statistical areas (Census Designated Places or CDPs)- Blocks –the smallest geographical area for which Census population counts are recorded; blocks never cross boundaries of any entity for which the Census Bureau tabulates data, including counties, county subdivisions, places, and American Indian, Alaska Native, and Native Hawaiian (AIANNH) areas- American Indian, Alaska Native, and Native Hawaiian (AIANNH) AreasExtracting and Formatting CIS DataA key component of the community layer is the ability to link CIS information spatially. Data from CIS cannot directly be joined with Census data. The two datasets have community name discrepancies which impede an exact match. Therefore, CIS data needs to be formatted to match Census community names. A custom report can be obtained from CIS to include a CID number, Community Name, County, State, Community Status, and Tribal status for all CIS records. Make sure all CID numbers are six digits and you follow the CIS community naming convention outlined in Table 4.2.1.1 in the Community Layer Update Technical Guide 20131206. Converting the CIS name“ADDISON, VILLAGE OF” to “ADDISON TOWN”involves removing unneeded spaces, comma, and preposition to make the join successful to the Census data. Using a comprehensive report at a national level gains efficiencies as bulk edits can be made. Data for each state should be extracted as needed by separating the CIS data into each type of community corresponding to the Census geography layers used, and a new JoinID column (e.g. ADDISON TOWN) can be created for each dataset allowing the CIS data to be joined to the Census data.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

About this dataset

Context

This list ranks the 1546 cities in the New York by Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic White Population: This column displays the rank of cities in the New York by their Hispanic White population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic White Population: The Hispanic White population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New York Hispanic White Population: This tells us how much of the entire New York Hispanic White population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 2 cities in the Towns County, GA by Non-Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Black Population: This column displays the rank of cities in the Towns County, GA by their Non-Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Black Population: The Non-Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Towns County Non-Hispanic Black Population: This tells us how much of the entire Towns County, GA Non-Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

U.S. Census Populated Place Areas represents the 2020 U.S. Census populated place areas of the United States that include incorporated places, cities, and census designated places identified by the U.S. Census Bureau.This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data. The Population Class field values represent population ranges as follows:Population from 0 - 249Population from 250 - 499Population from 500 - 999Population from 1,000 - 2,499Population from 2,500 - 9,999Population from 10,000 - 49,999Population from 50,000 - 99,999Population from 100,000 - 249,999Population from 250,000 - 499,999Population 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.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for places and equivalent entities in United States of America.This layer both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

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 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities 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 BuffersWithout Coastal Buffers (this dataset)Place AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: 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.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".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 support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"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).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

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.

About this dataset

Context

This list ranks the 3065 counties in the United States by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of counties in the United States by their Some Other Race (SOR) population, using the most recent ACS data available.
• counties: The counties for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the counties is shown in this column.
• % of Total counties Population: This shows what percentage of the total counties population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total U.S. Other Race Population: This tells us how much of the entire United States Other Race population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The Urban Areas dataset was compiled on May 31, 2023 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are 2,645 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529088

About this dataset

Context

This list ranks the 1 cities in the Colonial Heights city, VA by Multi-Racial Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Multi-Racial Black Population: This column displays the rank of cities in the Colonial Heights city, VA by their Multi-Racial Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial Black Population: The Multi-Racial Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Colonial Heights city Multi-Racial Black Population: This tells us how much of the entire Colonial Heights city, VA Multi-Racial Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .