20180504
11115800
1.0
FGDC CSDGM Metadata
FALSE
Divisions_20211214_v4
-179.147340
179.778470
-14.552549
71.352561
1
002
Geographic
GCS_WGS_1984
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.8.0'><WKT>GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4326]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.983152841195215e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4326</WKID><LatestWKID>4326</LatestWKID></GeographicCoordinateSystem>
20211214
17051900
20211214
17051900
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Customer Services Branch
301.763.1128
301.763.4710
4600 Silver Hill Road
Washington
DC
20233-7400
US
geo.geography@census.gov
ArcGIS Metadata
1.0
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Customer Services Branch
301.763.1128
301.763.4710
4600 Silver Hill Road
Washington
DC
20233-7400
US
geo.geography@census.gov
The online cartographic boundary files may be downloaded without charge.
To obtain more information about ordering Cartographic Boundary Files visit https://www.census.gov/geo/www/tiger.
SHP
PK-ZIP, version 1.93A or higher
The cartographic boundary files contain geographic data only and do not include display mapping software or statistical data. For information on how to use cartographic boundary file data with specific software package users shall contact the company that produced the software.
https://www2.census.gov/geo/tiger/GENZ2017/shp/cb_2017_us_county_5m.zip
https://www2.census.gov/geo/tiger/GENZ2017/shp/cb_2017_us_county_5m.zip
File Geodatabase Feature Class
Divisions_20211214_v4
2018-03-01
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Cartographic Products and Services Branch
vector digital data
Cartographic Boundary Files
2017
The 2017 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. 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 generalized boundaries for counties and equivalent entities are based on those as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
Divisions for DR-4630-KY
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Customer Services Branch
301.763.1128
301.763.4710
4600 Silver Hill Road
Washington
DC
20233-7400
US
geo.geography@census.gov
US
United States
ISO 3166 Codes for the representation of names of countries and their subdivisions
District
City and Borough
SHP
County equivalent
Borough
Municipio
Cartographic Boundary
Generalized
County
Island
Parish
City
2017
Municipality
Independent City
Census Area
State
Boundaries
ISO 19115 Topic Categories
4630
Divisions
No warranty, expressed or implied is made with regard to the accuracy of these data, and no liability is assumed by the U.S. Government in general or the U.S. Census Bureau in specific as to the spatial or attribute accuracy of the data. The act of distribution shall not constitute any such warranty and no responsibility is assumed by the U.S. government in the use of these files. The boundary information is for small-scale mapping purposes only; boundary depiction and designation for small-scale mapping purposes do not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.
The intended display scale for this file is 1:5,000,000. This file should not be displayed at scales larger than 1:5,000,000. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships.
-179.14734
179.77847
-14.552549
71.352561
publication date
2018-03-01
2018-03-01
Esri ArcGIS 12.8.0.29751
1
-179.147340
179.778470
71.352561
-14.552549
The Census Bureau performed automated tests to ensure logical consistency of the source database. Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. All polygons were tested for closure. The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database. Standard geographic codes, such as INCITS (formerly FIPS) codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities. The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files. Feature attribute information has been examined but has not been fully tested for consistency. For the cartographic boundary files, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the file's data table. For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature. Cartographic Boundary File multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a file, the object count will be less than the actual number of G-polygons.
The cartographic boundary files are generalized representations of extracts taken from the MAF/TIGER Database. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Some small geographic areas, holes, or discontiguous parts of areas may not be included in generalized files if they are not visible at the target scale.
Accurate against American National Standards Institute (ANSI) Publication INCITS 446-2008 (Geographic Names Information System (GNIS)) at the 100% level for the codes and base names present in the file. The remaining attribute information has been examined but has not been fully tested for accuracy.
Data are not accurate. Data are generalized representations of geographic boundaries at 1:5,000,000.
Spatial data were extracted from the MAF/TIGER database and processed through a U.S. Census Bureau batch generalization system.
2018-03-01
MAF/TIGER
All spatial and feature data
Census MAF/TIGER database
MAF/TIGER
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Customer Services Branch
The dates describe the effective date of 2017 cartographic boundaries.
2017-06-01
2017-05-01
INCITS (formerly FIPS) codes
0
Divisions_20211214_v4
Current County and Equivalent (national)
U.S. Census Bureau
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
Subregion
Subregion
String
255
0
0
SHAPE_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
SHAPE_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
dataset
EPSG
6.2(3.0.1)
Simple
FALSE
0
TRUE
FALSE
20211214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