U.S. Department of Commerce, U.S. Census Bureau, Geography
Division
2017
TIGER/Line Shapefile, 2017, nation, U.S., Current County and Equivalent National Shapefile
2017
vector digital data
http://www2.census.gov/geo/tiger/TIGER2017/COUNTY/tl_2017_us_county.zip
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 as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles.
201606
201705
publication date
No changes or updates will be made to this version of the TIGER/Line Shapefiles. Future releases of TIGER/Line Shapefiles will reflect updates made to the Census MAF/TIGER database.
-179.231086
179.859681
71.439786
-14.601813
NGDA Portfolio Themes
NGDA
Governmental Units and Administrative and Statistical Boundaries Theme
National Geospatial Data Asset
None
Nation
Polygon
County
Borough
Parish
Municipio
ISO 19115 Topic Categories
Boundaries
ANSI INCITS 38:2009 (Formerly FIPS 5-2),
ANSI INCITS 31:2009 (Formerly FIPS 6-4),ANSI
INCITS 454:2009 (Formerly FIPS 8-6), ANSI INCITS
455:2009(Formerly FIPS 9-1), ANSI INCITS 446:2008 (Geographic Names Information System (GNIS))
United States
U.S.
None
The TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. 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 in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest.
U.S. Department of Commerce, U.S. Census Bureau,
Geography Division, Spatial Data Collection and Products Branch
mailing
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
geo.geography@census.gov
Accurate against National Standard Codes, Federal Information Processing (FIPS) and the Geographic Names Information System (GNIS) at the 100% level for the codes and base names. The remaining attribute information has been examined but has not been fully tested for accuracy.
The Census Bureau performed automated tests to ensure logical consistency and limits of shapefiles. Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. All polygons are 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 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. Most of the codes for geographic entities except states, counties, urban areas, Core Based Statistical Areas (CBSAs), American Indian Areas (AIAs), and congressional districts were provided to the Census Bureau by the USGS, the agency responsible for maintaining the Geographic Names Information System (GNIS). Feature attribute information has been examined but has not been fully tested for consistency.
For the TIGER/Line Shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute 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. TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons.
Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created.
U.S. Department of Commerce, U.S. Census Bureau, Geography Division
Unpublished material
Census MAF/TIGER database
online
201606
201705
publication date
MAF/TIGER
All line segments
TIGER/Line Shapefiles are extracted from the Census MAF/TIGER database by nation, state, county, and entity. Census MAF/TIGER data for all of the aforementioned geographic entities are then distributed among the shapefiles each containing attributes for line, polygon, or landmark geographic data.
withheld
2017
Federal Information Processing Series (FIPS), Geographic Names Information System (GNIS), and feature names.
Vector
Simple
FALSE
0
TRUE
FALSE
North American Datum of 1983
Geodetic Reference System 80
6378137
298.257
COUNTY.shp
Current County and Equivalent National
U.S. Census Bureau
Feature Class
0
OBJECTID_1
OBJECTID_1
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.
STATEFP
Current state Federal Information Processing Series (FIPS) code
U.S. Census Bureau
National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents
U.S. Census Bureau
STATEFP
String
2
0
0
COUNTYFP
Current county Federal Information Processing Series (FIPS) code
U.S. Census Bureau
National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents
U.S. Census Bureau
COUNTYFP
String
3
0
0
COUNTYNS
Current county GNIS code
U.S. Census Bureau
INCITS 446:2008 (Geographic Names Information System (GNIS)), Identifying Attributes for Named Physical and Cultural Geographic Features (Except Roads and Highways) of the United States, Its Territories, Outlying Areas, and Freely Associated Areas, and the Waters of the Same to the Limit of the Twelve-Mile Statutory Zone
U.S. Geological Survey (USGS)
COUNTYNS
String
8
0
0
GEOID
County identifier; a concatenation of Current state FIPS code and county FIPS code
U.S. Census Bureau
The GEOID attribute is a concatenation of the state FIPS code followed by the county FIPS code. No spaces are allowed between the two codes. The State FIPS code is taken from "National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States". The county FIPS code is taken from "National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents".
GEOID
String
5
0
0
NAME
Current county name
U.S. Census Bureau
National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents
U.S. Census Bureau
NAME
String
100
0
0
NAMELSAD
Current name and the translated legal/statistical area description for county
U.S. Census Bureau
The NAMELSAD attribute is a concatenation of the county name followed by the translated legal/statistical area description. No spaces are allowed between the two codes.
NAMELSAD
String
100
0
0
ALAND
Current land area
U.S. Census Bureau
0
9,999,999,999,999
ALAND
Double
8
0
0
AWATER
Current water area
U.S. Census Bureau
0
9,999,999,999,999
AWATER
Double
8
0
0
INTPTLAT
Current latitude of the internal point
U.S. Census Bureau
-90.000000
90.000000
INTPTLAT
String
11
0
0
INTPTLON
Current longitude of the internal point
U.S. Census Bureau
-180.000000
180.000000
INTPTLON
String
12
0
0
Shape_Leng
Shape_Leng
Double
8
0
0
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
GEOID_Data
GEOID_Data
String
254
0
0
ALAND12
ALAND12
Double
8
0
0
AWATER12
AWATER12
Double
8
0
0
FIPS_CNTY
FIPS_CNTY
Integer
4
0
0
GEO_ID
GEO_ID
String
254
0
0
FIPS_CNT_1
FIPS_CNT_1
Integer
4
0
0
FIPS
FIPS
Integer
4
0
0
NAME_1
NAME_1
String
254
0
0
CNTY
CNTY
String
254
0
0
CNTY1
CNTY1
Double
8
0
0
P2012
P2012
Double
8
0
0
P2017
P2017
Double
8
0
0
P2021
P2021
Double
8
0
0
p2023
p2023
Double
8
0
0
FIPS_1
FIPS
Integer
4
0
0
State
State
String
8000
0
0
County
County
String
8000
0
0
GROC16
GROC16
Integer
4
0
0
GROCPTH16
GROCPTH16
Double
8
0
0
PCT_LACCESS_HHNV15
PCT_LACCESS_HHNV15
Double
8
0
0
PCT_LACCESS_LOWI15
PCT_LACCESS_LOWI15
Double
8
0
0
PCT_LACCESS_POP15
PCT_LACCESS_POP15
Double
8
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
ObjectID
ObjectID
Integer
4
0
0
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
mailing
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
geo.geography@census.gov
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 in the TIGER/Line Shapefiles is for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes do not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.
TGRSHP (compressed)
PK-ZIP, version 1.93 A or higher
http://www2.census.gov/geo/tiger/TIGER2017/COUNTY/tl_2017_us_county.zip
The online copy of the TIGER/Line Shapefiles may be accessed without charge.
To obtain more information about ordering TIGER/Line Shapefiles visit http://www.census.gov/geo/www/tiger
The TIGER/Line shapefiles contain geographic data only and do not include display mapping software or statistical data. For information on how to use the TIGER/Line shapefile data with specific software package users shall contact the company that produced the software.
20170601
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
mailing
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
geo.geography@census.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
Esri ArcGIS 13.2.0.49743
PA_CNTY_ExportFeatures1
PA_CNTY_ExportFeatures1
002
0.000
Projected
GCS_NAD_1983_2011
Linear Unit: Foot_US (0.304801)
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' 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/3.2.0'><WKT>PROJCS["NAD_1983_2011_StatePlane_Pennsylvania_South_FIPS_3702_Ft_US",GEOGCS["GCS_NAD_1983_2011",DATUM["D_NAD_1983_2011",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-77.75],PARAMETER["Standard_Parallel_1",39.93333333333333],PARAMETER["Standard_Parallel_2",40.96666666666667],PARAMETER["Latitude_Of_Origin",39.33333333333334],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",6565]]</WKT><XOrigin>-119214200</XOrigin><YOrigin>-96198500</YOrigin><XYScale>3048.0060960121918</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>103142</WKID><LatestWKID>6565</LatestWKID></ProjectedCoordinateSystem>
NAD_1983_2011_StatePlane_Pennsylvania_South_FIPS_3702_Ft_US
20250428
21513800
20250428
21513800
20230328
14235600
1.0
FALSE
File Geodatabase Feature Class
0.000
dataset
EPSG
8.2.10(10.3.1)
0
20250428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==