U.S. Department of Commerce, U.S. Census Bureau, Geography Division
2008
TIGER/Line Shapefile, 2008, state, Pennsylvania, County and Equivalent
2008
vector digital data
http://www.census.gov/geo/www/tiger
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database 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 the shapefiles can be combined to cover the whole nation.
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.
Pennsylvania(42)
200801
200807
Publication Date
TIGER/Line Shapefiles are extracted from the Census MAF/TIGER database. 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.
-80.519895
-74.689502
42.516072
39.719799
ISO 19115 Topic Categories
boundaries
None
State or equivalent entity
Polygon
FIPS Publication 5-2, FIPS Publication 6-4, FIPS Publication 8-6, FIPS Publication 9-1,FIPS Publication55-4
United States
U.S.
State or Equivalent Entity
Pennsylvania
PA
42
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.
Yes, the data are free from Title 13 restrictions
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
Mailing address
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
tiger@census.gov
8859part1
eng
Accurate against Federal Information Processing Standards (FIPS), FIPS Publication 6-4, and FIPS-55 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 FIPS 55. 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
200801
200807
Publication Date
MAF/TIGER
The selected geographic and cartographic information (line segments) are derived from the U.S. Census Bureau's Master Address File Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database.
TIGER/Line Shapefiles are extracted from the Census MAF/TIGER database by nation, state, county, and entity. Census MAF/TIGER data for the nation, state, county, and entity are then distributed among 58 shapefiles each containing attributes for line, polygon, or landmark geographic data.
withheld
2008
Federal Information Processing Standards (FIPS), ANSI, and feature names.
Vector
Simple
FALSE
0
TRUE
FALSE
0.000458
0.000458
Decimal degrees
North American Datum of 1983 in the 48 contiguous states, the District of Columbia, Alaska, Hawaii, Puerto Rico, the Virgin Islands of the United States, and the Pacific Island Areas.
Geodetic Reference System 80
6378137
298257
COUNTY.shp
Current County and Equivalent State-based entities
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.
STATEFP
Current state Federal Information Processing Standards (FIPS) code
U.S. Census Bureau
FIPS PUB 5-2, Codes for the Identification of the States, the District of Columbia and the Outlying Areas of the United States, and Associated Areas
U.S. Census Bureau
STATEFP
String
2
0
0
COUNTYFP
Current county Federal Information Processing Standards (FIPS) code
U.S. Census Bureau
FIPS PUB 6-4, Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas
U.S. Census Bureau
COUNTYFP
String
3
0
0
COUNTYNS
Current county ANSI code
U.S. Census Bureau
data are unavailable
data are unavailable
COUNTYNS
String
8
0
0
CNTYIDFP
Current county identifier; a concatenation of current state Federal Information Processing Standards (FIPS) code and county FIPS code
U.S. Census Bureau
FIPS PUB 5-2, Codes for the Identification of the States, the District of Columbia and the Outlying Areas of the United States, and Associated Areas, FIPS PUB 6-4, Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas
U.S. Census Bureau
CNTYIDFP
String
5
0
0
NAME
Current county name
U.S. Census Bureau
FIPS PUB 6-4, Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas
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
FIPS PUB 6-4, Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas and the translated legal/statistical area description code for county that appears in LSAD00
U.S. Census Bureau
NAMELSAD
String
100
0
0
LSAD
Current legal/statistical area description code for county
U.S. Census Bureau
00
Blank
U.S. Census Bureau
03
City and Borough
U.S. Census Bureau
04
Borough
U.S. Census Bureau
05
Census Area
U.S. Census Bureau
06
County
U.S. Census Bureau
07
District
U.S. Census Bureau
10
Island
U.S. Census Bureau
12
Municipality
U.S. Census Bureau
13
Municipio
U.S. Census Bureau
15
Parish
U.S. Census Bureau
25
city
U.S. Census Bureau
LSAD
String
2
0
0
CLASSFP
Current Federal Information Processing Standards (FIPS) 55 class code
U.S. Census Bureau
C7
An incorporated place that is independent of any county
U.S. Census Bureau
H1
An active county or equivalent feature
U.S. Census Bureau
H4
An inactive county or equivalent feature
U.S. Census Bureau
H5
A statistical county equivalent feature
U.S. Census Bureau
H6
A county that is coextensive with an incorporated place, part of an incorporated place, or a consolidated city and the governmental functions of the county are part of the municipal govenment
U.S. Census Bureau
CLASSFP
String
2
0
0
MTFCC
MAF/TIGER feature class code
U.S. Census Bureau
G4020
County or Equivalent Feature
U.S. Census Bureau
MTFCC
String
5
0
0
CSAFP
Current combined statistical area code
U.S. Census Bureau
100
599
CSAFP
String
3
0
0
CBSAFP
Current metropolitan statistical area/micropolitan statistical area code
U.S. Census Bureau
10000
49999
CBSAFP
String
5
0
0
METDIVFP
Current metropolitan division code
U.S. Census Bureau
10004
49994
METDIVFP
String
5
0
0
FUNCSTAT
Current functional status
U.S. Census Bureau
A
Active government providing primary general-purpose functions
U.S. Census Bureau
B
Active government that is partially consolidated with another government but with separate officials providing primary general-purpose functions
U.S. Census Bureau
C
Active government consolidated with another government with a single set of officials
U.S. Census Bureau
F
Fictitious Entity created to fill the Census Bureau geographic hierarchy
U.S. Census Bureau
G
Active government that is subordinate to another unit of government
U.S. Census Bureau
N
Nonfunctioning legal entity
U.S. Census Bureau
S
Statistical Entity
U.S. Census Burea
FUNCSTAT
String
1
0
0
COUNTY
COUNTY
Integer
4
0
0
NAME_1
NAME_1
String
254
0
0
Total_Popu
Total_Popu
Double
8
0
0
Pop_Under_
Pop_Under_
Double
8
0
0
Per_18_und
Per_18_und
Double
8
0
0
Pop_64_Und
Pop_64_Und
Double
8
0
0
Pop_62_ove
Pop_62_ove
Double
8
0
0
Pop_65_ove
Pop_65_ove
Double
8
0
0
Per_65_Plu
Per_65_Plu
Double
8
0
0
Median_Age
Median_Age
Double
8
0
0
Median
Median
Integer
4
0
0
Med_Age
Med_Age
Integer
4
0
0
Under_18
Under_18
Integer
4
0
0
Over_65
Over_65
Integer
4
0
0
Young_O
Young_O
String
254
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.
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
Mailing address
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
tiger@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://www.census.gov/geo/www/tiger
DVD-ROM (Only if offered offline)
ISO 9660
The online copy of the TIGER/Line files 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.
2008
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
Mailing address
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
United States
301-763-1128
301-763-4710
tiger@census.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
8859part1
tl_2008_42_county.shp.xml
eng
Esri ArcGIS 13.5.0.57366
PA_Counties_clipselection_CopyFeatures
PA_Counties_clipselection_CopyFeatures
002
0.000
Geographic
GCS_North_American_1983
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/3.5.0'><WKT>GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4269]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>1111948722.2222221</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>4269</WKID><LatestWKID>4269</LatestWKID></GeographicCoordinateSystem>
20260303
15155400
20260303
15155400
File Geodatabase Feature Class
0.000
dataset
EPSG
6.5(3.0.1)
0
20260303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==