20250623
11101600
1.0
FALSE
URBAN_AREAS_2020
002
Projected
GCS_North_American_1983
Linear Unit: Meter (1.000000)
NAD_1983_StatePlane_South_Carolina_FIPS_3900
<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/3.1.0'><WKT>PROJCS["NAD_1983_StatePlane_South_Carolina_FIPS_3900",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]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",609600.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-81.0],PARAMETER["Standard_Parallel_1",32.5],PARAMETER["Standard_Parallel_2",34.83333333333334],PARAMETER["Latitude_Of_Origin",31.83333333333333],UNIT["Meter",1.0],AUTHORITY["EPSG",32133]]</WKT><XOrigin>-37462300</XOrigin><YOrigin>-28285700</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>32133</WKID><LatestWKID>32133</LatestWKID></ProjectedCoordinateSystem>
20250620
11302600
20250620
12075300
150000000
5000
ItemDescription
Esri ArcGIS 13.1.7.41833
URBAN AREAS 2020
The smoothed Urban Areas are at the discretion of the State DOTs and are sent to the Federal DOT. These smoothed Urban Areas 2020 were updated in and finalized in 2023. Urban Area defined as over 5000 people or 2500 housing units.
SCDOT
Urban Areas
Functional Class roads
Transportation
U. S. Census Bureau, SCDOT, GIS office, Roads Data Services.
<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN>The Urban Areas 2020 depict all urban designated areas in the state and which MPO they belong to, the census population, and area (square miles). LAND_AREA_sqmi was taken from the 2024 census files here: LandAreaPop_UrbanAreas.
</SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN>There are 44 Urban Areas 2020, which are 2 </SPAN><SPAN STYLE="text-decoration:underline;"><SPAN>less than</SPAN></SPAN><SPAN> the URBAN_AREAS_2010 feature. Data updated from the the 2020 Census data.</SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>https://www.scdot.org/disclaimer.html</SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The South Carolina Department of Transportation makes no representations or warranties,</SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN> implied or expressed, concerning the accuracy, completeness, reliability, or suitability,</SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN> for any particular purpose of the information and data contained on this map or any information</SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN> furnished in connection herewith.</SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
File Geodatabase Feature Class
dataset
EPSG
6.12(3.0.1)
0
Simple
FALSE
0
TRUE
FALSE
URBAN_AREAS_2020
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.
AREA
AREA
Double
8
0
0
URBAN_NAME
URBANNAME
String
255
0
0
URBAN_CODE
URBANCODE
Integer
4
0
0
UACE
UACE
String
5
0
0
NAME
NAME
String
255
0
0
TOT_POP
TOTPOP
Double
8
0
0
POP_DENSITY
POPULDENSI
Double
8
0
0
POPULATION
POPULATION
Double
8
0
0
LANDAREA_sqmi
LANDAREA_sqmi
Double
8
0
0
NEW_AREA
NEWAREA
Double
8
0
0
HOUSEUNITS
HOUSEUNITS
Double
8
0
0
ENG_DIST
ENG_DIST
String
1
0
0
CENSUSVINTAGE
YEAR_RECOR
Integer
4
0
0
STATEID
STATEID
Integer
4
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.
20250620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=