20240822
12435600
1.0
FALSE
Zip_Code_of_Employees
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/3.2.0'><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>11258999068426.238</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>
20240822
12435600
20240822
12435600
Esri ArcGIS 13.2.2.49743
Zip_Code_of_Employees
Shapefile
0.000
dataset
EPSG
6.5(3.0.1)
0
Simple
FALSE
0
FALSE
FALSE
Zip_Code_of_Employees
Feature Class
0
FID
FID
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.
ZCTA5CE20
ZCTA5CE20
String
5
0
0
GEOID20
GEOID20
String
5
0
0
GEOIDFQ20
GEOIDFQ20
String
14
0
0
CLASSFP20
CLASSFP20
String
2
0
0
MTFCC20
MTFCC20
String
5
0
0
FUNCSTAT20
FUNCSTAT20
String
1
0
0
ALAND20
ALAND20
Double
14
14
0
AWATER20
AWATER20
Double
14
14
0
INTPTLAT20
INTPTLAT20
String
11
0
0
INTPTLON20
INTPTLON20
String
12
0
0
Zip
Zip
String
254
0
0
F_
F_
String
254
0
0
F3
F3
String
254
0
0
F4
F4
String
254
0
0
Code
Code
Double
19
0
0
Employees
Employees
Double
19
0
0
ObjectID
ObjectID
Integer
9
9
0
20240822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