20220602
19043000
1.0
FALSE
Tarpukario_apskritys
002
Server=GISWEB; Service=sde:postgresql:GISWEB; Database=tarmynas_test; User=lkiadmtst; Version=sde.DEFAULT
ArcSDE Connection
Projected
GCS_LKS_1994
Linear Unit: Meter (1.000000)
LKS_1994_Lithuania_TM
<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/2.9.0'><WKT>PROJCS["LKS_1994_Lithuania_TM",GEOGCS["GCS_LKS_1994",DATUM["D_Lithuania_1994",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",24.0],PARAMETER["Scale_Factor",0.9998],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3346]]</WKT><XOrigin>-5122000</XOrigin><YOrigin>-10000100</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>2600</WKID><LatestWKID>3346</LatestWKID></ProjectedCoordinateSystem>
FeatureClassToFeatureClass Apskritys "C:\Users\Aidas\Documents\ArcGIS\Projects\Linguistic Lanscape LT_PL\Linguistic Lanscape LT_PL.gdb" Tarpukario_apskritys # "OBJECTID "OBJECTID" true true false 10 Long 0 10,First,#,Apskritys,OBJECTID,-1,-1;geometry_L "geometry_L" true true false 19 Double 0 0,First,#,Apskritys,geometry_L,-1,-1;geometry_A "geometry_A" true true false 19 Double 0 0,First,#,Apskritys,geometry_A,-1,-1;Apskritis "Apskritis" true true false 50 Text 0 0,First,#,Apskritys,Apskritis,0,50" #
DeleteField Tarpukario_apskritys geometry_L "Delete Fields"
DeleteField Tarpukario_apskritys geometry_A "Delete Fields"
CopyMultiple "'C:\Users\Aidas\Documents\ArcGIS\Projects\Linguistic Lanscape LT_PL\Linguistic Lanscape LT_PL.gdb\Tarpukario_apskritys' FeatureClass" C:\Users\Aidas\Dropbox\asmeninis\Tarmynas\Tarmynas_connections\GISWEB@tarmynas_test@lkiadmtst.sde Tarpukario_apskritys "Tarpukario_apskritys FeatureClass tarmynas_test.lkiadmtst.Tarpukario_apskritys #"
Rename C:\Users\Aidas\Dropbox\asmeninis\Tarmynas\Tarmynas_connections\GISWEB@tarmynas_test@lkiadmtst.sde\tarmynas_test.lkiadmtst.Tarpukario_apskritys C:\Users\Aidas\Dropbox\asmeninis\Tarmynas\Tarmynas_connections\GISWEB@tarmynas_test@lkiadmtst.sde\tarmynas_test.lkiadmtst.LZV_tarpukario_apskritys FeatureClass
Rename C:\Users\Aidas\Dropbox\asmeninis\Tarmynas\Tarmynas_connections\GISWEB@tarmynas_test@lkiadmtst.sde\tarmynas_test.lkiadmtst.LZV_tarpukario_apskritys C:\Users\Aidas\Dropbox\asmeninis\Tarmynas\Tarmynas_connections\GISWEB@tarmynas_test@lkiadmtst.sde\tarmynas_test.lkiadmtst.Tarpukario_apskritys FeatureClass
20220602
18591000
20220602
18591000
Microsoft Windows 10 Version 10.0 (Build 19043) ; Esri ArcGIS 12.9.3.32739
Tarpukario_apskritys
Tarpukario apskritys
Vardynas
Tarpukario apskritys
File Geodatabase Feature Class
dataset
EPSG
6.9(9.2.0)
0
Simple
FALSE
0
TRUE
FALSE
Tarpukario_apskritys
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.
OBJECTID
OBJECTID
Integer
4
0
0
geometry_L
geometry_L
Double
8
0
0
geometry_A
geometry_A
Double
8
0
0
Apskritis
Apskritis
String
50
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.
20220602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