20230328
10034200
1.0
FALSE
UpdateSchema "CIMDATA=<CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' 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/3.0.0'><WorkspaceConnectionString>DATABASE=C:\Users\Fernando Rodriguez\Desktop\caminos_pp\OneDrive_2023-03-27\BASE DE DATOS PRIORIZACIÓN - copia\CAPAS VS\Secundaria priorizada\Secundaria priorizada</WorkspaceConnectionString><WorkspaceFactory>Shapefile</WorkspaceFactory><Dataset>Priorización Departamental.shp</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMStandardDataConnection>" <operationSequence><workflow><AddField><field_name>Cult_Coca</field_name><field_type>TEXT</field_type><field_length>2</field_length><field_is_nullable>False</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence>
CalculateField "Priorización Departamental" Cult_Coca "SI" "Python 3" # Text NO_ENFORCE_DOMAINS
CalculateField "Priorización Departamental" Cult_Coca "NO" "Python 3" # Text NO_ENFORCE_DOMAINS
UpdateSchema "CIMDATA=<CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' 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/3.0.0'><WorkspaceConnectionString>DATABASE=C:\Users\Fernando Rodriguez\Desktop\caminos_pp\OneDrive_2023-03-27\BASE DE DATOS PRIORIZACIÓN - copia\CAPAS VS\Secundaria priorizada\Secundaria priorizada</WorkspaceConnectionString><WorkspaceFactory>Shapefile</WorkspaceFactory><Dataset>Priorización Departamental.shp</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMStandardDataConnection>" <operationSequence><workflow><AddField><field_name>Erradica</field_name><field_type>TEXT</field_type><field_length>2</field_length><field_is_nullable>False</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence>
CalculateField "Priorización Departamental" Erradica "SI" "Python 3" # Text NO_ENFORCE_DOMAINS
CalculateField "Priorización Departamental" Erradica "NO" "Python 3" # Text NO_ENFORCE_DOMAINS
ExportFeatures "info base\Priorización Vias Secundarias" "C:\Users\Fernando Rodriguez\Documents\ArcGIS\Projects\postulado\Publicar\postulado.gdb\Priorización_Vias_Secundarias" # NOT_USE_ALIAS "fid_1 "fid_1" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,fid_1,-1,-1;CODIGOVIA "CODIGOVIA" true true false 30 Text 0 0,First,#,info base\Priorización Vias Secundarias,CODIGOVIA,0,30;CODIGOENT "CODIGOENT" true true false 50 Text 0 0,First,#,info base\Priorización Vias Secundarias,CODIGOENT,0,50;CODIGOEJ "CODIGOEJ" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,CODIGOEJ,-1,-1;FECHA "FECHA" true true false 10 Text 0 0,First,#,info base\Priorización Vias Secundarias,FECHA,0,10;NOMBRE "NOMBRE" true true false 100 Text 0 0,First,#,info base\Priorización Vias Secundarias,NOMBRE,0,100;SECTOR "SECTOR" true true false 100 Text 0 0,First,#,info base\Priorización Vias Secundarias,SECTOR,0,100;CORREDOR "CORREDOR" true true false 100 Text 0 0,First,#,info base\Priorización Vias Secundarias,CORREDOR,0,100;CATEG "CATEG" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,CATEG,-1,-1;PRINI "PRINI" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,PRINI,-1,-1;DISTPRINI "DISTPRINI" true true false 24 Double 15 23,First,#,info base\Priorización Vias Secundarias,DISTPRINI,-1,-1;PRFIN "PRFIN" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,PRFIN,-1,-1;DISTPRFIN "DISTPRFIN" true true false 24 Double 15 23,First,#,info base\Priorización Vias Secundarias,DISTPRFIN,-1,-1;LONGITUD "LONGITUD" true true false 24 Double 15 23,First,#,info base\Priorización Vias Secundarias,LONGITUD,-1,-1;OBSERVACIO "OBSERVACIO" true true false 250 Text 0 0,First,#,info base\Priorización Vias Secundarias,OBSERVACIO,0,250;DEPARTAM "DEPARTAM" true true false 20 Double 0 20,First,#,info base\Priorización Vias Secundarias,DEPARTAM,-1,-1;ID "ID" true true false 20 Text 0 0,First,#,info base\Priorización Vias Secundarias,ID,0,20;fid_2 "fid_2" true true false 24 Double 15 23,First,#,info base\Priorización Vias Secundarias,fid_2,-1,-1;LONG "LONG" true true false 24 Double 15 23,First,#,info base\Priorización Vias Secundarias,LONG,-1,-1;Cult_Coca "Cult_Coca" true true false 2 Text 0 0,First,#,info base\Priorización Vias Secundarias,Cult_Coca,0,2;Erradica "Erradica" true true false 2 Text 0 0,First,#,info base\Priorización Vias Secundarias,Erradica,0,2" #
CopyFeatures C:\Users\soportesig\Desktop\20230417_Publicar\postulado.gdb\Priorización_Vias_Secundarias C:\Users\soportesig\Documents\ArcGIS\Projects\MyProject1\gis001.sde\gis001.sde.Caminos_Paz\gis001.sde.Priorización_Vias_Secundarias # # # #
gis001.sde.Priorización_Vias_Secundarias
002
Server=localhost; Service=sde:postgresql:localhost; Database=gis001; User=sde; Version=sde.DEFAULT
ArcSDE Connection
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
<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/3.0.0'><WKT>PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</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>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem>
WGS_1984_Web_Mercator_Auxiliary_Sphere
20230417
11312400
20230417
11312400
Microsoft Windows Server 2016 Technical Preview Version 10.0 (Build 17763) ; Esri ArcGIS 13.0.3.36057
Priorización Vias Secundarias
Priorización
Vías
Vías Secundarias
Enterprise Geodatabase Feature Class
dataset
EPSG
6.18.3(9.3.1.2)
0
Simple
FALSE
0
TRUE
TRUE
gis001.sde.Priorización_Vias_Secundarias
Feature Class
0
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
fid_1
fid_1
Double
8
38
8
CODIGOVIA
CODIGOVIA
String
30
0
0
CODIGOENT
CODIGOENT
String
50
0
0
CODIGOEJ
CODIGOEJ
Double
8
38
8
FECHA
FECHA
String
10
0
0
NOMBRE
NOMBRE
String
100
0
0
SECTOR
SECTOR
String
100
0
0
CORREDOR
CORREDOR
String
100
0
0
CATEG
CATEG
Double
8
38
8
PRINI
PRINI
Double
8
38
8
DISTPRINI
DISTPRINI
Double
8
38
8
PRFIN
PRFIN
Double
8
38
8
DISTPRFIN
DISTPRFIN
Double
8
38
8
LONGITUD
LONGITUD
Double
8
38
8
OBSERVACIO
OBSERVACIO
String
250
0
0
DEPARTAM
DEPARTAM
Double
8
38
8
ID
ID
String
20
0
0
fid_2
fid_2
Double
8
38
8
LONG
LONG
Double
8
38
8
Cult_Coca
Cult_Coca
String
2
0
0
Erradica
Erradica
String
2
0
0
st_length(shape)
st_length(shape)
Double
0
0
0
20230417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