20210423
18352200
1.0
FALSE
FeatureClassToFeatureClass sdg_db.gis_data2.SAV E:\SDG\SDG\SDG.gdb SAV_1_2_1a # "nuts1 "NUTS1" true true false 5 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.nuts1,0,5;nuts2 "NUTS2" true true false 5 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.nuts2,0,5;nuts3 "NUTS3" true true false 5 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.nuts3,0,5;lau1 "LAU1" true true false 5 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.lau1,0,5;lau_name "LAU_NAME" true true false 50 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.lau_name,0,50;lau_name_en "LAU_NAME_EN" true true false 50 Text 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.lau_name_en,0,50;st_area_shape_ "st_area(shape)" false true true 0 Double 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.st_area(shape),-1,-1;st_length_shape_ "st_length(shape)" false true true 0 Double 0 0,First,#,sdg_db.gis_data2.SAV,sdg_db.gis_data2.SAV.st_length(shape),-1,-1;kodas "kodas" true true false 255 Text 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.kodas,0,255;pav "pav" true true false 255 Text 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.pav,0,255;F2010 "2010" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2010,-1,-1;F2011 "2011" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2011,-1,-1;F2012 "2012" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2012,-1,-1;F2013 "2013" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2013,-1,-1;F2014 "2014" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2014,-1,-1;F2015 "2015" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2015,-1,-1;F2016 "2016" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2016,-1,-1;F2017 "2017" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2017,-1,-1;F2018 "2018" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2018,-1,-1;F2019 "2019" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2019,-1,-1;F2020 "2020" true true false 8 Double 0 0,First,#,sdg_db.gis_data2.SAV,GIS$.F2020,-1,-1;ObjectID "ObjectID" false true false 4 Long 0 9,First,#,sdg_db.gis_data2.SAV,GIS$.ObjectID,-1,-1" #
CopyMultiple "E:\SDG\SDG\SDG.gdb\SAV_1_2_1a FeatureClass" E:\SDG\SDG\Goal1\goal1.gdb SAV_1_2_1a "SAV_1_2_1a FeatureClass SAV_1_2_1a #"
FeatureClassToFeatureClass E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a C:\Users\JanaV\Documents\ArcGIS\Projects\MyProject2\SDG--HUB-DB.STAT.GOV.LT.sde\sdg_hub.gis_data2.GOAL1 SAV_1_2_1a # "nuts1 "NUTS1" true true false 5 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,nuts1,0,5;nuts2 "NUTS2" true true false 5 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,nuts2,0,5;nuts3 "NUTS3" true true false 5 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,nuts3,0,5;lau1 "LAU1" true true false 5 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,lau1,0,5;lau_name "LAU_NAME" true true false 50 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,lau_name,0,50;lau_name_en "LAU_NAME_EN" true true false 50 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,lau_name_en,0,50;kodas "kodas" true true false 255 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,kodas,0,255;pav "pav" true true false 255 Text 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,pav,0,255;F2010 "2010" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2010,-1,-1;F2011 "2011" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2011,-1,-1;F2012 "2012" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2012,-1,-1;F2013 "2013" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2013,-1,-1;F2014 "2014" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2014,-1,-1;F2015 "2015" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2015,-1,-1;F2016 "2016" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2016,-1,-1;F2017 "2017" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2017,-1,-1;F2018 "2018" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2018,-1,-1;F2019 "2019" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2019,-1,-1;F2020 "2020" true true false 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,F2020,-1,-1;ObjectID "ObjectID" true true false 4 Long 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,ObjectID,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,E:\SDG\SDG\Goal1\goal1.gdb\SAV_1_2_1a,Shape_Area,-1,-1" #
DeleteField sdg_hub.gis_data2.SAV_1_2_1a kodas
DeleteField sdg_hub.gis_data2.SAV_1_2_1a pav
DeleteField sdg_hub.gis_data2.SAV_1_2_1a objectid
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' 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/2.6.0'><FeatureDataset>sdg_hub.gis_data2.GOAL1</FeatureDataset><WorkspaceConnectionString>ENCRYPTED_PASSWORD=00022e686b6573454d46447a70554e32487046365865773063783474796937354e52734c672f2f414434773046766b3d2a00;SERVER=SDG-DB.STAT.GOV.LT;INSTANCE=sde:postgresql:SDG-DB.STAT.GOV.LT;DBCLIENT=postgresql;DB_CONNECTION_PROPERTIES=SDG-DB.STAT.GOV.LT;DATABASE=sdg_hub;USER=gis_data2;VERSION=sde.DEFAULT;AUTHENTICATION_MODE=DBMS</WorkspaceConnectionString><WorkspaceFactory>SDE</WorkspaceFactory><Dataset>sdg_hub.gis_data2.SAV_1_2_1a</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AlterField><field_name>f2010</field_name><new_field_name>Y2010</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2011</field_name><new_field_name>Y2011</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2012</field_name><new_field_name>Y2012</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2013</field_name><new_field_name>Y2013</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2014</field_name><new_field_name>Y2014</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2015</field_name><new_field_name>Y2015</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2016</field_name><new_field_name>Y2016</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2017</field_name><new_field_name>Y2017</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2018</field_name><new_field_name>Y2018</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2019</field_name><new_field_name>Y2019</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>f2020</field_name><new_field_name>Y2020</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AddField><field_name>Y2021</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2021</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2022</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2022</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2023</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2023</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2024</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2024</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2025</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2025</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2026</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2026</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2027</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2027</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2028</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2028</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2029</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2029</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Y2030</field_name><field_type>DOUBLE</field_type><field_precision>7</field_precision><field_scale>1</field_scale><field_alias>2030</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence>
FeatureClassToFeatureClass sdg_hub.gis_data2.SAV_1_2_1a C:\Users\JanaV\Documents\ArcGIS\Projects\MyProject2\SDG--HUB-DB.STAT.GOV.LT.sde\sdg_hub.gis_data2.GOAL2 SAV_2_4_1a # "nuts1 "NUTS1" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,nuts1,0,5;nuts2 "NUTS2" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,nuts2,0,5;nuts3 "NUTS3" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,nuts3,0,5;lau1 "LAU1" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,lau1,0,5;lau_name "LAU_NAME" true true false 50 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,lau_name,0,50;lau_name_en "LAU_NAME_EN" true true false 50 Text 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,lau_name_en,0,50;y2010 "2010" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2010,-1,-1;y2011 "2011" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2011,-1,-1;y2012 "2012" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2012,-1,-1;y2013 "2013" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2013,-1,-1;y2014 "2014" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2014,-1,-1;y2015 "2015" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2015,-1,-1;y2016 "2016" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2016,-1,-1;y2017 "2017" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2017,-1,-1;y2018 "2018" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2018,-1,-1;y2019 "2019" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2019,-1,-1;y2020 "2020" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2020,-1,-1;y2021 "2021" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2021,-1,-1;y2022 "2022" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2022,-1,-1;y2023 "2023" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2023,-1,-1;y2024 "2024" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2024,-1,-1;y2025 "2025" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2025,-1,-1;y2026 "2026" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2026,-1,-1;y2027 "2027" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2027,-1,-1;y2028 "2028" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2028,-1,-1;y2029 "2029" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2029,-1,-1;y2030 "2030" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_1_2_1a,y2030,-1,-1;st_area_shape_ "st_area(shape)" false true true 0 Double 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,st_area(shape),-1,-1;st_length_shape_ "st_length(shape)" false true true 0 Double 0 0,First,#,sdg_hub.gis_data2.SAV_1_2_1a,st_length(shape),-1,-1" #
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' 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/2.6.0'><FeatureDataset>sdg_hub.gis_data2.GOAL2</FeatureDataset><WorkspaceConnectionString>ENCRYPTED_PASSWORD=00022e684d483177473033776552744b4e50556370695154373862393358486148576a5473337363423145396d4d6f3d2a00;SERVER=SDG-DB.STAT.GOV.LT;INSTANCE=sde:postgresql:SDG-DB.STAT.GOV.LT;DBCLIENT=postgresql;DB_CONNECTION_PROPERTIES=SDG-DB.STAT.GOV.LT;DATABASE=sdg_hub;USER=gis_data2;VERSION=sde.DEFAULT;AUTHENTICATION_MODE=DBMS</WorkspaceConnectionString><WorkspaceFactory>SDE</WorkspaceFactory><Dataset>sdg_hub.gis_data2.SAV_2_4_1a</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>UNITS</field_name><field_type>TEXT</field_type><field_precision>0</field_precision><field_length>15</field_length><field_alias>UNITS</field_alias><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence>
CalculateField sdg_hub.gis_data2.SAV_2_4_1a UNITS 'thous. ha' "Python 3" # Text
FeatureClassToFeatureClass sdg_hub.gis_data2.SAV_2_4_1a C:\Users\JanaV\Documents\ArcGIS\Projects\MyProject2\SDG--HUB-DB.STAT.GOV.LT.sde\sdg_hub.gis_data2.GOAL3 SAV_3_7_2b # "nuts1 "NUTS1" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,nuts1,0,5;nuts2 "NUTS2" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,nuts2,0,5;nuts3 "NUTS3" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,nuts3,0,5;lau1 "LAU1" true true false 5 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,lau1,0,5;lau_name "LAU_NAME" true true false 50 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,lau_name,0,50;lau_name_en "LAU_NAME_EN" true true false 50 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,lau_name_en,0,50;y2010 "2010" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2010,-1,-1;y2011 "2011" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2011,-1,-1;y2012 "2012" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2012,-1,-1;y2013 "2013" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2013,-1,-1;y2014 "2014" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2014,-1,-1;y2015 "2015" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2015,-1,-1;y2016 "2016" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2016,-1,-1;y2017 "2017" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2017,-1,-1;y2018 "2018" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2018,-1,-1;y2019 "2019" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2019,-1,-1;y2020 "2020" true true false 8 Double 8 38,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2020,-1,-1;y2021 "2021" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2021,-1,-1;y2022 "2022" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2022,-1,-1;y2023 "2023" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2023,-1,-1;y2024 "2024" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2024,-1,-1;y2025 "2025" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2025,-1,-1;y2026 "2026" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2026,-1,-1;y2027 "2027" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2027,-1,-1;y2028 "2028" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2028,-1,-1;y2029 "2029" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2029,-1,-1;y2030 "2030" true true false 8 Double 1 7,First,#,sdg_hub.gis_data2.SAV_2_4_1a,y2030,-1,-1;units "UNITS" true true false 15 Text 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,units,0,15;st_area_shape_ "st_area(shape)" false true true 0 Double 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,st_area(shape),-1,-1;st_length_shape_ "st_length(shape)" false true true 0 Double 0 0,First,#,sdg_hub.gis_data2.SAV_2_4_1a,st_length(shape),-1,-1" #
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' 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/2.6.0'><FeatureDataset>sdg_hub.gis_data2.GOAL3</FeatureDataset><WorkspaceConnectionString>ENCRYPTED_PASSWORD=00022e686c716d507956396a58665849326b38767a392f5936556b733841627633676a4351716859634948796552413d2a00;SERVER=SDG-DB.STAT.GOV.LT;INSTANCE=sde:postgresql:SDG-DB.STAT.GOV.LT;DBCLIENT=postgresql;DB_CONNECTION_PROPERTIES=SDG-DB.STAT.GOV.LT;DATABASE=sdg_hub;USER=gis_data2;VERSION=sde.DEFAULT;AUTHENTICATION_MODE=DBMS</WorkspaceConnectionString><WorkspaceFactory>SDE</WorkspaceFactory><Dataset>sdg_hub.gis_data2.SAV_3_7_2b</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AlterField><field_name>units</field_name><field_type>TEXT</field_type><field_length>100</field_length><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>
CalculateField sdg_hub.gis_data2.SAV_3_7_2b units 'Live births per 1 thousand women at the age specified' "Python 3" # Text
Rename D:\Documents\ArcGIS\Projects\SDG\sdg_hub.sde\sdg_hub.gis_data2.GOAL3\sdg_hub.gis_data2.SAV_3_7_2b D:\Documents\ArcGIS\Projects\SDG\sdg_hub.sde\sdg_hub.gis_data2.GOAL3\sdg_hub.gis_data2.SAV_3_7_2b_ FeatureClass
Rename D:\Documents\ArcGIS\Projects\SDG\sdg_hub.sde\sdg_hub.gis_data2.GOAL3\sdg_hub.gis_data2.SAV_3_7_2b_ D:\Documents\ArcGIS\Projects\SDG\sdg_hub.sde\sdg_hub.gis_data2.GOAL3\sdg_hub.gis_data2.sav_3_7_2b FeatureClass
Server=SDG-DB.STAT.GOV.LT; Service=sde:postgresql:SDG-DB.STAT.GOV.LT; Database=sdg_hub; User=gis_data2; Version=sde.DEFAULT
ArcSDE Connection
sdg_hub.gis_data2.SAV_3_7_2b
002
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/2.6.0'><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>
20210512
13494900
20210512
13494900
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 12.6.0.24783
sav_3_7_2b
Enterprise Geodatabase Feature Class
dataset
EPSG
6.9(9.2.0)
0
Simple
FALSE
0
TRUE
FALSE
sdg_hub.gis_data2.SAV_3_7_2b
Feature Class
0
nuts1
NUTS1
String
5
0
0
nuts2
NUTS2
String
5
0
0
nuts3
NUTS3
String
5
0
0
lau1
LAU1
String
5
0
0
lau_name
LAU_NAME
String
50
0
0
lau_name_en
LAU_NAME_EN
String
50
0
0
objectid
objectid
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
y2010
2010
Double
8
38
8
y2011
2011
Double
8
38
8
y2012
2012
Double
8
38
8
y2013
2013
Double
8
38
8
y2014
2014
Double
8
38
8
y2015
2015
Double
8
38
8
y2016
2016
Double
8
38
8
y2017
2017
Double
8
38
8
y2018
2018
Double
8
38
8
y2019
2019
Double
8
38
8
y2020
2020
Double
8
38
8
y2021
2021
Double
8
7
1
y2022
2022
Double
8
7
1
y2023
2023
Double
8
7
1
y2024
2024
Double
8
7
1
y2025
2025
Double
8
7
1
y2026
2026
Double
8
7
1
y2027
2027
Double
8
7
1
y2028
2028
Double
8
7
1
y2029
2029
Double
8
7
1
y2030
2030
Double
8
7
1
units
UNITS
String
15
0
0
shape
shape
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
st_area(shape)
st_area(shape)
Double
0
0
0
st_length(shape)
st_length(shape)
Double
0
0
0
20210512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