5000 150000000 2018-03-15 14192700 2021-07-14 16:27:34.02 editor:esri.dijit.metadata.editor 1.0 Item Description ItemDescription false FALSE 2019-10-09 10413800 002 Veredas 827678.657800 832739.086000 1167997.776700 1172509.931300 1 Projected GCS_MAGNA MAGNA_Colombia_Bogota 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/10.6'><WKT>PROJCS[&quot;MAGNA_Colombia_Bogota&quot;,GEOGCS[&quot;GCS_MAGNA&quot;,DATUM[&quot;D_MAGNA&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,1000000.0],PARAMETER[&quot;False_Northing&quot;,1000000.0],PARAMETER[&quot;Central_Meridian&quot;,-74.07750791666666],PARAMETER[&quot;Scale_Factor&quot;,1.0],PARAMETER[&quot;Latitude_Of_Origin&quot;,4.596200416666666],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3116]]</WKT><XOrigin>-4623200</XOrigin><YOrigin>-9510300</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>3116</WKID><LatestWKID>3116</LatestWKID></ProjectedCoordinateSystem> dataset 1626297927522r5435968034546603 2019-10-09 16:25:26.98 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Simple FALSE 6 TRUE FALSE Veredas de Sabaneta <div style='text-align:Left;'><div><div><p><span>Veredas de</span><span>l Municipio de</span><span>Sabaneta correspondiente al PBOT de Sabaneta, Acuerdo 22 de 2009.</span></p><p><span>Vereda: Unidad geográfica rural conformada por un número determinado de predios pertenecientes a una o varias parcelaciones, delimitado por accidentes geográficos naturales o vías.</span></p><p><span>Fuente: Anexo 5 del PBOT Acuerdo 22 de 2009.</span></p><p><span>F</span><span>echa: Año 2009</span></p><p><span /></p></div></div></div> Veredas del Municipio de Sabaneta correspondiente al PBOT de Sabaneta, Acuerdo 22 de 2009. Alcaldía Sabaneta Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321 VeredassectorizaciónPBOTSabaneta 1 -75.634221 -75.588402 6.113133 6.154051 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Informacion que puede ser utilizada por las entidades u organizaciones que autorice el Municip</SPAN><SPAN>i</SPAN><SPAN>o de Sabaneta.</SPAN></P></DIV></DIV></DIV> Otras restricciones Otras restricciones Otras restricciones Otras restricciones File Geodatabase Feature Class Veredas Feature Class 6 OBJECTID_1 OBJECTID_1 Internal feature number. Esri OID 4 0 0 Sequential unique whole numbers that are automatically generated. Shape Shape Feature geometry. Esri Geometry 0 0 0 Coordinates defining the features. NOMBRE NOMBRE String 70 0 0 PK_VEREDA PK_VEREDA String 14 0 0 SHAPE_Leng SHAPE_Leng Double 8 0 0 Shape_Length Shape_Length Length of feature in internal units. Esri Double 8 0 0 Positive real numbers that are automatically generated. Shape_Area Shape_Area Area of feature in internal units squared. Esri Double 8 0 0 Positive real numbers that are automatically generated. Area_Km Area_Km Double 8 0 0