20240317
20024200
1.0
FALSE
CreateFeatureclass "D:\Education\MSc Geography & Applied Geoinformatics\Thesis\Thesis Data\Thesis\Thesis.gdb" c2001_evros_prefire_Tmean_XYTableToPoint Point 2001_evros_prefire_Tmean.txt No Yes "GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],VERTCS["WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PARAMETER["Vertical_Shift",0.0],PARAMETER["Direction",1.0],UNIT["Meter",1.0]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision" # 0 0 0 #
XYTableToPoint 2001_evros_prefire_Tmean.txt "D:\Education\MSc Geography & Applied Geoinformatics\Thesis\Thesis Data\Thesis\Thesis.gdb\c2001_evros_prefire_Tmean_XYTableToPoint" lon lat value "GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],VERTCS["WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PARAMETER["Vertical_Shift",0.0],PARAMETER["Direction",1.0],UNIT["Meter",1.0]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision"
ExportFeatures c2001_evros_prefire_Tmean_XYTableToPoint "D:\Education\MSc Geography & Applied Geoinformatics\Thesis\Thesis Data\Shapefiles\2001_Evros_Prefire.shp" # NOT_USE_ALIAS "date "date" true true false 8 Date 0 0,First,#,c2001_evros_prefire_Tmean_XYTableToPoint,date,-1,-1;lon "lon" true true false 8 Double 0 0,First,#,c2001_evros_prefire_Tmean_XYTableToPoint,lon,-1,-1;lat "lat" true true false 8 Double 0 0,First,#,c2001_evros_prefire_Tmean_XYTableToPoint,lat,-1,-1;value "value" true true false 8 Double 0 0,First,#,c2001_evros_prefire_Tmean_XYTableToPoint,value,-1,-1" #
2001_Evros_Prefire
002
0.000
file://\\DJOWINDOWSMAC\D$\Education\MSc Geography & Applied Geoinformatics\Thesis\Thesis Data\Shapefiles\2001_Evros_Prefire.shp
Local Area Network
Geographic
GCS_WGS_1984
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/3.0.0'><WKT>GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4326]],VERTCS["WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PARAMETER["Vertical_Shift",0.0],PARAMETER["Direction",1.0],UNIT["Meter",1.0],AUTHORITY["Esri",115700]]</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>4326</WKID><LatestWKID>4326</LatestWKID><VCSWKID>115700</VCSWKID><LatestVCSWKID>115700</LatestVCSWKID></GeographicCoordinateSystem>
20240317
20032200
20240317
20032200
Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 13.0.3.36057
FWI Evros Box
Shapefile
0.000
dataset
EPSG
6.2(3.0.1)
0
Simple
FALSE
0
FALSE
TRUE
2001_Evros_Prefire
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.
date
date
Date
8
0
0
lon
lon
Double
19
0
0
lat
lat
Double
19
0
0
value
value
Double
19
0
0
20240317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