20221129
16592600
1.0
FALSE
PairwiseIntersect SewerAllocationParcels;'Rehab Basins' C:\Users\glee\Documents\ArcGIS\Projects\Sewer_Allocation\Sewer_Allocation_2022.gdb\dataset\SewerAllocation_PairwiseIntersectRehabBasins "All attributes" # "Same as input"
CalculateGeometryAttributes SewerAllocation_PairwiseIntersectRehabBasins "SewerAllocationParcels_CALCACRES AREA" # "US Survey Acres" PROJCS["NAD_1983_StatePlane_Tennessee_FIPS_4100_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192]] "Same as input"
PairwiseErase SewerAllocation_PairwiseIntersectRehabBasins UGB_Wetlands C:\Users\glee\Documents\ArcGIS\Projects\Sewer_Allocation\Sewer_Allocation_2022.gdb\dataset\SewerAllocationRehabBasin_EraseWetlands #
PairwiseErase SewerAllocationRehabBasin_EraseWetlands USA_Flood_Hazard_Reduced_Set C:\Users\glee\Documents\ArcGIS\Projects\Sewer_Allocation\Sewer_Allocation_2022.gdb\dataset\SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway #
CalculateGeometryAttributes SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway "SewerAllocationParcels_CALCACRES AREA" # "US Survey Acres" PROJCS["NAD_1983_StatePlane_Tennessee_FIPS_4100_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192]] "Same as input"
CalculateGeometryAttributes SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway "SewerAllocationParcels_CALCACRES AREA" # "US Survey Acres" PROJCS["NAD_1983_StatePlane_Tennessee_FIPS_4100_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192]] "Same as input"
CalculateGeometryAttributes SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway "SewerAllocationParcels_CALCACRES AREA" # "US Survey Acres" PROJCS["NAD_1983_StatePlane_Tennessee_FIPS_4100_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192]] "Same as input"
CalculateGeometryAttributes SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway "SewerAllocationParcels_CALCACRES AREA" # "US Survey Acres" PROJCS["NAD_1983_StatePlane_Tennessee_FIPS_4100_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192]] "Same as input"
CopyFeatures SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway "C:\Users\glee\Documents\ArcGIS\Projects\Sewer_Allocation\Planning on gis-sql1.sde\Planning.DBO.SewerAllocation\Planning.DBO.SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway" # # # #
Planning.DBO.SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway
002
Server=gis-sql1; Service=sde:sqlserver:gis-sql1; Database=Planning; Version=dbo.DEFAULT
ArcSDE Connection
Projected
GCS_NAD_1983_2011
Linear Unit: Foot_US (0.304801)
NAD_1983_2011_StatePlane_Tennessee_FIPS_4100_Ft_US
<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["NAD_1983_2011_StatePlane_Tennessee_FIPS_4100_Ft_US",GEOGCS["GCS_NAD_1983_2011",DATUM["D_NAD_1983_2011",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-86.0],PARAMETER["Standard_Parallel_1",35.25],PARAMETER["Standard_Parallel_2",36.41666666666666],PARAMETER["Latitude_Of_Origin",34.33333333333334],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",6576]]</WKT><XOrigin>-121521300</XOrigin><YOrigin>-93934000</YOrigin><XYScale>3048.0060960121928</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>103152</WKID><LatestWKID>6576</LatestWKID></ProjectedCoordinateSystem>
20221214
15133000
20221214
15133000
Microsoft Windows 10 Version 10.0 (Build 19044) ; Esri ArcGIS 13.0.3.36057
Unimproved Parcels By Basin
Unimproved Parcels with the Wetlands & Regulatory Floodway polygon areas removed. Includes Basin # & Calculated Acreages
Sewer Allocation
Sewer Allocation Unimproved Parcel Acreages with Wetlands & Regulatory Floodway Areas removed
Enterprise Geodatabase Feature Class
dataset
EPSG
8.2.10(10.3.1)
0
Simple
FALSE
0
TRUE
TRUE
Planning.DBO.SewerAllocationRehabBasin_EraseWetlands_RegulatoryFloodway
Feature Class
0
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
SewerAllocationParcels_CALCACRE
Calculated Acres
Double
8
38
8
RuthLIS_DBO_GisCamaCurrentYear_
ImprovedStatus
String
63
0
0
RuthLIS_DBO_GisCamaCurrentYear1
AccountType
String
63
0
0
SHAPE
SHAPE
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
BASIN_NUMBER
BASIN #
String
10
0
0
SHAPE.STArea()
SHAPE.STArea()
Double
0
0
0
SHAPE.STLength()
SHAPE.STLength()
Double
0
0
0
20221214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