20190911 15184500 1.0 FALSE sesleuth2020 -567291.227000 1892168.773000 51212.078000 2396552.078000 1 002 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_Albers <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[&quot;NAD_1983_Albers&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>266467840.99085236</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></ProjectedCoordinateSystem> 20220926 10395700 20220926 10395700 ISO19139 VAT_sesleuth2020 Table 16 Projected urbanization Biodiversity and Spatial Information Center OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. COUNT COUNT Integer 4 0 0 VALUE Probabilities of urbanization Biodiversity and Spatial Information Center 200 10 - 20% probability of urbanization Biodiversity and Spatial Information Center 800 70 - 80% probability of urbanization Biodiversity and Spatial Information Center 900 80 - 90% probability of urbanization Biodiversity and Spatial Information Center 975 95 - 97.5% probability of urbanization Biodiversity and Spatial Information Center 500 40 - 50% probability of urbanization Biodiversity and Spatial Information Center 25 0 - 2.5% probability of urbanization Biodiversity and Spatial Information Center 600 50 - 60% probability of urbanization Biodiversity and Spatial Information Center 1000 97.5 - 100% probability of urbanization Biodiversity and Spatial Information Center 1 Existing urbanization Biodiversity and Spatial Information Center 300 20 - 30% probability of urbanization Biodiversity and Spatial Information Center 400 30 - 40% probability of urbanization Biodiversity and Spatial Information Center 50 2.5 - 5% probability of urbanization Biodiversity and Spatial Information Center 950 90 - 95% probability of urbanization Biodiversity and Spatial Information Center 700 60 - 70% probability of urbanization Biodiversity and Spatial Information Center 100 5 - 10% probability of urbanization Biodiversity and Spatial Information Center VALUE Integer 4 0 0 311B40E3-E52E-4656-9272-B8D3DFF13078 dataset Curtis Belyea Biodiversity and Spatial Information Center, Dept. of Applied Ecology, NCSU Biologist, GIS and Database Specialist 919-513-7337 919-515-4454 127 David Clark Labs, Dept. of Applied Ecology, NCSU Raleigh NC 27695-7617 US cbelyea@ncsu.edu 20220926 ArcGIS Metadata 1.0 Curtis Belyea Biodiversity and Spatial Information Center, Dept. of Applied Ecology, NCSU Biologist, GIS and Database Specialist 919-513-7337 919-515-4454 127 David Clark Labs, Dept. of Applied Ecology, NCSU Raleigh NC 27695-7617 US cbelyea@ncsu.edu File Geodatabase Raster Dataset sesleuth2020 2016-05-23T00:00:00 Curtis Belyea Biodiversity and Spatial Information Center, Dept. of Applied Ecology, NCSU Biologist, GIS and Database Specialist 919-513-7337 919-515-4454 127 David Clark Labs, Dept. of Applied Ecology, NCSU Raleigh NC 27695-7617 US cbelyea@ncsu.edu This dataset represents the extent of urbanization (for the year indicated) predicted by the model SLEUTH, developed by Dr. Keith C. Clarke, at the University of California, Santa Barbara, Department of Geography and modified by David I. Donato of the United States Geological Survey (USGS) Eastern Geographic Science Center (EGSC). Further model modification and implementation was performed at the Biodiversity and Spatial Information Center at North Carolina State University.Urban growth probability extents throughout the 21st century were projected for the Southeast Regional Assessment Project (SERAP), which encompasses all or parts of the states of Alabama, Arkansas, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, Ohio, South Carolina, Tennessee, Virginia and West Virginia. Urban modeling for a large portion of the Appalachian, Gulf Coastal Plains & Ozarks, and Gulf Coast Prairie Landscape Conservation Cooperatives, which encompasses parts of the states of Texas, Oklahoma and Louisiana, Arkansas, Missouri and Illinois. Urban modeling for a large portion of these areas were completed earlier by the Biodiversity and Spatial Information Center as part of the Southeast Regional Assessment Project (SERAP). These data are supplementary to those projections. Urbanization in the southeastern United States puts natural ecosystems and agro-ecosystems at risk. A picture of what future urbanization might look like, based on recent trends, may be of value to urban, conservation and transportation planners alike. Biodiversity and Spatial Information Center; Dept of Applied Ecology at North Carolina State University, Raleigh, NC Curtis Belyea Biodiversity and Spatial Information Center, Dept. of Applied Ecology, NCSU Biologist, GIS and Database Specialist 919-513-7337 919-515-4454 127 David Clark Labs, Dept. of Applied Ecology, NCSU Raleigh NC 27695-7617 US cbelyea@ncsu.edu West Virginia Arkansas Georgia Pennsylvania Indiana Florida North Carolina Alabama Mississippi Kentucky South Carolina Tennessee Ohio Southeast Virginia Louisiana Oklahoma Texas United States Illinois Missouri Growth SLEUTH Urbanization Project Gigalopolis Prediction Model Development Development Kentucky Indiana Alabama South Carolina Georgia Missouri West Virginia SLEUTH Southeast Florida Growth Virginia Louisiana United States Oklahoma Project Gigalopolis Prediction Tennessee Urbanization Model North Carolina Illinois Texas Mississippi Arkansas Ohio Pennsylvania This data set is not intended for site-specific analyses. Interpretations derived from its use are suited for regional and planning purposes only. These data are not intended to be used at scales larger than 1:100,000. 60 Esri ArcGIS 13.0.0.36056 true -103.143808 -72.611193 21.815928 44.558369 true -96.607701 -88.421865 29.603247 39.266971 1 -103.143808 -72.611193 44.558369 21.815928 No restrictions. Prediction is run with the optimum set of coefficient values determined above. Output values in the Color Table were derived in order to capture probability of urbanization with a 95% confidence interval. Other important settings included: Monte Carlo Iterations = 200; Number of working grids = 6; Prediction Stop Date = 2101 Mosaicking was performed with the ArcGIS geoprocessor and the Python programming language via custom written code. For each subunit, the extent and mask environments were set to that of the study area. With these two settings, single output map algebra was used to Set null values (NoData) to 0 as such: gp.singleoutputmapalgebra_sa(“Con(Isnull(inputRaster),0,inputRaster") Cell statisctics was then performed on all subunits in the study area for each year to produce the mosaic raster for the study area for that year as such: gp.cellstatistics_sa(rasterList,studyarea_year,"MAXIMUM") Once the initial optimal Auxillary Diffusion Multiplier was arrived at, the maximum fractional values for Area, Edges and Clusters were found. This was accomplished running calibration with the initial optimal Auxillary Diffusion Multiplier and the following coefficient values: Diffusion: 100 Breed: 100 Spread: 100 Slope: 0 Road Gravity: 100 After this step was run, the resulting log file was opened in Microsoft Excel, and the highest values for the time periods between input urban data time stamps for Fractional Area, Fractional Edges and Fractional Clusters were recorded. These were used in later calibration steps to evaluate results by weighting error indicated by Fractional Area, Fractional Edges and Fractional Clusters. After each calibration step performed as described above, all log files produced were concatenated into a single file, which was opened in Microsoft Excel. Each of the Area, Edges and Clusters fractional measures for each time period between input urban data time stamps from the calibration were divided by their respective Maximum Fractional values determined in step 3 and summed. The absolute values of these summations are minimized through sorting, and the coefficient combinations with the lowest absolute weighted fractional error values are used as indicators for the ranges of coefficient values to be used in the next calibration step. It was observed that in initial rounds of calibration the diffusion coefficient among the best performing sets of coefficient combinations was often 1, indicating that lower values would provide better fitting results in calibration. As a result, the Auxillary Diffusion Multiplier was reduced until diffusion coefficient values other than 1 started appearing in the better performing combinations of coefficients. This step was repeated until optimal coefficient values were arrived at. The top performing combination of coefficients was then used in prediction. After prediction was completed, the output .gif images were converted to geo-referenced .tif files. After geo-referencing was completed, projection information was defined and the .tif files were converted to ESRI format grid datasets and reclassified to remove hillshade values used as backdrop using the ArcGIS geoprocessor and the Python programming language. Values in the raster attribute table were interpreted as follows: 1 - Extent of existing urbanization at the time of the most recent input dataset for the model SLEUTH; 3 - 0-2.5% Probability of urbanization for the projected year; 4 - 2.5-5% Probability of urbanization for the projected year; 5 - 5-10% Probability of urbanization for the projected year; 6 - 10-20% Probability of urbanization for the projected year; 7 - 20-30% Probability of urbanization for the projected year; 8 - 30-40% Probability of urbanization for the projected year; 9 - 40-50% Probability of urbanization for the projected year; 10 - 50-60% Probability of urbanization for the projected year; 11 - 60-70% Probability of urbanization for the projected year; 12 - 70-80% Probability of urbanization for the projected year; 13 - 80-90% Probability of urbanization for the projected year; 14 - 90-95% Probability of urbanization for the projected year; 15 - 95-97.5% Probability of urbanization for the projected year; 16 - 97.5-100% Probability of urbanization for the projected year. Prior to running the SLEUTH-3r model, the input ESRI format grid datasets were converted to .gif format images in order to conform to the model's requirements. Once input datasets were converted to .gif format, the first step in calibration, finding an initial optimal Auxillary Diffusion Multiplier, was performed. The scenario file for the study area was completed, with the following coefficient values: Diffusion = 100 Breed = 0 Spread = 0 Slope = 100 Road Gravity = 0 After this was run, the resulting log file was opened with Microsoft Excel, and the values for Fractional Area were examined. Ideal values for the Fractional Area were determined to be between +0.15 and 0.30 for the time periods between input urban data time stamps. If the fractional values were too low or too high the Auxillary Diffusion Multiplier was changed, according to their direct relationship, and the step was repeated. Prior to mosaicking, grids produced from outputs of the SLEUTH model were reclassified once more. Bin values were replaced with upper-limit probability values mulitplied by 10 (to avoid the need for floating point rasters and make interpretation of values more straighforward) as described below: gp.reclassify_sa(dataset, "VALUE", "3 25;4 50;5 100;6 200;7 300;8 400;9 500;10 600;11 700;12 800;13 900;14 950;15 975;16 1000", output_dataset) where the value 3 was reclassified to 25 (upper-limit 2.5%),...16 was reclassified to 1000 (upper-limit 100%). Calibration was initiated using the initial optimal Auxillary Multiplier, 25 Monte Carlo iterations and testing coefficient values as such, where no significant relief is present: Diffusion: 0 - 100; step interval 5 Breed: 0 - 100; step interval 5 Spread: 0 - 100; step interval 5 Slope: fixed at 25 Road Gravity: fixed at 100 due to the fact that it has been shown to have no significant relationship to fit statistics. Where significant relief is present: Diffusion: 0 - 100; step interval 10 Breed: 0 - 100; step interval 10 Spread: 0 - 100; step interval 10 Slope: 0 - 100; step interval 10 Road Gravity: fixed at 100 due to the fact that it has been shown to have no significant relationship to fit statistics. 2 11686 60.000000 17609 60.000000 true false -51951.227000 748292.078000 649208.773000 1804832.078000 649208.773000 748292.078000 -51951.227000 1804832.078000 298628.773000 1276562.078000 Projected urbanization gcpo_urb2020 1000 0 Unified Code of Units of Measure 16 false false false false false Pixel 39089 40991 60.000000 60.000000 16 1 Upper Left FALSE LZ77 1 pixel codes FGDBR TRUE row and column 60.000000 60.000000