Site Suitability Analysis

Suitability analysis of favorable areas for bear habitats in the Great Smoky Mountain National Park

Problem and Objective

The Great Smoky Mountain National Park is a protected area in the United States with the presence of black bears. Biologists in this park are concerned about the physical interaction between these bears and the visitors, especially around the roads and trails which could create a frightening experience for the visitors. The purpose of this analysis is to identify the suitable areas in the Great Smoky Mountain National Park that can provide bears with the basic needs that would reduce their interaction with park visitors.

Analysis Procedure

Strategies: The strategies adopted for this analysis are explained as follows. I used ArcGIS Pro (Version 2.9) software. I used the following Modules/tools for the analysis: Slope tool, Distance Accumulation tool, Feature to Raster tool, Reclassify tool, and Weighted Overlay tool. Data and data types – The data include (a) vector shapefiles for roads, streams, trails, and vegetation class and (b) a raster GRID file for elevation with 30- meter resolution. Data sources – GIS 520 Fall Semester, NCSU.

Methods: I added all five layers to the map. I set the processing extent for all the analyses to that of the vegetation layer in the geoprocessing group (environment) under the analysis tab. I also set the cell size for all procedures to the 30-meter resolution elevation layer. I created a new toolbox and created a new model in the toolbox. I added the vegetation layer to the model builder and transformed it from vector to raster data by attaching the feature-to-raster tool to it. I added the elevation data to the model and used the slope tool to compute the slope of the elevation. I then added the streams line data to the model and used the distance accumulation tool to calculate the accumulated distance for each cell to sources. I also attached the distance accumulation tool to the added road and trail shapefiles separately in the model builder to calculate the accumulated distance from each cell to input sources. I dragged the reclassify tool to the model builder window and attached one each to the five rasters from the previous analysis. I used it to reclassify the slope raster using the value field. I reclassified the slope into three categories where 3, 2, and 1 represent 00-300, 300-600, and 60 degrees and above respectively. I reclassified the accumulated stream distance using the reclassified tool where 3 represents 0-804.672 meter distance from the stream, 2 represents 804.672-1609.344 meters distance from the streams and 3 represents 1609.344 meters and above distance from the streams. I reclassified the accumulated trail distance using the reclassified tool where 1 represents the value for the distance from 0-804.672 meters from the trail, 2 represents the distance between 804.672-1609.344 meters from the trails, and 3 for any distance from 1609.344 meters and above. I also repeated the same reclassification process for roads using the reclassify tool. For all raster reclassifications, I left nodata as nodata. I added the Weighted Overlay tool to the model. I assigned equal weight to each of the five (5) layers (i.e 20% for each layer). I also selected a scale of 1-3 for the weighted overlay tool. The scale for each of the 5 layers was verified and updated as needed where 1 equals 1, 2 equals 2, 3 equals 3 and no data equals nodata.

External links: Weighted Overlay tool, Reclassify tool, Distance Accumulation tool

Workflow diagram of the analysis from the model builder

Result

The map above represents the least favorable, favorable, and most favorable regions for the habitation of bears in the Great Smoky Mountains National Park which will help reduce their interactions with visitors.

Application & Reflection

Problem description: The major objective of a business owner is to maximize profit and satisfy his or her customers. The purpose of this analysis is to identify suitable areas for the location of new grocery stores in New Hanover County, NC State. The aim is to locate one each in an urban center based on the following criteria: (a) access to high population – (50,000 - 70,000) (b) road (c) no grocery store around 500 meters distance from the location (c) available space of about 30 acres of land.

Data needed: The data needed include shapefiles of block group population census boundary for the New Hanover County with (a) Median household income and (b) Total population field. Also road and trail shapefiles as well as shapefile of point features of existing grocery stores in the study area. Also, a landcover raster data

Analysis procedures: I will set the processing extent for all the tools that would be used for the analysis to that of the block group shapefile census boundary for the New Hanover County layer. I will also set the cell size for all procedures to the landcover raster layer. I will add the trail and road data to the model builder. I will use the distance accumulation tool to compute the distance from the road, trail and grocery store points to anywhere on the surface. I will use the reclassify tool to reclassify the accumulated trail and road distance such as 1 is for the distance between 0-1609.344 and 2 is for any distance above 1609.344. Also, I will use the reclassify tool to reclassify the accumulated grocery store distance where 1 is assigned to 0-500 meters distance and 2 to any distance above 500 meters. I will add the block group boundary layer to the model builder and transform it from vector to raster data using the feature to raster tool using the median income field. Then, I will transform another block group raster data using the population field. I will use the reclassify tool to transform the median income raster by assigning 1 for income between $25,000 and above and 2 for any income below $25,000. Also, I will reclassify the population raster by assigning 1 to any population blockgroup above 2000 people, and 2 will be used to represent any population below 2000 people. I will reclassify the landcover raster data by assigning 1 to barren and herbaceous lands while two (2) will be assigned to every other landcover class. I will add the Weighted Overlay tool to the model. I will assign equal weight to all six layers. I will also select a scale of 1-2 for the weighted overlay tool. After running the model, class One (1) from the output raster will therefore represent suitable areas for the location of the grocery store in the study area.

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