Fearless Bears and People

Analysis Procedures

Ideally, the best sites for bear relocation are more than one mile away from roads and trails, under a half mile to water, on relatively flat terrain with certain types of vegetation. Using datasets of these five requirements provided by park biologists, I used ArcMap to examine polygon data of vegetation types, line data of streams, trails and roads and a raster image of elevation data. Using Model Builder, I created a model to convert these data sets to raster data using the Feature to Raster tool, Calculate Euclidean Distance tool and the Calculate Slope tool respectively. The Reclassify tool enabled me to classify values into three categories of favorability. These datasets, on equal scales of 1 to 3, were equally weighed in the Weighted Overlay tool producing an image showing the least, moderate and most favorable habitats.

I started by adding the 5 data layers to Model builder and the tools to complete the analysis. In all tools, where available, selections were made to change missing values to “NoData” and cell size output was set to 30 meters (m) to correspond to the cell size of the elevation raster.

I used the Streams shapefile as the input data to the Calculate Euclidean Distance Tool. The resulting greyscale raster “Distance to Streams” had values up to 6,330m. This image was used as the input to the Reclassify Tool where three new classifications were created. The most favorable condition, close proximity to a stream, was given a new value of 3 and assigned values of 0 to 804.67m (0.5 mile). The new value of 2 was set to values from 804.67m to 1,609.34m (1 mile) and new value 1 was all pixels with a value over 1,609.34m.

Distances to roads and trails were set up in a similar manner with the main difference being in the classification, where greater distance locations were more desirable. For each layer, the Calculate Euclidean Distance tool was used, and raster images generated. Each of these served as the input into their own Reclassify tool. For both layers, new classes 1 to 3 were created for areas up to 0.5 miles, 0.5 to 1 mile and greater than one mile respectively.

Application and Reflection

Problem description:

In another project, I’m currently looking at real estate and identifying properties based on various traits for redevelopment.

Data needed:

Using housing data on renovations from the US Census Bureau, elevation data from the USGS, Wake County greenway & parks data and Sentinel2 imagery. Another source of data to be investigated are property tax reassessment changes to find homes that have significantly changed in value indicating a highly renovated property.

Analysis procedures:

I can then identify properties that are on relatively level lots by using the calculate slope function. I would then use the sentinel 2 imagery infrared bands to classify the image into 5 groupings based on the percentage of the parcel covered by trees. Using the calculate distance tool would allow me to find properties near parks/greenway access points in .2-mile increments. The census data could be joined with parcel data to find homes that have not undergone significant renovation based upon their property tax value changes. I would reclassify these layers into a 5-point scale system to be combined in in a weighted overlay. The categories with heavier weight would be properties with minimal to no renovation and larger lot sizes while tree coverage and proximity to parks/greenways would have a lower weighting.