Embedded Files
Problem
A San Diego-based entrepreneur seeks assistance in identifying suitable locations for viticulture in the surrounding area. This project determines suitability based on multiple favorable criteria including slope, aspect and distance to freeways.
Analysis Procedures
The client provided an elevation raster file and a freeway shapefile. The raster file and shapefile were added to ArcMap. Aspect and slope raster files were created from the elevation raster file. The freeway shapefile was converted to a raster buffer file. All files were reclassified for suitability and a weighted overlay was performed. The map was symbolized for visual display.
I added the shapefile and raster dataset to ArcMap. The first step was to prepare the shapefile and raster data for reclassification. I created an aspect raster file from the elevation raster file using the Aspect tool. I then created a slope raster file from the elevation raster file using the Slope tool. Using the Euclidean Distance tool, I transformed the freeway shapefile into a raster buffer file. Each of the files were reclassified using the provided favorability criteria. Finally, all layers were added to the weighted overlay tool to produce the final suitability output.
Workflow diagram (Click to enlarge)
Results
Map highlighting suitability for vineyard siting (Click to enlarge)
Image displaying certificate of course completion (Click to enlarge)
Application & Reflection
This exercise introduced site selection based on raster data and weighted overlay. Site selection analysis using raster data allows for suitability factors to be overlaid using the raster cell classifications and weighted for analysis. Weighted analysis is possible with raster data, compared to vector data, which makes it a preferred skill for determining site suitability as some attributes can be given priority over others. Raster cell classification and weighted overlay are often used for analysis in the natural sciences, particularly for those who study natural resources. However, I think that raster cell classification and weighted overlay could be adopted in sociological research as well. Such a scenario might involve intersections between the natural and social worlds. Specifically, related to climate change, sociological research might examine social responses to climate change phenomenon, such as sea level rise. Raster data of coastal areas obtained through satellite imaging could be reclassified according to sea level encroachment and overlaid with social characteristics such as population density or even poverty information (available through the US Census Bureau) to determine locations with vulnerable (i.e., poor) or highly concentrated populations. These factors might have significant impact on recommendations for mitigating the effects of climate change.
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