Using Multi-criteria Dimensional Analysis (MCDA) to find suitable electric vehicle (EV) charging stations throughout the central Oklahoma area. The MCDA analysis produced a map utilizing multiple criteria along the interstate, where supporting a best-fit model based on specific criteria we supplied. Notably, the model's output indicated optimal placement areas clustered within the city, with suitability decreasing toward the outskirts. This result aligns logically with demographic factors, as EV charging stations are better suited to densely populated urban areas with higher income levels. However, the current geopolitical landscape in Oklahoma presents challenges to widespread EV charging station implementation.

The data for this was obtained from various sources.

1. ACOG_Boundary (ACOG underscore Boundary): U.S. Census

2. ACOG_PopIncome (ACOG underscore PopIncome): 2019 5-Year American Community Survey (Census Tracts)

3. ACOG_GasStations (ACOG underscore GasStations): Oklahoma Office of Management and Enterprise Services https://oklahoma.gov/omes/services/fleet-management/fueling-stations.html

4. ACOG_Highways (ACOG underscore Highways): Oklahoma Department of Transportation

   Interstate highways (functional classification==1) and other freeways or expressways (functional classification==2) were selected.

5. ACOG_HighwayExits (ACOG underscore HighwayExits): North America Highway Exits by ArcGIS

https://www.arcgis.com/home/item.html?id=77ee392dcabd42a8b0046b9f040d5f28

Steps to create the above maps:

1. Find the Density of Points or Lines

2. Change Polygons to Raster Values

3. Reclassify to Make a Criterion Raster

4. Perform a Multi-criteria Decision Analysis