There are three main approaches to primary data analysis. These depend on the initial data type and available attributes:
Rasterizing and ranking categorized vector layers: These are the layers that already contain all the necessary values, and at the preparation stage, all of them should be rasterized to the similar extent and resolution. Also, their categories should be ranked properly, with the highest values for the most suitable areas and vice versa. Examples of these layers are
hurricane_evacuation_zones,hurricane_inundation_zones, and so on.Ranking density rasters: These are raster heat maps that should be converted from continuous coverages to categorized values where the highest value symbolizes the most appropriate area, and the lowest is related to the least suitable area. Examples of these layers are
noise_heatmapandtree_density.Generating and ranking proximity rasters: This is the most tedious workflow. Vector layers should be rasterized...