Raster analysis
Unlike our previous analysis, now that we do not have input points or areas to choose from, we have to delimit those areas based on different criteria. That alone raises the idea of using rasters. Additionally, this time we not only have Boolean criteria (inside or outside), but also have some continuous preferences (closer, or farther, the better). This factor calls for raster analysis. In raster analysis, we can consider almost the same classification as in vector analysis:
- Overlay analysis: Masking a raster layer with a binary mask layer. Where the binary mask layer has a zero value, we drop the value of the other raster layer, or set it to zero.
- Proximity analysis: Analyzing the distance between features or cells, and creating a raster map from the results. The raster map can contain real-world distances (Appendix 1.12) or raster distances (number of cells) from features or non-null cells in the input vector or raster map.
- Neighborhood analysis: Analyzing the neighborhood...
