| Abstract: | In landscape planning applications, practitioners and governmental agencies are often faced with a broad array of clientele and constituents having particular land use requirements and needs, ranging from biological conservation to urban development, generating complex multidimensional regional planning goals and objectives. Under this often complex situation, investigators are searching for methods to intelligently simplify complicated spatial environments and render them into interpretable and practical settings. While numerous investigators have studied the generation of a single suitability map, we were interested in addressing the problem of coping with a set of many suitability maps. We applied a data reduction method, principal component analysis, across 15 suitability overlays representing diverse landscape requirements to search for simplified explanations indicating the latent structure of the landscape. The study area was located in a moraine landscape of southern Michigan. We discovered that the 15 suitability overlays could be reduced to seven dimensions, containing 65% of the original data structure and that the seven dimensions reflect a structure where a variety of land uses each have their own optimal spatial locations, indicating low to moderate competition between potentially conflicting land uses and rendering a more easily understood environment. This approach did not render a simple elegant solution but it did reduce the complexity associated with combining many suitability maps. |
