We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.
|Title||Geospatial data mining for digital raster mapping|
|Authors||Bruce K. Wylie, Neal J. Pastick, Joshua J. Picotte, Carol Deering|
|Publication Subtype||Journal Article|
|Series Title||GIScience and Remote Sensing|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Earth Resources Observation and Science (EROS) Center|