POWRE: Artificial Neural Networks for Spatial Aggregation and Disaggregation Problems

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Advances in satellite remote sensing (RS) and geographic information systems (GIS) technologies are contributing to rapid expansion in the availability and use of spatial information about Earth's environment at local to global scales. They have also led to the use of new methods in spatial analysis based on computational intelligence (CI). Artificial Neural Networks (ANN) are one type of CI that have been widely used in geography and RS for classification, change detection, optimization and spatial modeling. The main objective of this research is to apply ANN for spatial scaling, particularly aggregation and disaggregation resulting from the use of multiscale and multiresolution data in GIS and RS. Three specific areas of geographical enquiry will be addressed: first, characterization of land cover at the global scale, including improved estimates of land cover area as well as changes in land cover area; second, developing models integrating socio-economic and physical data collected at different spatial scales (e.g., Mediterranean Desertification and Landuse or MEDALUS); and third, the population census zone design problem, in which data are aggregated from fine to coarser spatial scales. This type of spatial analysis and modeling has a number of practical applications including census geography, global change research, land surface characterization, strategic landuse and transportation planning, environmental analysis and planning, and allocation of education, health, and other services. This research, which will focus on analytical and methodological issues centered around ANN applications in geography and remote sensing, will involve collaboration between the principal investigator and two leading European researchers. The POWRE award will enhance the principal investigator's research and educational experience, as well as contribute to her academic advancement and contribute to her leadership potential in the field in which women are significantly underrepresented.