Integrating complexity, GIS and soft computing for multi-scale spatial modeling and decision-making

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The increasing impact of human activities on the physical and natural environment has resulted in many unplanned land-use and land-cover (LULC) changes. Activities such as urbanization, deforestation and industrial development are now strongly associated with urban sprawl, while natural hazards such as forest fires and insect infestations have generally reduced the quality of the natural landscape. LULC change processes are difficult to predict as they are influenced by many complex and non-linear interactions between variables from human and biophysical systems. The long-term goal of this research program is to advance scientific understanding about the structure and mechanisms of LULC change processes for improving spatial prediction and decision-making. Complexity science and GIScience will be used as the theoretical foundations to develop dynamic and realistic land transformation models. The main objectives are to develop: (1) novel agent-based modeling (ABM) and CA approaches within geographic information systems (GIS) and Web-GIS frameworks; (2) soft computing approaches for handling and processing complex geospatial data sets at multiple spatial and temporal scales; (3) parallel processing methods and algorithms for robust geosimulations of large geospatial datasets; and (4) calibration and validation methods to generate reliable and useful models for spatial decision-making in planning and management contexts. Linking the developed approaches and tools to stakeholder collaboration methods will narrow the gap between modeling at a research level and meaningful real-world applications. This research contributes to expanding the scientific knowledge of LULC change processes and to advancing the field of GIS and GIScience to include dynamic capabilities using innovative spatial modeling approaches. The implementation of spatial decision support systems and tools based on the LULC models will further aid collaborative environmental planning and management. Scientists, planners and policy-makers in forestry, agriculture, and urban land-use will benefit from this research by having objective and scientific foundations on which to make effective decisions.