Reference number : GC-2017-Q1-01619, amendment number : , agreement type : G, recipient legal name : Dragicevic, Suzana (Simon Fraser University), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Rapid urbanization and the associated need for natural resources create increasing pressures on and impose a high risk of loss for valuable agricultural and forested lands. Examining the interfaces, mutual interactions and feedbacks between coupled human and natural system (CHANS) is a multidisciplinary effort and require an extension in thinking that combines the traditional research methods common in the social and natural sciences. The land-use and land-cover (LULC) change process is a typical CHANS, characterized as a complex system, and rooted in local-level human interactions with the environment that have consequences at multiple spatial scales. Innovative geospatial modeling and geosimulation approaches are needed to integrate the theory of complexity science with geographic information science. These new approaches will provide effective ways to: forecast possible scenarios; evaluate the effects of different management and policy strategies; and aid in mitigating the consequences of LULC change. The proposed research program has the specific objectives to: (1) develop an advanced generation of artificial intelligence geosimulation approaches for representing the behavior of various actors in the process and forecasting the spatial dynamics and pattern of LULC changes; (2) enhance the developed models with network science concepts to enable their use in larger spatial extents; (3) design model testing, calibration and validation procedures to evaluate and compare the effectiveness of the developed models for various LULC problems. Geographic automata, particularly geospatial agent-based modeling, will be integrated with techniques at the forefront of artificial intelligence as well as with the emerging discipline of network science all within geographic information systems (GIS) and science frameworks. The spatial modeling approaches will use GIS and remote sensing datasets and implemented in the context of spatial decision-making and land-use planning for the urban metropolitan areas and forest covers primarily in Canada, British Columbia and the Metro Vancouver Region. The proposed research program will enhance the capability to understand and assess the potential outcomes of the LULC change process. It will also create geospatial modeling methodologies and tools to improve urban and natural resources planning, decision-making and policy-building at local and national levels. The knowledge generated would be transferable to other contexts., program purpose french : Rapid urbanization and the associated need for natural resources create increasing pressures on and impose a high risk of loss for valuable agricultural and forested lands. Examining the interfaces, mutual interactions and feedbacks between coupled human and natural system (CHANS) is a multidisciplinary effort and require an extension in thinking that combines the traditional research methods common in the social and natural sciences. The land-use and land-cover (LULC) change process is a typical CHANS, characterized as a complex system, and rooted in local-level human interactions with the environment that have consequences at multiple spatial scales. Innovative geospatial modeling and geosimulation approaches are needed to integrate the theory of complexity science with geographic information science. These new approaches will provide effective ways to: forecast possible scenarios; evaluate the effects of different management and policy strategies; and aid in mitigating the consequences of LULC change. The proposed research program has the specific objectives to: (1) develop an advanced generation of artificial intelligence geosimulation approaches for representing the behavior of various actors in the process and forecasting the spatial dynamics and pattern of LULC changes; (2) enhance the developed models with network science concepts to enable their use in larger spatial extents; (3) design model testing, calibration and validation procedures to evaluate and compare the effectiveness of the developed models for various LULC problems. Geographic automata, particularly geospatial agent-based modeling, will be integrated with techniques at the forefront of artificial intelligence as well as with the emerging discipline of network science all within geographic information systems (GIS) and science frameworks. The spatial modeling approaches will use GIS and remote sensing datasets and implemented in the context of spatial decision-making and land-use planning for the urban metropolitan areas and forest covers primarily in Canada, British Columbia and the Metro Vancouver Region. The proposed research program will enhance the capability to understand and assess the potential outcomes of the LULC change process. It will also create geospatial modeling methodologies and tools to improve urban and natural resources planning, decision-making and policy-building at local and national levels. The knowledge generated would be transferable to other contexts., agreement title english : Modeling geospatial processes: Integrating artificial intelligence with complexity, networks and geographic information sciences, agreement title french : Modeling geospatial processes: Integrating artificial intelligence with complexity, networks and geographic information sciences, agreement number : RGPIN, agreement value : 290000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada