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What is environmental spatial analysis?

The theory, methods, and technologies associated with proper handling and use of spatial data for the analysis and management of environmental problems and processes. Core technologies include geographic information systems (GIS), remote sensing, and quantitative spatial analysis (i.e., spatial statistics and geostatistics).



The need for environmental spatial analysis


  • Turning data into information

The volume of digital environmental spatial data is growing rapidly and requires innovative approaches to make sense of it efficiently. The data come from a variety of sources, including new remote sensing technologies, survey research, automated monitoring technologies coupled with global positioning systems (GPS), and digitization of analog data collections. Efforts to synthesize these data must be coupled with rigorous understanding of how uncertainty affects the quality of derived information.




  • Applying new computing technologies

Methods and models for evaluating environmental patterns and processes exploit developments in statistical, cognitive, and information sciences. To enhance our understanding based on available data, environmental scientists and managers need to evaluate and have access to the best approaches. The new approaches include computational modeling and simulation, fuzzy logic, artificial neural networks, and internet mapping.



  • Making better decisions through integration

The complexity of the environmental problems we face requires understanding and fusion of spatial and other data from many sources, in many formats, and from multiple disciplinary perspectives. For this reason, we are faced with problems of integration that are both technological and human in nature.



The goals of SNRE's Environmental Spatial Analysis Program
  • To demonstrate and test methods for environmental spatial analysis in the context of problems of scientific and social significance.
  • To apply spatial data analysis methods to real environmental problems.
  • To train a new generation of spatially enabled resource managers and policy analysts.
  • To transfer the best spatial analysis technologies to users and managers.