Geovisualizing, representing, analyzing, modeling, and simulating

Complex Human-Environment Systems (CHES)

for improved envisioning, understanding, and planning

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Methodology for CHES research

 

Our digital, high performance (e.g., big data friendly, parallel/clouds computing enabled, web-based), and 4-D holographic methodology for landscape representation and modeling intends to render improved understanding, envisioning, and planning of CHES. In the context of multiple complexity features, including high dimensionality, hierarchical structure, heterogeneity, nonlinear relationships, feedback, path dependence, emergence, equifinanlity, and multifinality, this methodology contributes to harnessing (rather than ignoring or eliminating) CHES complexity such that innovative actions might be taken to steer the CHES under investigation in beneficial directions. Below is a list of exemplar methods we often rely on.

  1. Spatial Agent-Based Modeling (ABM)

  2. Space-Time Analysis

  3. Statistical Methods

  4. Social Survey

  5. GIS and Remote Sensing

  6. Camera Trapping and Occupancy Modeling

  7. Human-Landscape 4D Visualization

  8. Geocomputation and Programming


As new methods, technologies, or projects emerge, our digital, 4-D holographic methodology will surely evolve and become more sophisticated. We keenly welcome more researchers and practitioners to join our endeavors. We believe that methodological explorations are not only useful, but also fun!



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