Methodology for CHES research

 

Our landscape representation and modeling framework intends to render improved understanding, envisioning, and planning of CHES. In the context of multiple complexity features such as high dimensionality, hierarchical structure, heterogeneity, nonlinear relationships, feedback, path dependence, emergence, equifinanlity, and multifinality, this framework 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

   • Traditional Regression Analysis (Regression)

   • Spatial Statistics

   • Multilevel Modeling (MLM)

   • Structural Equation Modeling (SEM)

   • Latent Trajectory Modeling with ESF (LTM-ESF)

   • Landscape Survival Analysis (LSA)

   • Landscape Pseudo-History Analysis (LPHA)

4. Social Survey

5. GIS and Remote Sensing

6. Camera Trapping and Occupancy Modeling

7. Unmanned Aerial Vehicle

8. Human-Landscape 4D Visualization

9. 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!