I use research methods across the Geospatial, Environmental, Social, and Systems sciences to understand and predict the dynamics of socio-environmental systems

Generation of spatial data for public planning

Public planning revolves around utilization of spatial datasets to inform next development actions. Much of the lack of information for science-based public planning is stemmed from the difficulty of mapping the spatial distribution of target variables. The field of Geospatial science is useful for generating such spatial datasets by utilizing combinations of techniques that involve acquiring, processing, and relating spatial data. The first contribution of my research is the generation of spatial data through Geospatial science techniques. To do so, I have developed tools and models to derive such spatial datasets.

Simulating emergent large-scale phenomena

Agent-based modeling is a systems modeling approach for simulating the emergent phenomenon rising from small-scale system changes made by heterogenous decision-making entities called agents. Agent-based modeling is especially useful in public planning for simulating the impacts of policy scenarios such as urban development that human agents can sense and react to. The second contribution of my research is the simulation of the emergent outcome of policy scenarios by modeling the attributes and behaviors of human agents and modifying the agents or the environment to mimic policies. To do so, I have developed spatial agent-based models and modeling frameworks to enable the simulation of policy scenarios.