Geospatial Simulation, Three Ways
Sarah Wise (University College London)
Abstract
The term "geospatial simulation" suggests a specific, unified field of practice. In fact, geospatial elements can enter the simulation process at different points. Broadly, it is possible to classify three major branches of "geospatial" simulation: the use of geospatial data to initialise a simulation; the simulation of specifically geospatial processes or use of this data in the context of the model; and the generation of geospatial data by simulated processes. The same simulation may employ geospatial data or approaches at multiple points, so that these branches are not mutually exclusive. Indeed, they often intersect in interesting ways. It is therefore useful to consider how these different subfields interact and have developed. From the generation of spatially-informed synthetic populations to the use of simulation to create geospatial environments that inform further modelling, useful trends and best practices can be understood and applied across these branches. This talk will explore current trends in each of the categories and suggest future directions for collaboration and exploration.
Bio
Sarah Wise completed a PhD in the Computational Social Science department of George Mason University in 2014. She graduated from the University of Chicago in 2009 with a double major in Computer Science and East Asian Cultures and Language, and was a recipient of the George Mason University three-year Presidential Scholarship. She has worked for Argonne National Laboratory, the United States Department of State, and various government contractors. Her research interests lie in exploring and forecasting the development of systems involving people, infrastructure, and information using methodologies including agent based modelling (ABM), social network analysis (SNA), data mining, statistical analysis, and geographic information systems (GIS).
