Our work in agent-based models often generates huge volumes of data, which we must then sweep, intelligently, to explore phenomena and outcomes in simulation. As our models have grown in size and detail, we have been forging new schemes for organizing the data that they produce, and new media for exploring the dynamics that they suggest. Much of this is based around spatio-temporal GIS, which fuse data models, data access schemes, and visualization approaches that treat dimensions of both space and time.

The image above shows movement paths for individual, synthetic "agent" pedestrians in our immersive urban model. Agent paths are color-coded by their origin in the simulation, so that we can keep track of group behavior as dynamics of the simulated events unfold. The geovisualization of the paths is used to illustrate geography of movement (sinuosity along the two-dimensional plane that the street network affords) as well as progression in time (the height of the path-ribbon indicates the speed of movement at that place and time; where the ribbon "drops", an agent has slowed-down, usually to avoid a collision or to steer to pursue a new waypoint in their journey). More details of some of the movement analytics that we use to build these geovisualizations are here.

Massive ice CA

Spatiotemporal GIS

Urban simulation in GIS

Big data movement analytics

Land indicators of climate

High-performance computing and networking for geosimulation

Earthquake models

Ice-sheet modeling

Dynamic physics for built infrastructure

Moving agents through space and time

Validating agent-based models

Machine-learning behavioral geography

Accelerating agent-based models

Megacity futures

Immersive modeling

Space-time GIS and analysis

A toolkit for measuring sprawl

Modeling time, space, and behavior