Rioting and related intra-crowd dynamics are significant human processes, but we know less about the basic behavioral science and subsequent processes that drive and shape rioting than we would like to. This is due, in large part, to the difficulty in studying riots on the ground and to the sheer complexity of riot phenomena. We know even less about the geographical dynamics of rioting, even though there is a dedicated (but only general) appreciation that geography is important. Existing work has, for the most part, adopted the most straightforward path to discovery, by examining coarse (city-scale) geographies of rioting, or in the few instances where intra-crowd riot dynamics are considered they have focused on stylized abstractions of behavior. Because of the difficulties of using standard social science inquiry to study riots (surveys, ethnographic analysis, interviews), many researchers have turned to computer modeling to create synthetic riots that can be configured, sampled, and experimented with. But, building models of something as bewilderingly complex as rioting is really quite difficult and so many short-cuts are taken. In particular, models are usually cellular-based in form (where rasters represent people and their local environment) and founded on physical interactions between relatively “dumb” particle-people (where continuum mechanics, random walks, or particle-particle forces serve as a substitute for socio-spatial interaction and behavior).

We are working to build a better (more authentic and more diagnostically-useful) model of rioting that can significantly broaden the range of questions that can be posed in riot simulations. Our approach takes the standard socio-emotional agency that actually works well in a lot of existing models, but then ‘wraps’ it with geographic functionality that helps to determine where, when, how, and in what contexts and company that agency should be used or should apply. Agents represent individual people and we endow them with the ability to sense their surroundings authentically (with vision, affect, bias, and so on) and to use that information to animate their synthetic spatial thinking, spatial activity, spatial actions and reactions, and spatial interactions in simulation. With these building-blocks in place, we can then create riot scenarios with a range of configurations, characters, events, environments, and so on. We can use these as synthetic laboratories for experimenting with ideas, plans, and policies.

This is an early prototype of an "immersive" front-end to the model that we are building, using an animation pipeline that we have developed. The underlying riot engine for this version is rather primitive, but the animations look nice. We have a separate behavioral model that is more sophisticated, and generates statistical/GIS/geovisual output (see below).

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