geosimulation :: innovative geospatial simulation and analysis but innovative people

Home | Book | Research | Publications | Bio | Press | Geosimulation Labs
Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University

Modeling riots

Project overview | Eye candy | Demo movie | Support | Related groups
Project overview

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.

wired riot model crowd model geosimulation
My riot modeling work was featured in the January 2012 edition of Wired magazine in the United States: "#Riot: self-organized, hyper-networked revolts—coming to a city near you" (see page 82),


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).

Eye candy
riot model
Above, a screenshot from the graphic interface to the riot model. The user can "drop into" the riot simulation, immersively and walk/fly around, etc.
The figure above illustrates a geostatistically-generated "riot surface" from the riot model, with the relative positions of police (gray), rioters (red), non-rioters (blue), and "vulnerable" onlookers.
The figure above illustrates the space-time paths for a sequence of interactions between rioters ("rebels"), non-rioters ("civilians), and police in a small section of the simulated space. X and Y axes represent space; the Z axis shows time. So, if a column/pillar appears, that indicates that agents move in time but not space (i.e., they are standing still). This is the case for apprehended rioters ("arrested") that the police have successfully (1) notice, (2) chased, and (3) apprehended. We leave these agents in situ once captured, just to show where they were at that space/time.
Torrens, P.M. (2007-2012) “CAREER: Exploring the dynamics of individual pedestrian and crowd behavior in dense urban settings: a computational approach”. National Science Foundation (Faculty Early Career Development (CAREER); Geography & Regional Science/ Methodology, Measurement, and Statistics)
Related groups


GIS movement tracks

Big data movement analytics


climate indicators spatial analysis

Land indicators of climate

geosimulation high performance computing

High-performance computing and networking for geosimulation

earthquake model agent based GIS

Earthquake models

CA ice sheet model

Ice-sheet modeling

kinect control of GIS and robots
Robot motion control

simulating disasters ABM GIS
Human behavior in critical scenarios

crowd model riot model simulation wired

Modeling riots

physics engine GIS

Dynamic physics for built infrastructure

moving agents through space and time

Moving agents through space and time

validating agent based models

Validating agent-based models

machine learning GIS

Machine-learning behavioral geography

high performance computing urban simulation emergence

Accelerating agent-based models

megacity models

Megacity futures

immersive modeling

Immersive modeling

space-time GIS

Space-time GIS and analysis

measuring sprawl

A toolkit for measuring sprawl

space-time GIS

Modeling time, space, and behavior

simulating crowd behavior

Simulating crowd behavior

wi-fi geography

Wi-Fi geography

Simulating sprawl

Simulating sprawl