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Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University |
Modeling riots
Project overview | Eye candy | Demo movie | Support | Related groups | |||
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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).
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Movie |
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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).
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Eye candy | |||
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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. | |||
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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. | |||
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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. | |||
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Support |
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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) |
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Related groups | |
TBD |
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![]() Robot motion control |
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![]() Human behavior in critical scenarios |
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![]() Modeling riots |
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A toolkit for measuring sprawl
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![]() Simulating crowd behavior |
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![]() Wi-Fi geography |
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![]() Simulating sprawl |
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