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Dr. Paul M. Torrens, Center for GIS, Department of Geographical Sciences, and UMIACS, University of Maryland

Big data movement analytics

Project overviewEye candy | Related groups
Project overview

Many location-aware devices now provide ongoing streams of movement data, often with allied action, activity, interaction, and transactional data following not too far behind. These silos of data enable a suite of geographical analysis and modeling techniques, which themselves then produce further data. This is particularly true of agent-based models tasked with representing fine-resolution characteristics of movement and interaction in massively dynamic systems. There is a need for smart data-mining and knowledge-building schemes that can work on these data, and all of the thorny complexity that implies.

This project is focused on producing new techniques for extracting features, processes, and phenomena from movement data-sets generated by agent-based models. It builds on our earlier work on validating agent-based models using complexity signatures and space-time analyses, as well as our work on data-mining and machine-learning. The tools being developed are working in tandem with our suite for agent-based modeling.

Eye candy


GIS movement traces big data analytics

Geographical wisps: movement trails of synthetic walkers in simulation (lines), indexed by collision (beads) and time to collision (bead color from pink to red, high to low).


speed surface movement

Relative speed of collective movement through a downtown streetscape


wireframe movement tracks GIS

Individual wire movement tracks are extruded by ambient speed in the crowd flow


collision beeds for movement analytics on big data

Collision beads atop space-time movement tracks (projected by relative ambient speed as above). The beads index collision potential in the vicinity of the track. In this case, a group of walkers were in motion side-by-side and following. Above, the illustration provides just a small window on the data. For 50 synthetic walkers on the small simulated streetscape, the model generates a total of 2.997 million collision-cast points per minute of simulated "real-time" interaction. In other words, at 30 checks-per-second, the walkers collectively build this mental map of their ambient surroundings.


crowd wave big movement data

Above, a view of a larger portion of the data-set. The persistent colored paths are actually strings of collision beads, and are illustrative of convoying of synthetic walkers in close proximity.

Related groups


GIS movement tracks

Big data movement analytics


climate indicators spatial analysis

Land indicators of climate

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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

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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