geosimulation :: innovative geospatial simulation and analysis but innovative people

Home | Book | Research | Publications | Bio | Press | Geosimulation Labs
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

MoveBank.org

 


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