Movement Data Analytics

Movement Data Analytics

The aim is to develop scalable computational data analytics, simulation, and prediction models to relate movement to its context (i.e. behavior, geography, environment, and interactions). This helps us predict individuals’ responses to environmental and behavioral variabilities at multiple scales. This research breaks new ground in computational movement data analytics for integrating heterogeneous data sets to inform and calibrate more realistic context-sensitive simulation and predictive models.

MOVE receives an NSF award

Somayeh Dodge has been awarded an NSF grant as the sole PI of the project “Visualizing Motion: A Framework for the Cartography of Movement” (NSF award # 1853681, amount $328,756). Evgeny Noi will be working on this project.

This project will examine how motion as a dynamic phenomenon, with complex space and time dimensions, is effectively represented in geographic visual displays. It will develop visualization methods and tools to map movement patterns and interaction between individuals.