Research

MOVE@UCSB advances movement research through data science.

We leverage domain knowledge through interdisciplinary collaborations. We develop cutting-edge data-driven analytics, theory-driven computational models, and visualization techniques to analyze raw movement observations for meaningful knowledge discovery and predictions of movement. Below are some of the ongoing projects conducted by our team at MOVE@UCSB.

  • Mobility and COVID-19

    Modeling of the effects of NPIs in COVID-19 transmission Browse visualizations here: covid19-mobility-vis MOVE starts a new interdisciplinary collaboration with the UCSB Geography, Computer Science, Statistics and Applied Probability Departments, and the Information Systems and Modeling Group of the Los Alamos National Lab. The research is funded by VCR COVID-19 Seed Grants Program. The aim is to ...
  • Modeling Movement

    Analysis and Modeling of Movement of Tigers in Thailand Through international interdisciplinary collaboration with the Department of National Parks, Wildlife and Plant Conservation of Thailand, MOVE Lab develops analytical approaches and simulation models to understand how tigers and leopards interact with their environment in their ecosystem in the Thailand Western Forest Complex. The aim is to ...
  • Movement Visualization

    Mapping and Visualization of Motion This research focuses on understanding how humans perceive movement visually. Movement is realized in both a three- (or four-) dimensional space (i.e. location and time) and a multidimensional attribute space (i.e. context variables). The syntheses of these two spaces need new effective tools for dynamic visualization of the traversal of a ...
  • 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 ...