MOVE@UCSB advances movement research through data science.

 

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.




  • Modeling of the effects of NPIs in COVID-19 transmission

    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 develop data-driven and knowledge-driven approaches to learn and model spatial and ...
  • 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 increase our understanding of species resilience through the ...
  • Mapping and Visualization of Motion

    This research focuses on understating 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 moving individual through these ...
  • Movement Data Science

    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 ...