Research Projects – MOVE@ucsb

Mobility and COVID-19

Published August 16, 2021May 29, 2020 by Somayeh Dodge

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 develop data-driven and knowledge-driven approaches to learn and model spatial and socioeconomic heterogeneity in exposure to COVID-19 and study the relationships between non-pharmaceutical interventions (NPIs) in terms of mobility and interaction and virus transmission at multiple geographic scales. Graduate students who are involved in this projects are: Evgeny Noi, Zijian Wan, and Vania Wang.

Modeling Movement

Published April 21, 2021October 9, 2019 by Somayeh Dodge

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 analysis and modeling of the conspecific and predator-predator interactions across a range of environmental conditions and levels of human impact. The outcomes of this study will contribute to better understanding, and ultimately prediction of the survivability and behavioral changes of tigers in a changing environment. This is especially important with the increase of human-wildlife interaction as a result of greater intensity of land-use change and urbanization.

Movement Visualization

Published November 8, 2021October 9, 2019 by Somayeh Dodge

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 moving individual through these dimensions. Our research advances cartographic theories for movement and creates innovative visualization methods to enhance fundamental knowledge on how motion should be represented in space and time and across scales. For a prototype of this research please see the DYNAMOvis project.

Movement Data Analytics

Published April 21, 2021October 8, 2019 by Somayeh Dodge

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.