The MOVE Lab at the University of California Santa Barbara conducts basic and applied research to study movement and spatiotemporal processes such as human mobility, animal movement, migration, disease spread, and natural hazards (e.g. wildfires, hurricanes). Using large arrays of movement and tracking data sets, we develop data analytics, machine learning and knowledge discovery methods, agent-based simulation models, and visualization techniques. We apply spatial data science and computational approaches to advance the knowledge and understanding of how movement patterns are formed in dynamic natural and human systems. Current projects of the MOVE Lab are listed below. If you are interested in joining MOVE@UCSB to get involved in any of these research areas, please contact Dr. Dodge. We are always looking for talented students who are interested in computational solutions for spatiotemporal problems.

Movement Data Science

We develop scalable computational data analytics, simulation, and prediction models to study movement and its relationship to environmental and geographic contexts. Our research advances context-aware data analytics and movement models by integrating information captured from heterogeneous and high dimensional tracking data sets.

Mapping and Visualization of Motion

We are interested to learn how humans perceive movement visually and to develop meaningful representations for movement. 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. We advance cartographic theories and innovative visualizations to enhance fundamental knowledge on how motion should be represented in space and time and across scales.

Movement Ecology and Ecosystem Dynamics

Our research contributes meaningful methodologies to advance knowledge in movement ecology applications. As an example, through an 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.

Safe and Equitable Mobility in Smart Cities

We develop computational approaches to study equitable access to modern modes of transportation (e.g. bike-sharing, e-scooters, car-sharing, autonomous vehicles). The aim is to study how modern modes of transportation are used by communities of various socioeconomic status, and how the demand for such transportation systems varies across space and time. Another dimension of this research evaluates the risk (i.e. crash and other safety issues) involved in such transportation systems with a space-time focus.

Spatiotemporal Patterns in Human Health

We develop modeling and analytical approaches to study spatiotemporal phenomena related to human health. The aim is to investigate the relationships between spatiotemporal patterns of prevalent genetic disorders and their underlying geographic and environmental contexts. In particular, the objective is to develop a multi-scale modeling and simulation environment for spatiotemporal patterns of infectious diseases such as COVID-19 and genetic disorders and investigate their associations with the underlying geography, environment, historical migration patterns, and population dynamics.