About Us

 

The MOVE Research Group 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 movement responses to 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 to answer questions surrounding the behavior of moving individuals and interactions in social and ecological systems. We apply spatial data science and computational approaches to investigate how movement patterns are formed in dynamic natural and human systems and how individuals respond to changes in their environments. 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. Somayeh Dodge.

We develop scalable computational movement analytics, simulation, and prediction models to study movement and its relationship to environmental and geographic processes. Our research advances context-aware data analytics and movement models by integrating information captured from heterogeneous and high dimensional tracking data sets. We are interested in understanding how new forms of data can contribute to better modeling and understanding of movement patterns in greater details across spatial and temporal scales, and how representative the observed patterns are across populations. For example, see this study published in the International Journal of Geographical Information Science.

We are interested to learn how humans perceive movement visually and to develop meaningful representations for movement and patterns of 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 effective tools for dynamic visualization of the paths of moving individuals 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. For example, see the DynamVis software developed by students in the MOVE Lab to map animal tracking data in relationship to the environmental factors.

Our research contributes meaningful methodologies to advance knowledge about the behavior of animals 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. For example, see this study published in 20204 in Movement Ecology.

We develop computational approaches to study equitable access to modern modes of transportation (e.g. bike-sharing, e-scooters, car-sharing, autonomous vehicles). We are also exploring the impact of environmental disruptions, such as wildfires, on mobility and access of communities. The aim is to study how movement flows of communities of various socioeconomic status may be impacted by modern means of transportation, and how these flows might respond to disruptive events. Another dimension of this research evaluates the resilience of transportation systems in urban environments.