CAREER: Modeling Movement and Behavior Responses to Environmental Disruptions
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2043202
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Key facts
Disease
COVID-19Start & end year
20212026Known Financial Commitments (USD)
$78,645Funder
National Science Foundation (NSF)Principal Investigator
Somayeh DodgeResearch Location
United States of AmericaLead Research Institution
University of California-Santa BarbaraResearch Priority Alignment
N/A
Research Category
Secondary impacts of disease, response & control measures
Research Subcategory
Other secondary impacts
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
This project is about analyzing the effects of significant environmental disruptions on peoples' behavior across different geographic and temporal scales. Using movement as a positional marker, this project will provide data-driven results about the consequences of environmental change caused by disruptive events such as wildfire outbreaks, pandemics, and human-wildlife conflict situations. Intentional movement is an important trait shared among humans and animals and is a fundamental actor in the dynamics of geographical environments. This research will enable scientists to model behavioral adaptation caused by disruptive events. The transdisciplinary methods developed in this study are generalizable to understanding adaptation in both social and ecological systems, contributing new knowledge about spatial behavior of humans and keystone species. The project's educational initiatives will raise geography awareness and advance spatiotemporal thinking and will broaden participation of college students and educators in STEM education and research.
Despite the advances in tracking technologies, there still exists limited in our understanding of the mechanisms governing movement patterns. This interdisciplinary project will transform research and education in geographic information science by integrating information on both spatial and temporal change using computational movement analytics. This study will investigate three core research questions: 1) Are individuals' movement and behavior driven by their environment, and how do these relationships vary across spatial scales? 2) How can data science better capture movement patterns that reflect change in behavior? 3) How can movement be used as a proxy to understand and predict the behavioral adaptation of individuals caused by disruptive events? This project will create potentially transformative computational approaches for machine learning, modeling, and simulation of spatial behavior in environmental disruptions that includes wildfire, COVID-19 pandemic, and human interaction with endangered species. The first two studies focus on human movement responses and adaptation to short- and long- term environmental changes, while the latter evaluates resilience in an environment experiencing gradual land-use change.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Despite the advances in tracking technologies, there still exists limited in our understanding of the mechanisms governing movement patterns. This interdisciplinary project will transform research and education in geographic information science by integrating information on both spatial and temporal change using computational movement analytics. This study will investigate three core research questions: 1) Are individuals' movement and behavior driven by their environment, and how do these relationships vary across spatial scales? 2) How can data science better capture movement patterns that reflect change in behavior? 3) How can movement be used as a proxy to understand and predict the behavioral adaptation of individuals caused by disruptive events? This project will create potentially transformative computational approaches for machine learning, modeling, and simulation of spatial behavior in environmental disruptions that includes wildfire, COVID-19 pandemic, and human interaction with endangered species. The first two studies focus on human movement responses and adaptation to short- and long- term environmental changes, while the latter evaluates resilience in an environment experiencing gradual land-use change.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.