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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF MARYLAND, COLLEGE PARK
Doing Business As Name:University of Maryland College Park
PD/PI:
  • Kathleen Stewart
  • (301) 405-3230
  • stewartk@umd.edu
Co-PD(s)/co-PI(s):
  • Debbie A Niemeier
  • Junchuan Fan
Award Date:04/02/2020
Estimated Total Award Amount: $ 82,087
Funds Obligated to Date: $ 82,087
  • FY 2020=$82,087
Start Date:04/01/2020
End Date:03/31/2021
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:RAPID: Using location-based big-data to model people's mobility patterns during the COVID-19 outbreak
Federal Award ID Number:2027412
DUNS ID:790934285
Parent DUNS ID:003256088
Program:Geography and Spatial Sciences
Program Officer:
  • Scott Freundschuh
  • (703) 292-7076
  • sfreunds@nsf.gov

Awardee Location

Street:3112 LEE BLDG 7809 Regents Drive
City:COLLEGE PARK
State:MD
ZIP:20742-5141
County:College Park
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Maryland College Park
Street:LeFrak Hall,7251 Preinkert Drive
City:College Park
State:MD
ZIP:20742-0001
County:College Park
Country:US
Cong. District:05

Abstract at Time of Award

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to study the impacts of a rapidly expanding pandemic on human mobility. This research investigates how to measure changes in collective movement of people in response to the fast-evolving COVID-19 outbreak using large datasets of passively collected location data. It examines how locations within a state respond to public policy implementation and times of critical public messaging. Detailed knowledge on movement patterns of people can help public officials identify hotspots and critically isolated populations, as well as shed light on those groups who continue to travel for work or other purposes. This research contributes to improving the public response to an emergency and contributes to bridging different stakeholder mitigation strategies. Detailed knowledge of how people respond to a fast-spreading global pandemic is very limited and our understanding of these responses is mostly for small areas. This research will use a near real-time location-based dataset passively collected through the use of location-based apps during the period of pandemic. The project will develop scalable, big location-based algorithms to extract trips and examine the evolution of mobility patterns throughout the pandemic, and identify different mobility patterns. We will develop map-reduce based distributed algorithms to scale up mobility measure calculations based on the big location-based data as well as develop entropy measures to capture the time-varying characteristics associated with the travel patterns, and design strategies to correct biases that may be present in the location data. The methods and results of this research will be useful for understanding mobility during other hazards that affect communities, such as severe flooding to understand how travel is changed as a result of imperatives stemming from both the hazard and policy directives. 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.

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