Award Abstract # 2053588
Leveraging Crowdsourced Data to Assess Spatiotemporal Patterns of Resilience in Diverse Gulf Coast Communities Impacted by Natural Hazards

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF TEXAS AT ARLINGTON
Initial Amendment Date: November 2, 2021
Latest Amendment Date: August 30, 2022
Award Number: 2053588
Award Instrument: Standard Grant
Program Manager: Joy Pauschke
jpauschk@nsf.gov
 (703)292-7024
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: November 1, 2021
End Date: October 31, 2025 (Estimated)
Total Intended Award Amount: $396,200.00
Total Awarded Amount to Date: $396,200.00
Funds Obligated to Date: FY 2022 = $396,200.00
History of Investigator:
  • Michelle Hummel (Principal Investigator)
    michelle.hummel@uta.edu
  • Chengkai Li (Co-Principal Investigator)
  • Antwi Akom (Co-Principal Investigator)
  • Aekta Shah (Co-Principal Investigator)
  • Ramez Elmasri (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of Texas at Arlington
701 S NEDDERMAN DR
ARLINGTON
TX  US  76019-9800
(817)272-2105
Sponsor Congressional District: 25
Primary Place of Performance: University of Texas at Arlington
Arlington
TX  US  76019-0145
Primary Place of Performance
Congressional District:
25
Unique Entity Identifier (UEI): LMLUKUPJJ9N3
Parent UEI:
NSF Program(s): DRRG-Disaster Resilience Res G
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 041E, 9102
Program Element Code(s): 198Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The severity and cost of flood events continue to increase in the US, often with disproportionate impacts on vulnerable populations who may have higher sensitivity to the negative effects of flooding and lower capacity to adapt. Understanding what makes communities vulnerable or resilient to flooding is critical to developing mitigation actions that can reduce the negative effects of future hazards. However, existing frameworks for assessing resilience often fall short, as they do not address important dynamic and highly localized factors that influence peoples? ability to cope with, adapt to, and recover from natural hazards. This Disaster Resilience Research Grants (DRRG) project will develop a bottom-up, community-driven framework for local-level resilience assessment by generating and utilizing high-resolution crowdsourced datasets and leveraging local knowledge and experiences to examine how the factors contributing to resilience (i.e., exposure, sensitivity, and adaptive capacity) vary over space and time. Findings will have implications for more effective resilience building. As part of the project, crowdsourcing efforts using Streetwyze, a community-driven mapping platform, will increase the awareness of flooding and its daily impacts in communities and will encourage diverse voices to participate in the collection of data to support local resilience planning efforts.

This project will use a mixed-methods, sociotechnical approach to examine how crowdsourced datasets can be leveraged to (1) improve the spatiotemporal characterization of factors that influence community resilience to flood disasters, (2) develop new metrics that account for the dynamic social and physical nature of resilience, and (3) encourage more equitable capacity-building to reduce the impacts of future floods and enhance disaster resilience across diverse populations. The project focuses on flood hazards in coastal Mississippi and engages with diverse community partners from flood-prone areas in the cities of Gulfport and Biloxi. Two novel crowdsourcing technologies that include passively collected mobility data and actively generated qualitative and imagery data will be used to characterize fine-scale spatial and temporal patterns of exposure, sensitivity, and adaptive capacity. Surveys will evaluate how community members use the crowdsourced data and will assess the role of demographic and socioeconomic factors in influencing participation in the crowdsourcing effort. Geospatial, statistical, and machine learning models will be developed and applied to integrate the crowdsourced datasets with conventional sensor, satellite, and survey data. Model outputs will be used to develop novel measurement approaches that improve assessments of the factors contributing to community resilience.

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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