Award Abstract # 1952196
SCC-IRG Track 2: Integrating Information Flows and Supporting Communities as Decision-Makers in Response to Acute and Chronic Stressors

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF TEXAS AT AUSTIN
Initial Amendment Date: August 4, 2020
Latest Amendment Date: May 19, 2021
Award Number: 1952196
Award Instrument: Standard Grant
Program Manager: Daan Liang
dliang@nsf.gov
 (703)292-2441
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: September 1, 2020
End Date: August 31, 2025 (Estimated)
Total Intended Award Amount: $1,200,000.00
Total Awarded Amount to Date: $1,216,000.00
Funds Obligated to Date: FY 2020 = $1,200,000.00
FY 2021 = $16,000.00
History of Investigator:
  • Katherine Lieberknecht (Principal Investigator)
    klieberknecht@utexas.edu
  • Keri Stephens (Co-Principal Investigator)
  • Suzanne Pierce (Co-Principal Investigator)
  • Fernanda Leite (Co-Principal Investigator)
  • Lourdes Rodriguez (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Texas at Austin
110 INNER CAMPUS DR
AUSTIN
TX  US  78712-1139
(512)471-6424
Sponsor Congressional District: 25
Primary Place of Performance: University of Texas at Austin
TX  US  78759-5316
Primary Place of Performance
Congressional District:
37
Unique Entity Identifier (UEI): V6AFQPN18437
Parent UEI:
NSF Program(s): S&CC: Smart & Connected Commun
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 116E, 042Z, 9251, 9102, 9231, 9178
Program Element Code(s): 033Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Local communities, and the people who live within them, experience firsthand the impacts of severe weather events, such as flooding, extreme heat, and wildfire. At the same time, vulnerable populations see these impacts further magnified by chronic stressors such as poverty and poor health. As extreme weather events increase in frequency and severity, all communities across the US?and especially vulnerable ones? need to improve resilience planning and outcomes. At the same time, residents on the front line of acute events and chronic stress often have critical knowledge about the characteristics of these episodes, the harms they cause, and potential solutions. This valuable local knowledge has been difficult to integrate into policy and decision-making. Local agencies, nonprofit organizations, researchers, and communities have identified the urgent need to better link everyday knowledge about people?s experiences of and responses to extreme weather events to larger scale planning and preparation work conducted by municipalities and their partners. To address this need, this Smart & Connected Communities project will build upon a partnership with the Dove Springs community of Central Texas ? a diverse, socially vibrant, but economically challenged neighborhood experiencing repeated and severe flooding?to develop a safe and secure online portal where residents can share knowledge about their community and their response to stressors. Using data shared through this portal, researchers will work with community members to design an information framework and policy process to link local knowledge with existing large-scale data sets?such as Census data, historic flood information, and land cover data? already used to prepare for extreme weather events, while building long-term community health, prosperity, and safety. These integrated data will also be used to envision and model healthy, safe, and prosperous futures at regional and statewide scales. This project serves the national interest by contribution to the National Science Foundation?s mission to promote the progress of science advancing national health, property, and welfare. Specifically, the models, information framework, online data portal, community engagement processes, policy-making process, and data security protections created for this project can be adapted by other communities in the US experiencing extreme weather events and chronic stressors, serving the national interest and society. Furthermore, this project will promote the progress of science through new contributions to data and social sciences; advance the national health and welfare through enhanced resilience in the pilot community through better preparation for extreme events and chronic stressors; and create professional development and educational opportunities for participating residents.

In this project, we will produce a community-led, innovative interface which will help residents prepare for acute stressors while building long-term resilience to chronic stressors; a safe, secure, and private technological system to collect, store, and analyze local vulnerability indicator data based on lived experiences of extreme weather events, community assets, and day-to-day threats to well-being; an original knowledge management framework to integrate and aggregate multiple data types and scales; a co-developed process to integrate local data into resilience policy and decision-making; and a system to integrate local knowledge into big-dataset integrative modeling. We will use integrative research methods, including qualitative and quantitative methods to conduct a longitudinal assessment of community resilience and measure decision-making processes; participatory co-design of the portal; development of data privacy processes and construction of a knowledge graph; and data visualization, multi-modal analysis, and machine-supported reasoning. We anticipate the following broader impacts to society: provision of local knowledge to improve municipal and nonprofit decision and policy-making; sharing of innovative contributions with other US cities; establishment and training of residents as Climate Navigators (community-based experts on the neighborhood-scaled impacts of climate events), which will increase short and long-term resilience in the community (including opportunities for professional development and a Higher Ed Pathway Project); and integration of local knowledge into computer-based modeling protocols to improve the ability to guide resilience, preparedness, and acute responses across the state.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Lieberknecht, Katherine "Community-Centered Climate Planning: Using Local Knowledge and Communication Frames to Catalyze Climate Planning in Texas" Journal of the American Planning Association , v.88 , 2022 https://doi.org/10.1080/01944363.2021.1896974 Citation Details
Lieberknecht, Katherine "Leading with Local Knowledge: Climate Adaptation, Local Knowledge, and Participation in Austin, Texas Network of Plans and the Co-Designed Climate Navigators Tool and Process" Journal of Planning Education and Research , 2023 https://doi.org/10.1177/0739456X221144610 Citation Details
Lieberknecht, Katherine and Carlson, Nancy and Stephens, Keri and Leite, Fernanda and Acuña, Frances and Lowell, Jonathan "Closing the Climate Gap: Insights from Local Knowledge for Climate Adaptation Planning" Journal of the American Planning Association , 2024 https://doi.org/10.1080/01944363.2024.2318226 Citation Details
Ma, Jong Won and Leite, Fernanda and Lieberknecht, Katherine and Stephens, Keri K and Bixler, R Patrick "Using Q-methodology to discover disaster resilience perspectives from local residents" International Journal of Disaster Risk Reduction , v.104 , 2024 https://doi.org/10.1016/j.ijdrr.2024.104353 Citation Details
Xu, Yifan and Stephens, Keri K. and Carlson, Nancy H. and Lieberknecht, Katherine E. and Leite, Fernanda "Moving toward community preparedness efficacy: Uncovering barriers in communities disproportionately impacted by flooding" Journal of Contingencies and Crisis Management , v.32 , 2024 https://doi.org/10.1111/1468-5973.12524 Citation Details

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