Socially-integrated robust communication and information-resource sharing technologies for post-disaster community self-reliance
Lead PI:
Cynthia Chen
Co-Pi:
Abstract

When a disaster strikes, communities often become isolated and citizens come together to help each other: people share resources, pass along information, and take on tasks that are outside of their usual domains. These activities have been reported in both academic literature and anecdotal documents. Some examples include: New Yorkers’ sharing private vehicles and boats during the 2005 MTA strike that crippled NYC transit services; and neighbors helping neighbors escape from flooding caused by 2016’s Hurricane Matthew using rafts improvised from inflatable mattresses in Rowland, NC. These peer-to-peer resource sharing activities fill important gaps during times of disaster that cannot be fulfilled by emergency response agencies. This project helps fill this gap by working with urban and rural, higher- and lower-income communities in Washington State to understand and advance more effective local information and resource dissemination during a disaster. The project integrates hardware, robust communication technologies, social capacities, and spatial conditions to leverage and enhance place-based social networks for information-resource sharing; and investigate a little-studied scientific frontier intersecting communications, sharing, and disaster resilience. The results of the project will be scalable and useful on a daily and emergency basis to communities that increasingly face natural disaster risks and are interested in enhancing their resilience through information and resource sharing.

More specifically, the project will co-design with the two communities and conduct research in four thrusts. Thrust 1—robust communications, will develop robust off-grid community-based networks that are owned and operated by the community. Thrust 2—building community social networks for information-resource sharing, will conduct surveys to collect information about people’s sharing behavior and their social ties within a community, and develop novel models to infer community-based social networks. Thrust 3—community-based information-resource sharing, will develop models and strategies that will lead to efficient information-resource sharing. The key hypothesis is that social network structures affect the optimality and stability of information-resource sharing. Thrust 4 (Dynamic Map your Neighborhood) integrates the results of Thrusts 1-3 and uses them to co-design and pilot-test applications with two communities in WA (urban and rural). The diverse team expertise facilitates knowledge and methods across disciplines through the design of robust communication technologies, and novel ways to solicit social ties information and information-resource sharing models that considers both model optimality and human inputs (e.g., leader nominations from the communities).

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.

Cynthia Chen
Bio: Cynthia Chen is a professor in the Department of Civil & Environmental Engineering at the University of Washington (Seattle). She is an internationally renowned scholar in transportation science and directs the THINK (Transportation-Human Interaction and Network Knowledge) lab at the UW. Cynthia has published numerous peer-reviewed publications in leading journals in transportation and systems engineering including Transportation Research Part A-F and PNAS. Her research has been supported by many federal and state agencies. She is an associate director of TOMNET (Center for Teaching Old Models New Tricks), a USDOT-funded Tier 1 University Transportation Center led by ASU, as well as a co-investigator of the new Center of Understanding Future Travel Behavior and Demand, a USDOT-funded national center led by UT Austin. Currently, Cynthia is an associate editor for Transportation Science, and is on the editorial board of Sustainability Analytics and Modeling.
Performance Period: 10/01/2023 - 09/30/2027
Institution: University of Washington
Award Number: 2311405
Smart and Safe Prescribed Burning for Rangeland and Wildland Urban Interface Communities
Lead PI:
Xiaolin Hu
Co-Pi:
Abstract

Prescribed fires have long been used by ranchers and farmers in the Great Plains as a land management tool. They help farming and grazing by replenishing the soil, increasing forage production, and protecting prairies from invasive overgrowth. They are also used by rural and Wildland Urban Interface (WUI) communities to remove built-up fuels for reducing risks of wildfires. Despite the many benefits of prescribed fires, there are safety and environmental concerns for prescribed burn events. On the safety aspect, an escaped fire or a fire reignited from smoldering fuels can become uncontrolled and result in severe property damages and injuries to people. On the environmental aspect, smoke from prescribed fires causes air pollution for local communities and communities downwind. To manage and minimize these concerns, optimal planning and execution of prescribed fires are crucial. The objective of this project is to develop a community sensing, planning, and learning infrastructure to support smart and safe prescribed burning for communities that use prescribed fires for rangeland and wildfire risk management. The developed infrastructure will be integrated into a cloud-based platform to support landowners to optimally plan and operate prescribed burns, collect and share data about burning, and train fire operators to learn the most effective ways of burning. The project will also promote technology awareness for building smart communities in rural areas, by increasing partnerships among academia, rural communities, and local governments.

The integrated research of this project includes: 1) technical research on multi-scale sensing and data fusion, data-driven burn condition modeling, grassland fuel mapping & hotspot detection, and fire behavior modeling and simulation; 2) social science research that addresses the knowledge gap on how communities engage with and coordinate burn practices through the use of technology; and 3) community engagement that develops tools, data repositories, and activities to support communities’ smart and safe prescribed burning. The multiscale sensing and data fusion integrate data from heterogeneous sources including satellite remote sensing, unmanned aircraft systems, and crowdsourced reports. We will work with two communities in Kansas to evaluate and demonstrate the developed research: 1) The Gyp Hills community represents a rangeland community where an average prescribed fire covers over hundreds of acres for grasslands primarily used for grazing; 2) the Eastern Kansas community represents a suburban WUI community where prescribed fires are employed at a smaller scale.

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.

Xiaolin Hu
Dr. Xiaolin Hu is a Professor and Director of Undergraduate Studies, and Director of the Systems Integrated Modeling and Simulation (SIMS) Lab of Computer Science Department at Georgia State University. He received his Ph.D. degree from the University of Arizona, Electrical and Computer Engineering Department in 2004. His research interests include modeling and simulation theory and application, complex systems science, agent and multi-agent systems, and advanced computing in parallel and cloud environments. His work covers both fundamental research and applications of computer modeling and simulation. Dr. Hu was a National Science Foundation (NSF) CAREER Award recipient.
Performance Period: 10/01/2023 - 09/30/2027
Institution: Georgia State University Research Foundation, Inc.
Award Number: 2306603
Strengthening Resilience of Ojibwe Nations Across Generations (STRONG)
Lead PI:
Kimberly Marion Suiseeya
Co-Pi:
Abstract

Climate change exacerbates existing threats to the livelihoods and well-being of many Native American nations across the United States. Additionally, the effects of invasive species, mining, and development have been increasing on critical ecosystems that provide food, water, and cultural security for Indigenous Peoples. Working with tribal partners, this Smart and Connected Communities Integrative Research Grant (SCC-IRG) seeks to understand how enhanced data access, availability, and usability can strengthen community resilience. The project converges social science, data science, environmental science, and computer engineering through a traditional knowledge framework to identify key links between resilience and sovereignty of Indigenous communities. Deployment of cyberinfrastructure with advanced sensing technologies helps demonstrate how multi-model socio-ecological data can be combined into actionable resilience frameworks. As the result, new pathways of climate adaptation are created for culturally significant plants and animals, as well as guiding development plans to minimize adverse impacts.

This tribally driven project adopts a traditional knowledge framework to synthesize traditional and scientific knowledge to advance innovations in resilience research in three areas: 1) Environmental Science: the project deploys state-of-the-art Wild Sage edge-enabled sensing platforms and tiny battery-free energy-harvesting sensors to continuously measure water and ecosystems conditions, and to assess the effects of climate change, mining contaminants, and oil/gas pipeline failures on manoomin (wild rice) and associated wetland ecosystems; 2) Governance: the project assesses the effects of how scientific knowledge generated through a traditional knowledge framework impacts tribal planning and governance by co-producing and evaluating culturally appropriate resilience indicators for anticipating, responding to, and mitigating acute and chronic socio-ecological perturbations; and 3) Community Impact: the project co-develops and deploys Noondawind, a dynamic, integrated cyberinfrastructure platform that connects diverse end-users with STRONG data and analyses to examine how access to data strengthens tribal sovereignty and resilience. By making research products more responsive to community-identified needs and enhancing accessibility of cyberinfrastructure for diverse end-users, this project aims to demonstrate how data can play a central role in building governance capacity and strengthening intertribal coordination on common environmental, economic, political, and social well-being priorities.

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.

Kimberly Marion Suiseeya
Kimberly Marion Suiseeya is an environmental social scientist with expertise in environmental justice, global environmental politics, Indigenous politics, and community-driven research. Her research examines how Indigenous communities shape and are impacted by multilateral environmental agreements like the UN Framework Convention on Climate Change. She is a Commission Member of the IUCN’s Commission on Environmental, Economic, and Social Policy, a Research Fellow with the Earth System Governance project, and a member of the Earth System Governance project’s Planetary Justice Taskforce. Dr. Marion Suiseeya is also an experienced policy practitioner who has worked and conducted research in Guyana, Laos, Thailand, Myanmar, and the US. Her research is supported by the National Science Foundation and the Alfred P. Sloan Foundation.
Performance Period: 08/01/2023 - 07/31/2026
Institution: Northwestern University
Award Number: 2233912
Connecting coastal communities with continuous, sensor-based monitoring of water quality
Lead PI:
Christopher Neill
Co-Pi:
Abstract

Coastal communities face large challenges monitoring water quality in multiple places and frequently enough to identify water quality problems and to document improvements following investments in programs or infrastructure that improve water quality. Traditional water quality monitoring is conducted by periodic collection of physical water quality samples. However, conditions that lead to large water quality and biological impacts (such as periods of low oxygen during heat waves or following periods of high river discharge) occur infrequently and are not well captured by existing monitoring programs that sample only periodically. This makes it hard for citizens to document problems and for managers to identify appropriate water quality actions. New continuously-recording water quality sensors now have the potential be deployed across multiple locations to provide time-series and information to better identify water quality impacts and potential solutions. Dissolved oxygen is a key water quality parameter that can now be measured with reliable, low-cost sensors. However, the transition to continuous sensor-based monitoring is complex and will require concurrent changes to the institutions that conduct monitoring, their norms and social practices, and the way the public and regulatory agencies use this new form of water quality data.

This project will determine how volunteer citizen scientists respond to use of in situ recording sensors rather than grab samples, and test how volunteers respond to different procedures for deploying and interacting with sensors. The project will also test low-cost mobile platforms for deploying sensors in coastal waters. It will focus on three communities connected to water quality in the watershed of Buzzards Bay, Massachusetts: (1) local residents who live within coastal watersheds, including many who volunteer to collect water samples; (2) town officials responsible for water quality planning and regulation; and (3) state officials responsible for setting and enforcing water quality regulations. It will determine which combinations and scales for sensor deployment produce data most likely to be used and acted upon by residents, town officials, and government regulators. It will also provide knowledge on how different communities interpret and make sense of more detailed water quality data derived from sensors compared with data from traditional grab samples. Understanding this transition—both technologically and socially—will help advance the use of water and environmental sensors by the many organizations across the U.S. that conduct environmental monitoring and that might in the future deploy automated continuous environmental sensors.

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.

Christopher Neill
Dr. Christopher Neill studies natural and restored ecosystems around the globe, always with a local focus. He is interested in how changes in land use affect ecosystem function, particularly water and nutrient cycling, and in our ability to restore impaired systems. In the Amazon, Dr. Neill uses a range of experimental techniques to investigate how deforestation and intensifying agriculture alter the flow of water and materials from the land into—and then within—streams and rivers. In Massachusetts, he studies how the choices we make along our coasts and in our own backyards affect biodiversity. He also works with local conservation organizations to design and assess improved methods of ecosystem protection and restoration.
Performance Period: 10/01/2023 - 09/30/2027
Institution: Woodwell Climate Research Center, Inc.
Award Number: 2317235
Abstract

This Smart and Connected Communities - Integrative Research Grant (SCC-IRG) Track 1 project will develop and evaluate smart, community-supported solutions for improving efficiency and equity in public microtransit systems. Poor transit service in small cities and towns around the US severely challenges the everyday lives of their many disadvantaged residents. Although public microtransit, which provides point-to-point service in small vehicles, has recently emerged as a promising solution, it remains ineffective at accommodating the rising demand without additional resources (vehicles and drivers). This project seeks to improve system performance equitably, through increased ridesharing and shifting flexible trips to off-peak periods. To this end, it will investigate techniques to motivate microtransit users to act prosocially (volunteer to shift one’s trip time to accommodate the high load of work trips, cooperate with the request to walk more to share a ride with a disabled user, reciprocate after learning that one previously benefited from another user) by evoking feelings of empathy towards other community members. Through a program dedicated to commuters, this project will also provide reliable and stress-free transportation to disadvantaged workers and students. These innovations will result in fewer unserved microtransit trip requests and cancelations and therefore lead to quality-of-life improvements, including reduced wage loss and missed medical appointments for riders. The prosocial acts motivated through this research will strengthen community membership, emotional safety, and sense of belonging. This project has the potential to benefit the thousands of small US communities that lack access to employment, health care, and other critical destinations.

The awarded research will create new paradigms for facilitating prosocial behavior in sociotechnical systems, moving away from traditional pricing mechanisms and incentives. Empathy-building messaging based on real-time user information and powered by artificial intelligence (AI), will enable and motivate prosocial behavior in microtransit at a low cognitive burden while accounting for individual needs and preferences. To increase ridesharing and operational efficiency, microtransit algorithms will be developed for the operation of a first-of-its-kind hybrid system that integrates a commuter program for work and school trips on a fixed, routine schedule with both on-demand and other trips scheduled in advance. This project will engage with both governmental and nongovernmental organizations to understand stakeholder needs and improve stakeholder acceptance of the technology and will advance our understanding of the contributions of community-based organizations and education in the success of smart and connected communities.

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.

Eleni Bardaka
Dr. Eleni Bardaka is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering (CCEE) at NCSU.
Performance Period: 10/01/2023 - 09/30/2026
Institution: North Carolina State University
Award Number: 2325720
Diaspora, Agriculture, & AI: Community-based Integration of Smart Technologies into Black Diasporic Agricultural Practices
Lead PI:
Sucheta Ghoshal
Co-Pi:
Abstract

Black diasporic farming communities are important sites of sustainable food production for millions of people in the US and worldwide. As tight-knit agricultural collectives, they generate food throughout cities, promote diasporic values of ecological well-being, resource conservation, and interdependence, and foster the possibility of social and political transformation for black, indigenous, racialized and marginalized groups. While advancements in food security, nutrition, and environmental health have the potential to feed nearly twice as many people per year, particularly among historically marginalized groups, black diasporic urban farming communities face several hurdles to efficiency that remain largely under-addressed. Activities such as risk management, soil health monitoring, and crop harvesting rely on repeated, time-consuming work that is challenging to support on limited budgets, resources, and labor. The field of artificial intelligence (AI) promises solutions to many of these challenges, making the case for an emerging market around AI-driven agricultural technologies. This project aims to (1) advance understanding of sociotechnical ecosystems involving AI to support diasporic urban farming; (2) collaboratively develop AI-based technologies that better integrates and sustains technological gains with diasporic knowledge, and (3) systematically assess the impact of AI-based farming technologies on diasporic communities and industrial partners.

In particular, our research seeks to advance the field of smart agriculture for diasporic urban farming communities along three urgent axes: (1) Labor: Addressing labor needs, decreasing bias within weeding, and ensuring access to affordable services for farmers who need them; (2) Ecosystem: Advancing care for a farm’s ecological conditions by supporting synergistic relationships with the land and surrounding organisms, training novice farmers, and monitoring greenhouse conditions; (3) Health: Innovating mechanisms for healthy soil conditions, reducing toxicity, and increasing the quality and quantity of nutrients. This work unfolds across three phases. Phase 1 begins with an ethnographic case study involving participant observation of urban agricultural practices and semi-structured interviews with partner organizations and identified stakeholders. Phase 2 complements this empirical work with an evaluation and design study that identifies sociotechnical solutions for agricultural decision-making informed by black diasporic needs. Phase 3 involves technical implementation that mindfully integrates black diasporic knowledge with AI-based technologies towards a smart and connected diasporic farming infrastructure. This work relies on our interdisciplinary team’s close collaboration with three farming organizations, three industry partners, and sustained partnerships across wider diasporic farming networks, and oversight from experts in AI, HCI, critical geography, urban studies, and community-based inquiry.

This project is partially funded by the Advancing Informal STEM Learning (AISL) program, which is committed to funding research and practice with continued focus on investigating a range of informal STEM learning (ISL) experiences and environments that make lifelong learning a reality.

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.

Sucheta Ghoshal
Sucheta Ghoshal is an Assistant Professor at the Department of Human Centered Design & Engineering at the University of Washington. Sucheta has been embedded in grassroots social movements in the United States—both as a researcher and as an activist—for the last five years. Her research focuses on studying how grassroots social movements in the United States relate to information and communication technologies (ICTs). Broadly, her work strives to critically question ICTs in their totality for the role they continue to play in the larger systems of oppression—namely, systemic racism, class, caste, and gendered oppressions. Additionally, she is interested in uncovering ways in which we can form public means of consciousness, resistance, and accountability against technology-mediated systemic oppression. Sucheta was formerly a software engineer at the Wikimedia Foundation where she built several tools for Wikipedia and worked on building a community of Wikipedians in India. She has been a community organizer working in various capacities globally for over a decade.
Performance Period: 12/15/2023 - 11/30/2026
Institution: University of Washington
Award Number: 2310515
Behavior-driven Building Safety and Emergency Management for Campus Communities
Lead PI:
Burcin Becerik-Gerber
Co-Pi:
Abstract

Campus communities are vulnerable to a wide range of emergencies such as active shooter incidents and fires, exposing students, teachers, and other members to significant risks. This Smart and Connected Communities Integrative Research Grant (SCC-IRG) project aims to explore new ways in which human behaviors are systemically and robustly incorporated into building design and emergency protocols. Specifically, the focus of the project is to understand how people in different roles respond to building emergencies, both individually and collectively, with empirical data collected from human-subject experiments, behavioral theories, and insights from domain experts. Intelligent crowd simulations are developed to represent the goals, preferences, and actions of diverse groups of building occupants as well as their interactions with others and the environment. The research team works synergistically with a range of campus community stakeholders, including first responders, law enforcement and emergency management personnel, and building designers to parameterize, validate, and test the crowd simulation. Appropriate applications are identified to mitigate safety and health risks. Underrepresented and underprivileged students are recruited into the research activities. Furthermore, the team explores the generalizability of the methodology, datasets, and research findings to non-building emergencies and non-campus communities.

Current crowd simulations for examining building emergencies often rely on oversimplified assumptions about human behaviors, lack cross-examination in different emergency contexts, and overlook the varied needs of stakeholders. To address these gaps, this project considers two common yet distinctive building emergencies (i.e., active shooter incidents, and fires) that are common in campus communities, and holistically models the impact of personal, social, and environmental factors on human behaviors of building occupants. The team implements agent-based models and develops multi-agent crowd simulations that capture individual and collective behaviors based on a highly reconfigurable and adaptable decision-theoretic framework. The team also engages campus stakeholders as well as other community members in a series of workshops to co-create likely and representative emergency scenarios. By allowing for exploring the outcomes of such “what-if” scenarios, the crowd simulation software has the potential to serve as a valuable tool for building designers, facility operators, and emergency managers.

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.

Burcin Becerik-Gerber
Dr. Burcin Becerik-Gerber is a professor and Chair of Sonny Astani Department of Civil and Environmental Engineering, and founder and the Director of USC Center for Intelligent Environments (CENTIENTS). During the last 15 years, her research focused on advanced data acquisition, modeling, visualization for design, construction, and control of user-centered responsive and adaptive built environments. She pioneered a new field: Human-Building Interaction (HBI), which is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. She published her work in more than 150 peer-reviewed journal and conference papers. Her work has received support worth over $12 million from a variety of sources, including the NSF, DoE, DHS and DoT and corporate sponsors. In 2012, she was named by the MIT’s Technology Review as one of the top 35 technology innovators under the age of 35 (first civil engineering faculty to receive this recognition). She received the FIATECH Celebration of Engineering and Technology Innovation Award in 2018. The same year, she was awarded the Rutherford Visiting Fellowship by the Alan Turing Institute, UK’s national data science and AI institute. Between 2012-2019, she held the inaugural Stephen Schrank Early Career Chair. In 2020, she was appointed as a USC Viterbi Dean's Professor. In 2021, se was elected to the National Academy of Construction. Since 2021, she serves on the Board on Infrastructure and the Constructed Environment of the National Academies of Sciences, Engineering, and Medicine. She received mentoring and leadership recognitions such as the Mellon Mentoring Award (2017) and an Executive Leadership in Academic Technology, Engineering and Science (ELATES) Fellowship (2021), which speak to her commitment to education and leadership in academia. In 2022, she received an Emmy Award as a co-producer of the documentary, “Lives, Not Grades,” which told the story of a novel course, she co-designed and co-taught, that focused on engineering innovation for global challenges.
Performance Period: 08/15/2023 - 07/31/2026
Institution: University of Southern California
Award Number: 2318559
Smart Connected Oral Health Community: Using AI and Digital Technologies to Close the Gap in Oral Health Disparity
Lead PI:
Jiebo Luo
Co-Pi:
Abstract

Tooth decay is a pandemic disease that affects 35% of the global population, or 2.4 billion people. Dental caries (tooth decay) particularly impacts children and adults living in poverty, who have poor access to dental care. Current biomedical approaches to controlling dental caries have had limited success. This project is creating a smart, connected oral health community with improved access to care and greater oral health equity. The investigators aim to develop and test a community-based infrastructure that combines use of artificial intelligence (AI) technology, facilitated by home use of smartphones, with community engagement through interactive oral health community centers, mobile vans, and community health workers. The project has the potential to reform the oral health care delivery system, empower communities with digital tools, and overcome barriers to oral health equity. Beginning with a focus on families with young children, the model could also be adopted by other underserved populations, such as the elderly and refugees, who face similar challenges in accessing oral health care.

The project team is refining the underlying technology with AI-powered oral disease screening and management with cloud surveillance, and data collection facilitated by a dentistry smartphone app for at-home self-monitoring. The team is also investigating the social dimensions of the problem. It is establishing oral health community centers supported by community health workers who apply established methods of human motivation to reach and empower families in the community, and to teach and motivate them to use the new AI apps. The team will be assessing community outcomes of the project, with a focus on the use of technology tools for caries detection and treatment prioritization, and community engagement with services in oral health community centers. Outcomes will be measured by the perceived competence of providers and patients, and the technology's acceptance, usability, and effectiveness.

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.

Jiebo Luo
Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the University of Rochester. He joined the University of Rochester in 2011 after a prolific career of 15 years at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. He has authored over 600 technical papers and more than 90 U.S. patents. He has served as program co-chair of ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017, and general co-chair of ACM Multimedia 2019 and IEEE ICME 2024, as well as on the editorial boards of several IEEE Transactions journals and publications. He served as the Editor in Chief of the IEEE Transactions on Multimedia for a 3-year term (2020-2022). He is a Fellow of NAI, ACM, AAAI, IEEE, SPIE, and IAPR.
Performance Period: 06/15/2023 - 05/31/2026
Institution: University of Rochester
Award Number: 2238208
Equitable-Access Flood Modeling for Timely and Just Adaptation in the Near and Long Term
Lead PI:
Katharine Mach
Co-Pi:
Abstract

Climate change is intensifying flood risks, with profound socioeconomic consequences. Equitable flood adaptation is designed to offer greater and/or more lasting benefits to overburdened communities than past projects in a warming climate. In pursuit of this goal, this Smart and Connected Communities Integrative Research Grant (SCC-IRG) project develops and tests a new paradigm of flood adaptation marked by innovation in access to, and use of, an interactive, fast flood risk simulation tool. The project aims to produce new knowledge about the role and effectiveness of collaborative models in promoting social justice and environmental well-being. Widespread adoption has the potential to make future solutions to be more time-sensitive, equitable, and effective for different communities and hazard types.

Flooding dynamics are complex and uncertain, decision-making is limited by social, political, and institutional constraints, and participatory processes are very time-consuming. This project brings together experts in civil engineering, adaptation sciences, and regional planning to (a) overcome technical barriers in flood risk simulation that have been limiting collaborative exploration by communities, notably the ability to predict flood impacts at fine resolution and at regional scales for a wide range of scenarios, and (b) measure if and how a new sociotechnical framework can improve outcomes such as increasing participation of marginalized populations, shortening planning timelines, and more equitably distribution of benefits and resources across groups and neighborhoods over time. Linking a digital engagement platform to a fast-response flood simulation tool could represent a breakthrough innovation for more equitably responding to climate change. The framework, if successful, could be broadly applied at neighborhood to regional scales to aid in climate change adaptation.

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.

Katharine Mach
Katharine Mach is a Professor at the University of Miami Rosenstiel School of Marine, Atmospheric, & Earth Science and a faculty scholar at the UM Abess Center, focused on environmental science and policy. Her research assesses climate change risks and response options to address increased flooding, extreme heat, wildfire, and other hazards. Through innovative approaches to integrating evidence, she informs effective and equitable adaptations to the risks. Mach was the 2020 recipient of the Piers Sellers Prize for world leading contribution to solution-focused climate research. She previously was a Senior Research Scientist at Stanford University and Director of the Stanford Environment Assessment Facility. Before that from 2010 until 2015, she co-directed the scientific activities of Working Group II of the Intergovernmental Panel on Climate Change. This work on impacts, adaptation, and vulnerability culminated in the IPCC’s Fifth Assessment Report and its Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. The associated global scientific collaborations have supported diverse climate policies and actions, including the Paris Agreement. Mach is a chapter lead for the US Fifth National Climate Assessment and was a lead author for the IPCC Sixth Assessment Report. She serves as Co-Editor in Chief for Climate Risk Management, a member of the National Academies Climate Security Roundtable, an editorial board member for Oxford Open Climate Change and Environmental Research: Climate, and an advisory committee member for the Aspen Global Change Institute, the Stratospheric Controlled Perturbation Experiment, and Carbon180. Across all of her research projects, she engages in relevant policy processes, and she frequently discusses climate change risk and adaptation with the media, the private sector, nongovernmental organizations, and communities. Mach received her PhD from Stanford University and AB summa cum laude from Harvard College. At UM, Mach is the Chair of the Department of Environmental Science and Policy. She teaches Interdisciplinary Environmental Research: Introduction to the Why and the How (ECS 601/EVR 603) and the Science of Actionable Knowledge (EVR 511/611).
Performance Period: 09/01/2023 - 08/31/2026
Institution: University of Miami
Award Number: 2305476
Core Areas: Water Management
Partners
Design and Development of a Near Real-Time Community Crowdsourced Resilience Information System for Enhancing Community Resilience in the Face of Flooding and other Extreme Events
Lead PI:
Barnali Dixon
Co-Pi:
Abstract

Communities along the coast are increasingly vulnerable to coastal hazards such as flooding due to extreme weather events and sea level rise. In the US alone, 40% of the population lives in coastal cities and subjected to elevated risks of such hazards. The probability of a flooding event in these communities is also increasing with global warming. The proposed project will design a neighborhood-scale Community Resilience Information System (CRIS-HAZARD) by leveraging citizen science and community participation for enabling real-time data-driven decision-making to make communities more resilient to flooding. CRIS-HAZARD will support frequent bi-directional flow of information among communities, research scientists, and decision-makers. The objective is to develop a platform that facilitates the smart and connected city framework by engaging diverse communities to improve the lives of all citizens, especially those who are marginalized. The project is piloted in Pinellas County, Florida, in the Tampa Bay region on the Gulf Coast of west-central Florida. This region’s geography and low elevation make it especially vulnerable to climate change-induced extreme weather events like flooding.

Unlike previous attempts at integrating data and models to predict flooding events, the approach of CRIS-HAZARD is distinctive as this research pioneers the integration of user-supplied data (crowd-sourced and social media) with real-time flood prediction models and uncertainty analysis techniques, which is expected to advance our understanding of risk and resilience in coastal communities facing persistent flooding events. The initiative integrates the expertise of research institutions, government agencies (Office of Emergency Management or OEM), local stakeholders, and community engagement networks to enhance community-based planning and policy decisions, promoting community resilience. Furthermore, the project fosters customized resiliency planning at the neighborhood level engaging citizen scientists as partners. It aligns with the National Science Foundation's mission to provide transparent and accessible information on risks and vulnerability, contributing to the development of smart and resilient communities nationwide.

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.

Barnali Dixon
Dr. Barnali Dixon is a Professor at School of Geosciences and contribute to Geography and Environmental Science and Policy programs. Dr. Barnali Dixon is the Director of the Geospatial Analytics Lab (G-SAL) and also the Executive Director and PI for the Initiative on Coastal Adaptation and Resilience (iCAR) at USF. Her research focuses on the development and application of Environmental Decision Support Systems (EDSS) integrated with Geospatial Technologies and geocomputation for modeling and managing land-water interfaces in the context of extreme weather events and climate change (including sea level rise) to facilitate informed data-driven planning, adaptation and resilience efforts with a particular emphasis equitable resilience. She is interested in making the ‘smart city framework’ smarter by using a holistic approach that facilitates intentional inclusivity of direct and indirect engagement of citizens to develop customized solutions. Her recently completed project funded by AT&T included the development of an integrated Community Resiliency Information System (CRIS). Her current NSF-funded project is called ‘Design and Development of a Near Real-Time Community Crowdsourced Resilience Information System for Enhancing Community Resilience in the Face of Flooding and other Extreme Events’. She is particularly interested in the development of transdisciplinary and spatially explicit models using Artificial Intelligence tools (AI) such as fuzzy logic, Artificial Neural Networks, Support Vector Machine, Relevance Vector Machine, Random Forest and other machine learning algorithms and soft computing techniques. She is considered one of the leaders in the subfield of geocomputation, where geospatial technologies intersect with machine learning and soft computing tools. She is the recipient of the Fulbright Specialist Award.
Performance Period: 10/01/2023 - 09/30/2026
Institution: University of South Florida
Award Number: 2325631
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