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.

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.

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.

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.

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.

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.

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.

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.

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.