Cities and communities in the U.S. and around the world are entering a new era of transformational change, in which their inhabitants and the surrounding built and natural environments are increasingly connected by smart technologies, leading to new opportunities for innovation, improved services, and enhanced quality of life. The Smart and Connected Communities (SCC) program supports strongly interdisciplinary, integrative research and research capacity-building activities that will improve understanding of smart and connected communities and lead to discoveries that enable sustainable change to enhance community functioning. This project is a Research Coordination Network (RCN) that focuses on achieving SCC for medium/small size, remote, and rural communities through a polycentric (multiple centers) integrated policy, design, and technology approach. The communities served by the RCN have higher barriers to information, resources, and services than larger urban communities. To reduce this gap, the PIs propose to develop need-based R&D pipelines to select solutions with the highest potential impacts to the communities. Instead of trying to connect under-connected communities to nearby large cities, this proposal aims to develop economic opportunities within the communities themselves. This topic aligns well with the vision of the SCC program, and the proposed RCN consists of a diverse group of researchers, communities, industry, government, and non-profit partners.

This award will support the development of an RCN within the Commonwealth of Virginia which will coordinate multiple partners in developing innovations utilizing smart and connected technologies. The goal of the research coordination network is to enable researchers and citizens to collaborate on research supporting enhanced quality of life for medium, small, and rural communities which frequently lack the communication and other infrastructure available in cities. The research coordination network will be led by the University of Virginia. There are 14 partner organizations including six research center partners in transportation, environment, architecture and urban planning, and engineering and technology; two State and Industry partners (Virginia Municipal League and Virginia Center for Innovative Technology); four community partners representing health services (UVA Center for Telemedicine), small and remote communities (Weldon Cooper Center), neighborhood communities (Charlottesville Neighborhood Development), and urban communities (Thriving Cities); and two national partners which support high speed networking (US-Ignite) and city-university hubs (MetroLab). Examples of research coordination include telemedicine services, transportation services, and user-centric and community-centric utilization and deployment of sensor technologies.

Urban communities are increasingly including Green Stormwater Infrastructure (GSI) in their watershed management plans to manage stormwater in cities. Stormwater programs are scientifically limited by a lack of knowledge of the longevity of GSI, how real-time adaptive control can improve performance, and lack of process for using collected data in new GSI designs and policy. Further, there is no scientifically robust method to consider social equity in GSI design and planning. To overcome these challenges, and achieve sustainable stormwater management, solutions must use other available technologies in new ways that are co-created with community members woven into the planning-design-implementation process. The project hypothesizes that this challenge can be met using smart systems that can: 1) create and expand opportunities for GSI, 2) improve the sustainable function of these systems, and 3) address community infrastructure needs and preferences to overcome issues of inequity.

The main objective of this planning proposal is to develop a roadmap to combat the community-stormwater challenge. This project will accomplish this by forming a task force, Community Science Work Group, of a cross-disciplinary team of researchers, government agencies, community and industry partners to create a roadmap to develop a set of computer technologies and tools to design smart, sustainable, community driven, equitable GSI systems for urban communities. This will be accomplished through continuously engaging with community stakeholders to incorporate preferences and technical and societal interactions (e.g., GSI co-benefits) at all levels. This project will establish channels to build and engage a project team for a future proposal to effectively use technology in urban environments to respond to the stated community needs. This current project will also explore if emerging computing technologies can help meet community-stormwater challenges through an iterative stakeholder engagement process, which would lead to new science in urban stormwater systems and a new avenue for application of computer technology. This project will broaden community understanding and engagement in GSI infrastructure, increase GSI ecosystem services and community resilience, and ultimately improve the urban environment and contribute to social equity. Through tightly intertwined cross-disciplinary research and outreach goals, this project provides a transformative benefit to society by providing a fair and open community driven platform to improve cities’ efforts to effectively address federal water quality and safety needs and establish new frontiers for urban sustainability. This planning project will serve as a vital start to build a platform to alleviate the community-stormwater challenge.
 

Responding to risk events involves interactions among diverse stakeholders (e.g. government agencies, non-profit organizations, community residents). Such interactions are typically unbalanced and inefficiently organized, which leads to coordination failures and inefficient response. Community-based social networks offer a critical resource during crisis response, whose capacity has been significantly enhanced with the ubiquitous usage of social media and smart devices. The challenge is to enable innovative, community-based coordination mechanisms that allow sharing risk and information without undermining each other. The project offers a community-wide risk assessment and protective action decision-making framework that takes into account the risk sharing and trust building tradeoffs in online (i.e. internet, social media) and offline (i.e. face-to-face) social networks. The proposed project considers underserved tribal communities in Oklahoma as a testbed for building conceptual and operational frameworks to demonstrate how such networks can facilitate more effective and scalable risk sharing to provide complementary pathways to resilience. This SCC-PG will help public authorities and non-profit organizations communicate with their target audience in a more effective and efficient way during one or more hazard events.

Accurate and actionable messages about hazardous events are key to saving lives, minimizing adverse impacts in at-risk communities, and creating more proactive and resilient communities. Community-based social networks offer a critical resource during crisis response, however the challenge is to enable innovative, community-based coordination mechanisms that would allow more proactive sharing of risk information through online (i.e. internet, social media) and offline (i.e. face-to-face) social networks. The primary goal of this Smart and Connected Communities (SCC) Panning Grant (SCC-PG) is to lay a foundation for a risk sharing network that will fundamentally advance the understanding of how a system of diverse actors at different levels of social system, embedded with smart and social media tools, collectively generate community resilience. The project’s interdisciplinary team will organize a series of activities to develop the foundation for a risk sharing network for the underserved tribal communities in Oklahoma that will produce a set of actionable insights and operations for promoting resilience. Specific tasks include: (1) Building a comprehensive understanding of the interactive features of community-based risk and information sharing processes with key stakeholders’ engagement; and (2) Advancing coherent theoretical and computational insights from several strands of scientific literature to develop effective coordination mechanisms among diverse actors in a social system. The project will generate transformative knowledge that will be instrumental in responding to future crisis events.

Extreme heat is deadly and disproportionately affects the elderly and residents of low-income neighborhoods. Extreme heat and humidity events will increase in coming years. Lack of air-conditioning and the urban heat island effect create dangerous indoor conditions. Up to 60% of older, poorly built homes in low-income areas lack AC. Combining residents’ behavior and building characteristics in machine learning (SciML) and agent-based models (ABM) will identify when residents are exposed to overheating risks in their home and connect them with resources to mitigate dangerous conditions. Leveraging collaboration between Iowa State University, the City of Des Moines, Polk County, community organizations, and collaborators at UNI and UTA, this team will use empirical data and participatory processes to develop novel hybrid social- and physics-aware models to increase predictability of extreme heat-related indoor conditions. A community-focused microclimate-informed indoor heat emergency alert (CommHEAT) system will personalize community heat-related emergency management capacity. This will provide societal benefits via improved prediction of indoor conditions in homes for adaptation to extreme heat. The social-biophysical models are broadly transferable to facilitate climate adaptation and improve public health associated with extreme heat. Project completion will provide communities with a framework for microclimate-informed heat alerts in real time. Outcomes will support local heat health action plans to reduce emergency calls, heat illness-related hospitalizations, and mortality from indoor heat exposure.

This research will address knowledge gaps through social and thermal-physical models of adaptation/response to extreme heat indoors. Data will integrate social/behavioral responses to extreme heat with physics-constrained models and develop response strategies across spatial and temporal scales. Intellectual merit includes development of novel data-driven modeling combining validated ABM and physics-constrained SciML models of building features with human behavior within/near buildings. This project contributes to three scientific advances: (1) describing human behavior during extreme events based on human choices; (2) creating localized physics-constrained indoor condition models with real-time parameters; and (3) integrating models in an app using a transferable framework to predict conditions over time. Through participatory design with vulnerable residents in the study area the ABM will be an empirically valid model of the community, enabling prediction of responses to different app-enabled heat mitigation strategies under climate scenarios in a heat alert system that will improve health outcomes. Transferable SciML will account for underlying physics that tie local conditions to building thermal properties. The CommHEAT app will visualize alert mitigation scenarios to guide decision-making at multiple scales for adaptation.

The exponential increase in extreme events over the last decade compels new methods of managing risk in communities exposed to recurring natural hazards. This project advances the National Science Foundation’s goal “Growing Convergence Research” to enable smart and connected communities by initiating and expanding collective learning capacity through integrating digital twin technologies and social games. This project proposes to engage decision makers across sectors and scales of jurisdiction in managing risk by reallocating attention, time, resources and overcoming barriers to act collectively as hazardscapes change. This project will use the threat of wildfire across two communities in northern California as community engagement study sites. Working with thirteen community partners, the project will develop an innovative sociotechnical digital twin of the San Francisco Bay Area that integrates virtual models of physical infrastructure systems, social/commercial networks, and insurance mechanisms that distribute risk over space and time. Serious games will be designed to activate learning processes inherent in play to engage community’s awareness and commitment to collective action.

This project will use a complex systems approach to hazard reduction across multiple scales of risk by developing a new generation of socio-technical digital twin that integrates models of physical infrastructure systems and virtual networks of communication with social games to engage community stakeholders’ awareness and commitment to collective action. Using a conceptual framework of complex adaptive systems, this project will investigate whether community learning processes that focus on cognition and action will mitigate wildfire risk in the short-term and lead to sustainable adaptation to recurring risk conditions in the long-term. This inquiry advances risk management theory by testing a prototype sociotechnical framework for developing shared knowledge to support decision making by multiple actors at different scales to reduce hazard risk. The sociotechnical digital twin provides a macro view of risk at the regional scale, as well as detailed views of interactions at the micro scale, essential to manage operations. Translating risk information into formats that are easily understood by different groups and embedding learning processes in gaming scenarios to advance risk reduction is transformative. A major goal is to shift the perspective from reaction to extreme events after they occur to anticipation of risk and mitigation of potential losses before hazards occur. Using serious games, a process of iterative learning for diverse community actors increases the level of shared cognition of risk and commitment to action. The project will engage under-represented minorities in affected regions and support decision-makers in vulnerable communities.

Clean and safe water is a basic necessity for a community to survive and thrive. However, millions of people are exposed to unsafe levels of drinking water contaminants including toxic and persistent heavy metals and ubiquitous “forever chemicals” such as per– and polyfluroalkyl substances (PFAS). Despite strict regulations, and well-established laboratory methods for detecting these widespread and persistent contaminants, these pollutants sometimes go undetected because of infrequent sampling and testing. In this project engineers, computer scientists, and social scientists from the University of Massachusetts Lowell will work closely with community stakeholders (residents, neighborhood groups, nonprofits, drinking water utilities, and regulators) to pilot a smart Internet of Things (IoT) enabled water-quality monitoring and alert system in several socio-economically diverse communities of Massachusetts. Given that drinking water contamination and exposure occurs disproportionately in economically and racially disadvantaged communities with older infrastructure, the proposed technology will empower underprivileged groups to use the data to advocate for remediation efforts. The transdisciplinary sociotechnical systems approach to implement a smart community engaged water-quality monitoring and alert system will be a new paradigm for addressing similar large scale societal and infrastructural problems.

In this SCC project, the investigators will (1) deploy citizen-scientist-operated electrochemical electronic tongue (E-Tongue) devices for rapid, onsite, water quality testing of contaminants such as lead and arsenic, (2) co-design with community stakeholders a user-friendly app and cloud-computing platform for data analysis, and (3) foster shared learning and collaboration among community stakeholders to build social cohesion and trust in water testing technologies and the local authorities. Furthermore, this work will develop spatiotemporal machine learning algorithms and a cloud-computing platform that will take the responses from the individual E-Tongue devices and produce predictions of contaminant type, concentration, probable source, and extent of the contamination. This information will be used to quickly notify the public health authorities for intervention and alert affected residents to take appropriate actions. Through the design, development, and testing of a smart sensing and cloud-computing system, the proposed transformative research will contribute to the fundamental understanding and practical design of novel spatiotemporal analytics, mobile computing, and machine learning techniques for real-time water contaminant threat detection and early warning systems. The research will also advance our knowledge and understanding of the technologies, training, and relationships required to facilitate a sustainable, scalable sensor platform for water quality testing and increase awareness and social trust in water testing technologies and local authorities.
 

Communities across the world are experiencing a challenging paradox: accelerating development in the context of climate change. Often, the impacts are concentrated in disadvantaged communities, requiring new approaches to pursuing local and equitable solutions to climate resilience. Common SENSES will demonstrate such an approach by integrating cutting-edge science with community priorities in conjunction with a capital redevelopment of Blue Hill Ave. in Boston, MA, a long-neglected thoroughfare running through the heart of the city’s historically Black communities. Networks of sensors installed throughout Blue Hill Ave.’s neighborhoods will measure the threat of environmental hazards, including extreme heat and rainwater flooding, from street to street. These data will be explored collaboratively with community stakeholders in workshops that will culminate in proposals for the placement of green infrastructure (e.g., rain gardens, green roofs) optimized to mitigate hazards in the neighborhood following the redevelopment. The project is a community-city-university partnership between the Dudley Street Neighborhood Initiative (DSNI) and Project RIGHT, which serve communities along the Blue Hill Ave. corridor; the City of Boston’s Mayor’s Office of New Urban Mechanics (MONUM); and an interdisciplinary team affiliated with Northeastern University’s Boston Area Research Initiative (BARI).

Common SENSES will make four major advances. (1) It will generate new techniques for modeling sensor data to quantify disparities in hazards from block to block within communities, or microspatial inequities. (2) It will develop new practices and tools for participatory modeling, or the process of generating solutions by placing complex data in the hands of community stakeholders. (3) It will evaluate the impacts that green infrastructure can have on mitigating microspatial inequities in communities. (4) It will demonstrate how sensor networks can be best integrated with community needs and perspectives to have true public impact, something that this emergent technology often lacks. The project will have extensive benefits for the Blue Hill Ave. corridor and will demonstrate a model for similar projects locally and globally. The team will work with the City of Boston to replicate the approach in other projects throughout the greater Boston region. This project will also publish the Common SENSES playbook, a non-academic publication summarizing the insights, tools, and practices developed throughout the project to enable other communities to incorporate in their own pursuit of local solutions to climate resilience.

This project seeks to reduce loneliness in older adults who reside in long term care (LTC) communities through new augmented reality (AR) technology. Loneliness is a serious condition that is related to increases in heart disease, depression, suicide, mental and physical decline, and reduced quality of life and death. Two out of five older adults in the U.S. report being lonely. Even more alarming, three out of four LTC older adults experience loneliness. The COVID-19 pandemic, with its accompanying safety protocols, has intensified loneliness across the LTCs. The project will discover how augmented reality can reduce loneliness in LTC older adults by linking them with family members who reside elsewhere. This project will allow older adults and family members to see each other’s 3-dimensional realistic images, eat meals together, and interact with one another in various activities, such as playing cards. Investigators of this project are experts in engineering, computer science, gerontology, nursing, medicine and social health science. Working with older adults and family members in the design and testing of the AR technology, the team will compare AR to 2D interactive communication technologies, such as Zoom or Facetime. Initial understanding of the feasibility and acceptability of this enhanced AR technology among older adults, families and LTC staff will guide future studies targeting loneliness, ultimately improving quality of life for older adults. The community focus for this project will be older adults residing in LTC communities in Middle Tennessee with the potential to scaling the solution across the nation.

The project will fundamentally advance the scientific and the technological methodologies of collaborative Augmented Reality to enhance social presence and thus social connectedness, to create realistic and socially appropriate interactions. It will make several fundamental contributions in both technology and social science during the course of this research: 1) create a novel multi-objective optimization based framework that minimizes positional errors of the hand of the avatar while preserving its nonverbal behavior with respect to the human it represents; such an ability will allow shared activities (e.g., drinking tea together) with appropriate social nonverbal behavior (e.g., gaze and postures), a critical component of communication; 2) create a new methodology of a user’s motions onto its avatar to generate naturalistic, socially appropriate motion that respects dissimilarities between the user’s and its avatar’s environments (e.g., differences in room geometries) through novel motion mapping and optimization that ensures natural walking patterns; 3) develop a greater understanding of the feasibility, acceptability and social presence in the use of varying collaborative AR activities and environments for older adults with different levels of cognitive impairment and their family members; 4) develop a greater understanding of the impact of collaborative AR on loneliness based on level of cognitive impairment; 5) gain a greater understanding of the logistics and deployment of this technology in LTCs and family homes to inform scalability; and 6) create activity design guidelines for reduction of loneliness in older adults. The research will be conducted through participatory design using key stakeholders (e.g., older adults, activity directors, LTC management) and evaluated using a two-arm experimental design comparing collaborative AR to current state-of-the-art 2D interactive communication technologies.