Smart city platforms–encompassing mobile apps, cameras, sensors, algorithms, and predictive analytics—generate troves of data on residents. Research suggests that excessive surveillance reinforces a sense of insecurity and leads residents to fear civil liberties violations, particularly among communities of color. Our digital rights platform will empower community members by granting them agency over how the City collects, uses and stores their personal data. The platform will be designed collaboratively with hundreds of Long Beach residents participating in a civic user testbed and other qualitative data collection. The platform will feature text and open-source iconography that visually conveys how the City of Long Beach uses specific technologies, what data these technologies collect and how the City utilizes that data. We plan to strategically deploy signage across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, mobile payment kiosks, a 311 app. The platform will include a QR code or hyperlink that take users to an online dashboard where they may learn additional details, update data collection preferences, and share comments and concerns with local officials—giving residents a clear understanding of how local government collects, analyzes, shares, and retains their personal data. This digital rights platform will feature text and the open-source iconography that visually conveys how the City of Long Beach uses specific smart technologies, what data these technologies collect and how the City utilizes that data. The platform considers the technical, legal, ethical, and spatial aspects of smart technologies. Grounded in frameworks of trust and contextual integrity, the project is focused on the City’s vision to use data in ethical ways that avoid reinforcing existing racial biases and discriminatory decision-making. Specifically, we plan to strategically deploy signage across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, mobile payment kiosks, a 311 app. The platform will include a QR code or hyperlink that take users to an online dashboard where they may learn additional details, update data collection preferences, and share comments and concerns with local officials. We plan to work with smart city technology developers to create a software solution that will, ultimately, enable residents to opt out of data collection. The project will inform novel accountability strategies meant to ensure that wildly disparate smart city technologies—each employed for a distinct purpose—respect residents’ data privacy and avoid discriminatory impacts.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 NSF Smart and Connected Communities project will employ a novel and multidisciplinary approach informed by community participation to detect, map, and analyze “near-miss” events that occur when a collision between a vulnerable road user, such as a bicyclist or pedestrian, and an automobile is narrowly avoided. Rising injury and fatality rates in the United States for vulnerable road users is an area of societal concern, and contribute to public hesitancy to walk or bicycle more. These trends challenge ongoing efforts nationwide that aim to both make roads safer for all and reduce transportation sector emissions through a modal shift to increased walking, bicycling, and transit use. To date, data-driven solutions to address issues related to vulnerable road user safety often rely on official crash data, but these data cannot alone comprehensively represent the safety experiences of vulnerable road users. The ability to more broadly record near-miss events, and how their frequency and locations compare to officially reported crash data, is essential to informing safety-oriented transportation planning strategies. To address this topic, this project will integrate approaches and technological innovations from geography, traffic engineering, and urban planning, in partnership with community collaborators in greater Reno and Sparks, Nevada.Recent advancement in classification techniques applied to data collected from Light Detection and Ranging, or LiDAR, sensors provides an ability to detect near-miss events involving vulnerable road users. This project will deploy a portable network of such sensors at locations throughout greater Reno and Sparks. Sensor locations will be informed by responses to a web-based survey distributed to those who frequently walk or bicycle in the community that will prompt them to identify specific locations of vulnerable road user safety concern. Data will be collected at these locations for one week. Emerging near-miss detection methods will be applied to the field-collected data, and frequency and type of near misses will be compared against official crash data. A community focus group will review near miss events detected by these sensors and provide feedback to improve event identification methods. A Geodesign workshop will produce a collaborative plan that will prioritize locations for future assessment of vulnerable road user safety, and identify potential countermeasures. These efforts will help guide ongoing efforts to integrate a sensor network that if effectively scaled, could improve the ability to detect near-miss events in real-time, which in turn can better inform planning efforts to improve road user safety.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.

Approximately 91% of the world population lives in environments that do not currently meet air quality standards. In the United States (U.S.), the Clean Air Act of 1970 has resulted in air pollution concentrations dropping below national standards, meaning that most communities in the U.S. have cleaner air. However, clean air is not realized across all communities, especially in communities of color, where air quality can differ significantly. Further, regulatory air quality sensors that are sparsely deployed may not accurately detect the quality of air that residents breathe in their communities. With the availability of low-cost sensors and advancement of low-cost single-board computers and microcontrollers, this research aims to provide residents with an ability to accurately understand their air quality through the deployment of an Internet of Things (IoT) air quality sensor. We will meet with residents that have been affected by both redlining and nearby pollution sources to better understand how air quality affects their daily lives and what air quality information is most beneficial to them. In addition, the team will closely collaborate with partner school(s) to create K-12 curriculum for students to learn how to create their own air quality sensor, deploy it at their school, and make the air quality readings publicly available.

In this research, we will combine the availability of low-cost particulate matter sensors with the accessibility of IoT compatible single-board computers and microcontrollers to enable publicly available fine-grained air quality information. To provide real-time access to the data, a prototype mobile application for both iOS and Android, along with a web dashboard, will be developed. To address common challenges of both power and connectivity, we will partner with PCs for People to deploy the sensors and provide connectivity through their existing infrastructure. An enclosure will be developed that ensures proper airflow, has low interference with wireless communication, and is modular to allow other sensing capabilities in the future. We will compare the findings from a test deployment of the sensors with regulatory sensors readings and share the results with the community and local officials. To ensure the sustainability of the project and provide an opportunity for it to expand, we will create an open-source Computer Science and Engineering curriculum in partnership with a local middle school and we will pilot a tech camp at our university.

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.