High concentrations of energy use from fossil fuels can lead to poor air quality, resulting in adverse health effects as well as economic consequences. A prime example is found where large numbers of idling vehicles congregate (e.g., schools and hospital drop-off/pick-up zones), leading to microclimates of unhealthy air. Workers, such as valet parking attendants, can spend their entire workday in these microenvironments, and children passing through these zones can experience up to 60% higher levels of pollution than adults, because of their height. These vehicle-caused, poor-air-quality microclimates offer a compelling opportunity for communities to engage with emerging technologies to take ownership of their air and the behaviors that impact its quality. This project sociotechnical approach, called SmartAir, will synergistically integrate dynamic air-quality information with social-norm feedback to positively influence decisions that affect the well-being of vulnerable individuals working in or passing through polluted microenvironments. The feedback approach for decreasing idling mirrors the feedback provided by digital speed displays, which has been shown to positively influence driver behavior (reduced speeding) and thus reduce health-impacts of that behavior (reduced traffic accidents). The proposed pilot demonstrations will take place in Northern Utah, a region that periodically experiences the poorest air quality in the country. The project SmartAir employs a comprehensive community engagement approach — from the development of the sensing and display technologies to cocreation of culturally sensitive messaging, cooperatively conducted pilot studies, and efficacy evaluation.

SmartAir will produce novel technological and behavioral-science developments. First, this project will develop wearable, calibrated, low-cost air quality sensing nodes that will support members of smart and connected communities to minimize pollution exposure. Second, this project will enable the rapid integration of sensor measurements with local meteorological information and data-screening algorithms to dynamically provide feedback to individuals about idling behavior and to workers that seek to minimize pollution exposure. Third, the SmartAir system will be integrated into behavior-change experiments and the co-creation of community-crafted messaging to influence individual choices. Comprehensive involvement of the community partners will be critical to co-develop and pilot solutions to address poor air quality and ultimately ensure a highly scalable and sustainable system. The broader impacts of this work are multifold, including the following. SmartAir will serve as a framework for closing the loop between air quality measurements and individual decision making. It will also help drive institutional decisions that reduce worker pollutant exposure and improve worker performance, career longevity, and job satisfaction. Anonymized data will be made available to support numerous personal and community-driven needs, such as health-effects studies, anti-idling campaigns, school drop-off policies, and urban/traffic planning. Additionally, this project will have a substantial outreach effort that involves community members in message crafting, data collection, and interpretation.
 

This project is a Smart and Connected Communities award. The community is part of Evanston, Illinois and is composed of the lead partners described below:

- EvanSTEM which is a in-school/out of school time (OST) program to improve access and engagement for students in Evanston who have underperformed or been underrepresented in STEM.

- McGaw YMCA which consists of 12,000 families serving 20,000 individuals and supporting technology and makerspace activities (MetaMedia) in a safe community atmosphere.

- Office of Community Education Partnerships (OCEP) at Northwestern University which provides support for the university and community to collaborate on research, teaching, and service initiatives.

This partnership will develop a new approach to learning engagement through the STEAM (Science, Technology, Engineering, Arts, and Mathematics) interests of all young people in Evanston. This project is entitled Interests for All (I4All) and builds upon existing research results of the two Principal Investigators (PIs) and previous partnerships between the lead partners (EvanSTEM and MetaMedia had OCEP as a founding partner). I4All also brings together Evanston school districts, OST prividers, the city, and Evanston's Northwestern University as participants.

In particular the project builds on PI Pinkard's Cities of Learning project and co-PI Stevens' FUSE Studios project. Both of these projects have explicit goals to broaden participation in STEAM pursuits, a goal that is significantly advanced through I4All. In this project, I4All infrastructure will be evaluated using quantitative metrics that will tell the researchers whether and to what degree Evanston youth are finding and developing their STEAM interests and whether the I4All infrastructure supports a significantly more equitable distribution of opportunities to youth. The researchers will also conduct in depth qualitative case studies of youth interest development. These longitudinal studies will complement the quantitative metrics of participation and give measures that will be used in informing changes in I4All as part of the PIs Design Based Implementation Research approach. The artifacts produced in I4All include FUSE studio projects, software infrastructure to guide the students through OST and in-school activities and to provide to the students actionable information as to logistics for participation in I4All activities, and data that will be available to all stakeholders to evaluate the effectiveness of I4All. Additionally, this research has the potential to provide for scaling this model to different communities, leveraging the OST network in one community to begin to offer professional development more widely throughout the school districts and as an exemplar for other districts. These research results could also affect strategies and policies created by local school officials and community organizations regarding how to work together to create local learning environments to create an ecosystem where formal and informal learning spaces support and reinforce STEAM knowledge.
 

This Smart and Connected Community (SCC) project will partner with two rural communities to develop STEMports, an innovative Science, Technology, Engineering and Mathematics (STEM) learning game for workforce development. The game's activities will take players on localized Augmented Reality (AR) missions to both engage in STEM learning challenges and discover emerging STEM careers in their community, specifically highlighting innovations in the fields of sustainable agriculture and aquaculture, forest products, and renewable energy. Community Advisory Teams (CATs) and co-design teams, including youth, representatives from the targeted emerging STEM economies, and decision-makers will partner with project staff to co-design STEMports that reflect the interests, cultural contexts, and envisioned STEM industries of the future for each community.

The project will: (a) design and pilot an AR game for community STEM workforce development; (b) develop and adapt a community engagement process that optimizes community networking for co-designing the gaming application and online community; and (c) advance a scalable process for wider applications of STEMports. This project is a collaboration between the Maine Mathematics and Science Alliance and the Field Day Lab at the University of Wisconsin-Madison to both build and research the co-designing of a SCC based within an AR environment. The project will contribute knowledge to the informal STEM learning, community development, and education technology fields in four major ways:

Deepening the understanding of how innovative technological tools support rural community STEM knowledge building as well as STEM identity and workforce interest.

Identifying design principles for co-designing the STEMports community related to the technological design process.

Developing social network approaches and analytics to better understand the social dimensions and community connections fostered by the STEMport community.

Understanding how participants' online and offline interactions with individuals and experiences builds networks and knowledge within a SCC.

With the scaling of use by an ever-growing community of players, STEMports will provide a new AR-based genre of public participation in STEM and collective decision making. The research findings will add to the emerging literature on community-wide education, innovative education technologies, informal STEM learning (especially place-based learning and STEM ecosystems), and participatory design research.

Project website is: mmsa.org/stemports

Smart services are deeply embedded in modern cities aiming to enhance various aspects of citizens' lives, including safety, wellness, and quality of life. Examples include intelligent traffic control and air quality control. Given these services, monitoring a city's safety and performance collectively is crucial, yet also challenging due to many potential conflicts among the number increasing of services deployed. Researchers have accumulated abundant knowledge on how to design these services independently. However, underlying expected or unexpected couplings among services due to complex interactions of social and physical activities are under-explored, which leads to potential service conflicts. Developing approaches of reducing conflicts is essential for ensuring social inclusion and equity of city services because when conflicts occur, their impacts are likely to be concentrated in some sub-communities (e.g., specific geographic locations, specific user groups like patients with respiratory illness, etc.) meaning that some citizens will experience lower quality services than others due to the diversity. Put differently, service conflicts contribute to a digital divide in service provision.

The key intellectual merit of the proposed project is the development of a socially aware conflict management theory and its deployment for smart cities, consisting of 5 sequential components as follows. (1) a novel, template-based requirements specification component/tool that integrates social and technical requirements to formally define a conflict; (2) a social diversity aware detection approach that utilizes machine learning and conflict correlations to detect conflicts in practice; (3) a multi-objective yet equity-centric resolution method that accounts for socially acceptable trade-offs, behavioral models, and control theory to resolve existing conflicts; (4) a participant-based conflict prevention solution that employs Game Theory and Reinforcement Learning in a scalable, decentralize fashion to prevent future conflicts; (5) a social intervention approach based on education outreach and professional training to disseminate the proposed technology to empower the community. The real-world implementation of this theory by working with the city partners in Newark NJ will show its effectiveness and broader impacts on a diverse set of stakeholders of conflict management from city operators, to service providers, to average citizens.

The goal of this project is to further the ability of cities and communities to deploy technology that saves lives through safer transportation systems. The approach is to create open source analytics solutions to enable novel transportation applications that utilize data from low-cost video sensors. Video data are processed using edge computing (inexpensive computing hardware that performs analysis without storing significant amounts of data) in order to reduce the amount of data stored. Social dimensions of the research project emerge from the deep research partnership between the City and the University, with the goal to provide replicable and near-term social impacts. The project aligns with the Vision Zero concept to reduce traffic fatalities, with programs that are based on education, enforcement and design. By understanding the risk profile of an intersection through automated detection of near miss events, communities will be able to proactively design and alter streets and intersections to be safer.

The goal of designing a smart city, when addressing the technical challenges at the intersection, street and system levels, has several research components. (i) Development of new algorithms for multi-target tracking: The problems of occlusion, temporal assignment of features to objects and target motion will be jointly formulated. (ii) Integrated optimization and simulation for signal control: We formulate the problem of estimating signal control parameters (offsets, phasing etc.) in a network as one of global optimization. (iii) Real-time reinforcement learning is a natural choice when online machine learning meets real world feedback from the City. Our ability to obtain and analyze continuous-time data at the network level will provide insights on how conflict points and patterns can change through the network. This is expected to impact decisions in traffic management, smart city planning and 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.

The National Science Foundation supports a wide variety of valuable technical and social civic innovation through its CIVIC Innovation Challenge and Smart and Connected Communities. While many of these
projects have significant and long-lasting impact in multiple smart and connected communities, others fail to make the translation into sustainable and scalable community best practices. This EAGER will employ a multi-stage process to identify the combinations of factors which underlie successful translation as well as those factors which can be barriers. This EAGER will also explore specific steps, interventions, and resources which can propel more projects to sustainable, scalable, and long-term success. An important part of this research is connecting individual projects with what may be appropriate ideas, approaches,
and resources, and documenting the outcomes. We may discover that some issues we identify really aren’t that important, aren’t attractive to the project teams, or don’t have the intended impact. However,
US Ignite is starting with a strong track record of coaching successful technological and social adoption of smart and connected community practices across more than fifty communities over the past decade.
During the EAGER process, US Ignite will also work closely with the existing Smart and Connected Communities Virtual Organization, the CIVIC Innovation Challenge team, and the NSF S&CC team. The
results of this EAGER will be documented publicly and made available to the wider set of academic partners, community partners, industry partners (including startups), government partners, and the press.
US Ignite will work with the NSF and its Smart and Connected Community and CIVIC Innovation Challenge research projects to pilot pathways to deploy these technology and policy projects into larger-
scale and multi-city sustainable deployments. US Ignite will analyze existing NSF-funded research projects in S&CC and CIVIC to determine suitability for sustainable and successful deployment in multiple communities. Deployment may be accomplished through a number of pathways such as spinning out the technology to a startup (with or without the original investigators), making the technology and/or
data available as open source for others to use, protecting the intellectual property so that it’s valuable to a committed industry partner or industry alliance, through implementation by governments or nonprofits, or other pathways. Some of the pathways will leverage partners already providing these services (e.g., I-CORPS, university tech transfer organizations, and One Million Cups). Ability to positively impact
equity, inclusiveness of diverse populations, community engagement, and civic trust will be important components.

Texas coastal communities have historically been exposed to environmental threats from natural and industrial sources. In Ingleside on the Bay (IOB), a small, rural community situated along the shoreline of Corpus Christi Bay, tropical storms and high rates of relative sea-level rise cause extreme and nuisance flooding, while industrial expansion is placing stress on the community’s way of life and the natural resources upon which it relies. Such communities lack the comprehensive data needed to advocate for and make informed decisions about risk reduction strategies to mitigate the impacts of industrial growth and climate change. This proposal engages with the nonprofit Ingleside on the Bay Coastal Watch Association (IOBCWA), community members, and governmental representatives to assess the role of distributed, real-time sensor technology in improving IOB’s capacity to respond to dynamic environmental conditions that affect the quality of its air, water, and land resources. It also examines how emerging community-based nonprofits like IOBCWA engage with diverse organizations in response to new threats and how they can utilize environmental sensing data in planning and advocacy efforts.

This project will leverage interdisciplinary, sociotechnical methods to (1) assess the current structure of communication and information-sharing networks related to environmental threats and mitigation planning in IOB; (2) activate academic-civic partnerships to deploy environmental monitoring sensors to generate a pilot smart grid for comprehensive and timely data collection; (3) develop a preliminary online data visualization dashboard that makes sensor data available in real-time to the community; and (4) assess how the data and dashboard can be utilized by residents and nonprofit organizations to inform sustainable planning and development strategies that address industrial permitting challenges and safeguard community and environmental well-being. To achieve the technical objectives, this project will develop and deploy a pilot sensing network for real-time environmental monitoring, design an online dashboard and data analysis framework to display the collected data in real-time, and beta test the dashboard among a diverse group of residents, community leaders, and local stakeholders. To achieve the social science objectives, this project will apply grounded theory to characterize the evolving role of community-based nonprofits in networking, civic engagement, and policymaking efforts in IOB and identify data needs that can be addressed by leveraging sensor technology to provide a scientific basis for decision-making. Community workshops will provide opportunities to refine the study needs and objectives, obtain feedback on the sensor network and dashboard, and co-develop a vision for future integrative research efforts.

Detained youth are a population that experience disparities in educational opportunities and in particular, have systemically fewer rich opportunities for STEM learning. Access to educational resources and STEM learning for detained youth are critical to position them to have marketable employment skills and potentially contribute to the STEM workforce of the future. Taking lessons from the conditions and challenges posed by the COVID-19 pandemic, this project seeks to develop and deliver a Personal Learning Environment for Youth, an educational ecosystem that is accessible to a wide range of detained youth learners and provides individual tailoring based on youth interactions with the system. To develop the system, the project takes input from a multidisciplinary juvenile justice community collaborative working group that examines the existing educational infrastructure, determines challenges and affordances, and provides input into the design and delivery of the personalized learning system. The outcome of this research will be a framework for facilitating STEM learning for detained youth using smart and connected technologies.

The project takes place in the context of the Norfolk Juvenile Detention Center (NJDC). The work pursues a set of research questions that seek to identify the barriers and factors impacting accessibility to STEM learning and educational services, how the pandemic conditions changed those challenges, and anticipates the predicted challenges to delivering a personalized learning STEM education ecosystem. Stakeholders, including the center's academic staff, management, the public school system, and personnel related to juvenile justice, form a focus group engaged in conversation about the current educational ecosystem and the design features that would support stronger STEM learning for a personalized system. Participant responses will be distilled into design principles using grounded theory that will guide the design and development of the personalized learning system. The primary outcome of the research will be a case study that describes the framework for the personalized learning system and the design principles on which it rests.

Lack of participation in the digital economy is an impediment to societal well-being and production which asymmetrically affects rural communities. The literature indicates that technological availability (e.g., broadband) is only a part of the problem: rural communities are not as active as their urban counterparts in technology adoption. The adoption problem covers the extent to which rural communities have the financial resources and awareness, skills, and aspirations (or collectively, the literacy) to seize the productivity opportunities afforded by smart and connected technologies (SCT). This planning project will conduct a pilot study to determine how improved technology literacy can impact the rural adoption of SCT for building productivity to economically and socially revitalize rural communities. Specifically, this project will explore the development of a novel educational tool for technology literacy called Productivity Enhancing Technology Experience-Kits (Pete-Kits). Pete-Kits will be combinations of low-cost devices such as microprocessors and sensors that make use of communications technologies like WiFi, Bluetooth, and Radio Frequency ID (RFID), and which can be combined with cloud connectivity to support high school students as they develop entrepreneurial SCT projects within their rural communities. These kits will be developed with input from high school students and community members. High school teachers and students will receive training on how to use the kits, and students will then be invited to develop their own SCT entrepreneurial projects which will be judged in a final community competition event.

The research will examine: (1) how rural awareness of SCT is influenced by hands-on experiences with Pete-Kits, (2) to what degree are the basic skills for using SCT increased through interactions with Pete-Kit, and (3) the effects are of Pete-Kit based training and competition on rural participants’ productivity, and their aspirations for entrepreneurship, remote work, and quality of life within their community. These questions will largely be addressed via survey instruments, administered both before and after the intervention, to both participants and to attendees of community events. Community forums and workshops will also be held to review the strengths and weaknesses of the Pete-Kit program, and to develop relationships with other communities and tribal nations for future scalability. This planning project aligns well with the Smart & Connected Communities program’s goal to accelerate the creation of the scientific and engineering foundations that will enable smart and connected communities to bring about new levels of economic opportunity and growth. This project is also receiving funding from the ITEST program, which has priorities for (1) increasing awareness of STEM & ICT occupations, (2) motivating students to pursue educational pathways to those occupations, and (3) developing disciplinary content knowledge and skills necessary for entering those occupations.

The breadth of artificial intelligence (AI) applications has grown significantly, particularly over the last decade, increasing productivity and efficiency across numerous sectors. Cities have become the primary sites of data collection and algorithm deployment, but the professional field of urban planning lacks a comprehensive evaluation of how AI can/should be used to improve analytical processes. Urban planning anticipates and guides the future physical and social conditions of communities to improve quality of life – all with a heavy reliance on increasingly large and varied datasets, which suggests the untapped potential of AI if the field were to develop robust frameworks for ethical deployment. This project examines and seeks to address the tension between improving the efficiency of public service provision and enhancing redistributive and procedural equity within urban decision-making. As AI’s role in society grows, so do the concerns that it may reproduce racial bias, deepen “digital divides,” infringe on privacy, and do little to address the “wicked problems” at the heart of complex social issues. In addition, it may shed light on broader impacts of automation in urban life, such as workforce displacement, lifestyle changes, and future developments in public service professions.

This project is a partnership between Virginia Tech, the American Planning Association (APA), and Arlington County, Virginia’s Departments of Community Planning, Housing and Development (DCPHD Planning Division) and Technology Services (DTS). As part of this planning grant, the partnership will survey members of the APA and conduct feasibility analysis workshops and focus group sessions with DCPHD. The objective is to assess a broad range of tasks performed by County planners and determine which of these have the highest likelihood of being assisted and improved by AI technologies. This includes county-level responsibilities for comprehensive planning, land use, infrastructure, environment, housing, parks, and transportation. This project expects that each of these areas has the potential for more advanced data and analytical capabilities. The approach partners researchers, planning professionals, and community members will focus on the explainability and transparency of AI-based planning activities. This relates to the equitable deployment of AI methods and will also address concerns about trust in the use of data and analytical processes.