Irrigation plays an important role for agricultural sustainability in U.S. and around the globe. In particular, the agricultural economy in the state of Nebraska, the largest U.S. state in terms of irrigated area, highly relies on irrigation in the growing season. With insufficient precipitation that has never met the demand for crop growth, the rural areas in Nebraska faces a central challenge to best utilize limited underground water in the Ogallala Aquifer for irrigation to sustain the growth of economy in the state. While irrigation can often help increase crop production, its cost and environmental effects are significant, especially during drought years. Furthermore, excessive irrigation can decrease harvestable yield, increase the coast for farming, and contribute to environmental degradation (such as nitrate leaching and soil erosion). Hence, irrigation scheduling is a key component for agricultural sustainability and growth in many rural areas in Nebraska.This proposed project will establish a smart and connected system to help rural communities' irrigation scheduling in Nebraska. The system will include intensive low-cost and smart sensor networks to measure soil moisture and 2-meter meteorological parameters in Scotts Bluffs county in western Nebraska, respectively. A citizen-science network with the same set of smart sensors will also be established by engaging with citizens outside of intensive network. The data collected from these sensors will be seamlessly assimilated into a modeling system that integrates weather forecast model, crop model, and irrigation scheduling optimization algorithm to assist farmers to maximize their crop profits, as well as to reduce excess water use. Observation data and model outputs will be delivered to the rural communities through smart-phone apps and on-demand web services that enable the users to access and visualize these data (including recommendations for irrigation schedule) to serve their customized needs (for any particular farm). By engaging communities through our proposed smart and connected system and planned activities, this project will help rural communities to embrace the opportunity of using smart technologies to address their economic challenges.Intellectual Merits:A smart and connected system is proposed that combines recent advances in environmental sensing and modeling to provide the recommendation of irrigation scheduling on the daily basis for farmers in two rural areas in Nebraska. This system will be used to test the hypothesis that data-driven approach and dedicated engagement with communities can be integrated to increase farms' profit and reduce engineering and environmental costs through optimizing irrigation scheduling in rural communities. A positive feedback loop is also hypothesized in which the smart-connected system can augment existing engagement and social acceptance of smart systems within the rural communities, and such augment in turn further strengthen the data-driven approach for improving system smartness, robustness, and the connections between communities. By providing solutions for seamless environmental sensor array design and communications, weather forecast with assimilation of sensor data at regional scale, and irrigation scheduling optimized for farm profits, the system in this project can be viewed as a prototype for future precision farming in many rural areas over the Southern Great Plains where the regional economy heavily depends on irrigation.Broader ImpactsThe proposed project will have broader impacts to rural communities by functioning as an accessible resource through our collaboration partners to outreach citizens in Nebraska, Iowa, and Illinois. This project will also provide training to students via updated courses and labs for interdisciplinary research that intersects biological science and engineering, environmental science and engineering, computer science and engineering, and social science. Research findings and data will be disseminated broadly at scientific meetings, public lectures, in publications, web sites, smartphone apps, and community engagement activities, thereby further broadening impacts of this project globally, especially for the regions where irrigated agriculture is dominant.

Information and communication technologies allow individuals to apply for benefits like health care and housing, to have groceries delivered to their homes, to schedule/attend healthcare appointments, and to apply for employment. However, digital inequalities in terms of access, use, and self-efficacy reflect offline socioeconomic inequalities and pose a serious threat to today's increasingly tech-reliant society. The digital divide is a multidimensional phenomenon that refers to three levels of differences: first, in who has access to the Internet; second, who has skills to use the Internet; and third, how the Internet is actually used. Many efforts to bridge the digital divide have failed because they only address the first level (e.g., providing public Wifi, computers, computer labs) without continued onboarding and training support. This project aims to address digital disparities in Southeast Michigan by leveraging and building the digital literacy of local experts (level 2) to provide digital support to communities (levels 2 and 3). Inspired by the transformative Community Health Worker model, this work proposes "Community Tech Workers" (CTW), a community-driven, "train the trainer" approach that promotes digital literacy in communities. Participants include a public housing authority in Detroit's Eastside neighborhood, a refugee resettlement agency that serves refugees in a public housing community in Ypsilanti, an advisory board consisting of local workforce development agencies and industry representation, and a steering committee of individuals who are involved in outreach, education, and interventions related to increasing digital proficiency and public health.

The research will (1) develop and validate a survey instrument measure to assess community digital capacity; (2) develop an initial CTW training program; (3) assess the training, learning experience, and impact of the CTWs within each community; and (4) capture and evaluate the economic value of the CTW model. The survey instrument will extend existing assessments for measuring individual digital capacity to create a novel instrument that measures digital capacities at a community grain size. This instrument will be validated across two communities, and then be used to measure the impact of the CTW activities on the two communities over time. CTW training activities will be evaluated via interviews with participants and observations of the training sessions. Recommendations for a credentialing system to document CTW proficiency and for a marketplace system to promote employment opportunities will be elicited via participatory co-design sessions, information which can be used to inform integration with or development of job recommendation and gig work applications. Finally, data on costs will be collected to generate a cost-benefit analysis of the CTW model, to explore its further scalability. Secondary outcomes of the work include providing local residents with free training to obtain the necessary skills to become a CTW, and in the future, temporary employment for selected tech workers and connections to local IT employment opportunities. Incorporating the model across multiple communities will help uncover generalizable requirements for creating equitable socio-technical infrastructures that support community digital capacity and expand local opportunities. This systemic perspective on the development and deployment of the CTW model aligns well with the goal of the NSF Smart and Connected Communities (S&CC) program, which is 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, safety and security, health and wellness, accessibility and inclusivity, and overall quality of life. This project is also supported by the Improving Undergraduate STEM Education program, which seeks to support projects that have high potential for broader societal impacts, including improved diversity of students and instructors participating in STEM education, professional development for instructors to ensure adoption of new and effective pedagogical techniques that meet the changing needs of students, and projects that promote institutional partnerships for collaborative research and development.

Infants and toddlers with developmental disabilities or delays use early intervention (EI) for rehabilitation services. Yet, EI quality is compromised for racially and ethnically diverse and socially disadvantaged families. A key lever to improve EI quality is family-centered care, an evidence-based approach that is grounded in family engagement for shared decision-making. This project is motivated by the need to give families a smart and connected option for engaging in the design of the EI service plan for their child. This effort will develop and evaluate an upgraded Participation and Environment Measure (PEM), an evidence-based electronic option for directing equitable family-centered EI service design. PEM upgrades will (a) increase content relevance for racially and ethnically diverse families, and (b) leverage modern artificial intelligence solutions to personalize the PEM user experience to a broader range of EI enrolled families. This upgraded PEM electronic option will be evaluated in a population of racially and ethnically diverse EI families, to assess for its capacity to improve EI quality and to appraise supports and barriers to its longer-term implementation within the broader EI service system. This project builds evidence for the first customized, culturally relevant electronic option to direct family-centered care during EI service design. The approaches and technologies developed may be applicable to similar service contexts. Additionally, this project increases opportunities for conducting interdisciplinary research at the intersection of computer science and rehabilitation science, building interprofessional capacity for research engagement among EI service providers and students training for pediatric rehabilitation careers, and sponsoring students from historically underrepresented groups in diverse research labs that value inclusive excellence.

This proposal develops key innovations to family-centered EI in two ways. First, for the PEM electronic option, the project will (a) increase content relevance for racially and ethnically diverse families, and (b) personalize the PEM user experience to a broader range of EI enrolled families. For the former, the project will establish cultural equivalencies of the original PEM assessment and critically examine its intervention content to ensure that families can voice concerns about racial climate and collect and share goal attainment strategies using community-preferred communication channels. For the latter, the team will incorporate an adaptive conversational agent into the PEM intervention to improve caregiver navigation and guidance, and we will develop methods to automatically customize its strategy exchange feature to individual caregiver needs. These innovations will result in fundamental advances to natural language processing research through the investigation of adaptive dialogue policies for task-oriented or mixed-initiative dialogue systems, generalized dialogue act schema, and lexicon-informed meaning representations. We will evaluate the upgraded PEM electronic option with racially and ethnically diverse and socially disadvantaged EI enrolled families, to assess for its capacity to improve caregiver and provider perceptions of family-centered EI service quality, improve parent engagement in EI service plan implementation, and increase the availability and relevance of participation-focused EI service plans. We will engage EI stakeholders to appraise supports and barriers to its longer-term implementation in EI. These advances will yield evidence for a customized, culturally-relevant electronic option to foster family-centered care in EI.

Communities are adversely affected by social harm events such as crime, traffic crashes, medical emergencies, and drug usages. This proposal aims to develop algorithms and software systems for the collection, analysis, and dynamic prediction of social harm events to facilitate appropriate government interventions to improve the quality of life in communities. The project has a significant community engagement component and software developed through the research will be used by the Indianapolis Metropolitan Police Department (IMPD), Indianapolis Emergency Medical Services (EMS), National Alliance of Mental Illness, the Indiana prosecutor's office, and individual citizens for sharing of social harm analytics and collaboration in social harm intervention. This objective will be achieved by: i) creating software systems for cross-agency social harm data integration, ii) developing mathematical models for capturing social harm event dynamics along with public trust and grievance towards police, and iii) conducting a field trial of the developed software system in Indianapolis. The methods developed in the project will also be applicable to other smart and connected communities across the country and could be used for data analytics integration and allocation of resources across government departments. Graduate students from both social science and computing disciplines will be trained in interdisciplinary research methods that span criminal justice, statistics, and computer science. Research interests in the domain of algorithms for heterogeneous data in smart cities will be encouraged through a workshop hosted by the investigators at Indiana University-Purdue University Indianapolis (IUPUI).

Social harm data resides within a disconnected set of community databases and current methodologies for modeling social harm neglect space-time dynamics altogether or focus on a small related subset of event types. Furthermore, interventions are designated in spatial locations for several weeks or months at a time, failing to account for the daily changes in risk of social harm events where crime, traffic crashes, and medical emergencies cluster in different times and locations in communities. Current policing interventions that focus on spatial risk (i.e., hotspots) are often too narrow and seek only to optimize crime reductions. In order to address some of these limitations, this project will develop: i) software systems for heterogeneous social harm data integration, ii) new marked point processes for modeling heterogeneous social harm event dynamics including trust and grievances towards police, iii) optimal control methods for space-time point processes that are lacking in current point process research, and iv) near real time software-human systems for deploying hourly interventions to dynamically changing risk. During phases one and two, the project team will work collaboratively with IMPD's community policing unit and leverage this unit's relationships with local neighborhood watch, faith-based, juvenile diversion, and volunteer groups that are predominantly comprised of minority community members serving largely minority neighborhoods. This collaboration will facilitate broad community buy-in for phase three and enable communication with and recruitment of community groups disproportionately exposed to social harm risk. The last phase of the project will include a randomized controlled trial of heterogeneous data driven policing in Indianapolis in collaboration with IMPD, Indianapolis EMS, Indianapolis Mayor's Office, National Alliance of Mental Illness, Marion County Prosecutor's Office, the Indy Public Safety Foundation, and the general public who will be encouraged to download a version of the application through a press release prior to the trial launch. In the trial, the extent to which police in partnership with community stakeholders can respond to dynamic, heterogeneous social harm hotspots will be investigated and the impact across four types of social harm (crime, traffic crashes, EMS calls for service, and community trust in police within high risk communities) will be measured.

This activity is in response to the NSF Dear Colleague Letter: Supporting Transition of Research into Cities through the US ASEAN (Association of Southeast Asian Nations Cities) Smart Cities Partnership (NSF 20-024), in collaboration with the US Department of State. This research seeks to integrate innovations in smart and connected communities with creative gardens within the city alleys of Makassar City, Indonesia via a synergistic collaboration between US and Indonesian teams and a close partnership with Makassar City. Makassar is striving to become a livable world class city for a fast-growing, diverse population of 1.7 million people. The ongoing “Garden Alley” project in the city aims to improve the “livability” of the city, measured by factors including air-quality, heat index, food security, and social interactions. To date, Makassar has implemented 40 gardens within 15 of the city’s sub-districts, covering a sizable portion of the city’s alleys. The goal of this research is to catalyze the transformation of Makassar City’s garden alleys into smart environments by deploying a sensor network at representative green allies and conventional allies to collect data related to air quality, microclimates, and other factors, to analyze the heterogeneous data using machine learning techniques, and to then share the data and its insights with city representatives and specific communities within the city.

This transformative research will provide the foundational science and knowledge that are needed to design, optimize, and deploy S&CC technologies within the ASEAN region and beyond. This interdisciplinary research will yield several major innovations: 1) New low-cost, durable, and mobile sensor networks will be designed for air quality and microclimate monitoring in the hot and humid climate in southeast Asian cities. 2) Suitable machine learning techniques will be employed to exploit the multi-dimensional and heterogeneous data collected from both existing infrastructures and new mobile test platforms in Makassar and create intelligent spatio-temporal operational maps of Makassar’s alleys that can be used for various design, planning, and operational decisions by the city. 3) Data-driven city-scale smart operating schemes involving feedback loops will be explored through close engagement with the Indonesian partners. The developed solutions will be highly dynamic yet robust and have the potential to be scaled, as well as transferred to other cities. Additionally, this project will initiate a new collaboration between the US and Indonesia, improving the quality of life in an emerging southeast Asian city, but with potentially broad applicability, and provide a broad range of dissemination activities, involvement of students in international activities, as well as active engagement with local communities and researchers in Indonesia.
 

Uncertainties during global pandemics, such as the novel Coronavirus disease (COVID-19), can generate fear and anxiety, resulting in panic-buying and overreactive consumer behavior. Information from a multitude of sources may further exacerbate the situation, leading to shortages of critical disease prevention products for emergency managers and those in dire need. The consumer response may also vary based on population demographics and community interactions. This project aims to understand the relationships between consumer panic-buying, reports on infected cases, and local population demographics in a large and densely populated urban epicenter of the virus. Fundamental understanding of community factors and the role of reports on consumer behavior in emergencies will enable effective and timely decisions on resource planning and disbursement, preventing unexpected shortages of critical supplies in large and diverse urban centers. In addition, the quantitative methodologies developed in this project bridge the disciplines of engineering, computer science, social and health science, creating a new interdisciplinary paradigm that provides a holistic view towards emergency preparedness and disaster management in urban centers.

The main focus of this RAPID project is to develop a multi-network framework that captures the linkages and inter-dependencies between networks that govern information spreading, panic spreading, and disease spreading in urban populations. Fundamental understanding of the relationships between various factors such as consumer buying behavior, socio-economic community characteristics, and the extent of available health information enables the assessment of potential outcomes such as shortages of critical disease prevention supplies. Data and crowdsourced information from the COVID-19 experience of selected NYC neighborhoods is used as a case study for validation studies. An accurate understanding of the multi-faceted consumer behavior enables decision analytics for effective planning and targeted disbursement of critical supplies for mitigating the effects of panic-buying. The identification of underlying complex and interdependent network structures provides insights into the design of equitable and effective strategies for resource planning and allocation to tackle the vicious panic cycle in emergencies, thus promoting urban resilience.
 

This goal of this project is to optimize processes for remote delivery of meals to persons in need. The COVID-19 pandemic has fundamentally disrupted processes of food delivery to economically depressed and vulnerable segments of the US population. With the closing of schools and the advent of social distancing practices, over 13 million low-income students who have historically relied on their school to provide daily meals are now without important nutritional support, and centralized school summer meal distribution programs are no longer viable. Similarly, low-income adults and seniors that depend on centralized distribution of meals at shelters and food banks are now being forced to cope with virus mitigation procedures that severely limit their access. Both short and long term solutions to food security for all in this new normal depend on greater reliance on remote food delivery, and although vehicle routing and pickup/delivery problems have been studied for close to 50 years, the constraints imposed by contemporary public health and social distancing concerns present new optimization challenges. This research will contribute new problem formulations and solutions to these important classes of remote food delivery problems, and through existing relationships with the Allegheny County Department of Human Services, Southwestern Pennsylvania United Way, Allies for Children and the Greater Pittsburgh Food Bank, the project will apply research results to inform their ongoing pilot food delivery efforts. More broadly, these results will stimulate future research on these problems and influence remote food delivery problems nationwide.

To realize these results and impact, this project aims to develop new algorithms and heuristics that address the unique constraints and objectives presented by these geographically-dispersed food delivery problems, to provide a theoretical basis for more efficient operational practice. With respect to school bus student meal delivery, algorithms and heuristics for solving several problems will be developed and analyzed. First, the project will consider the coupled problem of assigning stops to students requiring meals and generating efficient routes to accommodate these students within a global meal time window, while enforcing social distance constraints on number of students that can be assigned to any one bus stop. Second, the research will investigate an extended formulation that additionally allows the use of smaller passenger vehicles or vans, to better service students that have difficult access to bus stops and/or long walk times. To ensure relevance, the project will utilize demand and bus route data from selected school districts in Allegheny County, PA to evaluate performance. Finally, with respect to remote distribution of food to low-income seniors, the algorithms and heuristics developed for student meal delivery will be extended and adapted to this more capacity constrained setting, where food must be moved exclusively in smaller volunteer passenger vehicles. Data obtained from the Greater Food Bank of Pittsburgh will be used to evaluate these extended research results. All data sets used and solutions results obtained will be made available to stimulate future research in this area.