Principal Investigator(s):
Shashi Shekhar, McKnight Distinguished Professor, Computer Science and Engineering
Project summary:
This project is investigating a smart, urban infrastructure systems framework for advancing access and wellbeing in cities. With transformative new infrastructures (e.g., smart electricity grid and urban farms) on the horizon, this research will provide new perspectives on how the future spatial deployment of these new infrastructures in cities will shape wellbeing, health, and the environmental sustainability of outcomes in the different areas of cities. The project is advancing basic research in multiple disciplines including environmental and civil engineering, computer science, urban planning, and public policy. It is creating a unique public database, establishing citizen science protocols, and advancing the science of smart, sustainable urban systems through knowledge co-production with cities engaged in infrastructure planning. The project is engaging in educational activities through interdisciplinary training for graduate students and professionals in urban planning, policy, and sustainability. Furthermore, a strong component of citizen science engagement is being involved through K-12 teachers and students, particularly in schools with underrepresented populations.
Environmental sustainability, human health, and wellbeing outcomes in cities are significantly shaped by key physical infrastructure provisions of water, energy, food, shelter, transportation-communications, sanitation waste management, and public spaces, as well as their interactions with the social, environmental, and urban form parameters. The investigators are conducting an interdisciplinary, community-engaged research project in the cities of Minneapolis, St. Paul, and Tallahassee. The research is engaging four themes: a) Developing the first comprehensive, fine-scale, intra-urban database of over 100 social-ecological health and wellbeing parameters via novel citizen science/crowdsourcing campaigns using low cost sensors; b) Developing advanced computational algorithms to uncover hotspots and spatial correlations in the data and evaluate data-driven/discipline-inspired access and wellbeing hypotheses; c) Using outcomes from a) and b) to develop connected, multi-infrastructure futures scenario models with new infrastructures through shared scenario visioning exercises, as well as evaluating policy learning and value of information; d) Focusing on education and workforce development for middle-high schoolers, graduate students, and sustainability professionals. Outcomes from this research will be useful for informing citizens and policymakers about smart infrastructure transitions being planned in cities.