New research phase to begin in Fall 2020 (check back soon for details)
Planning Grant (2017-2019): A Data-Driven Framework for Smart Decision-Making in Small and Shrinking Communities
This project is funded with a planning grant from NSF for Smart & Connected Communities research. Our team brings together expertise in Architecture, Community & Regional Planning, Computer Science, Rural Sociology, and Sustainable Environments.
Many American small towns and rural communities have been in decline since the 1980s. In the Midwest, most communities have experienced this through shrinking populations, an exodus of younger people, job losses, and aging infrastructure. Evidence shows that these trends have continued over several decades and are unlikely to be reversed. Yet the research on small and rural communities has focused primarily on documenting and observing aspects of decline or promoting uncertain growth strategies, rather than understanding how communities can protect quality of life and community infrastructure while they shrink. This project aims to fill this gap by developing a new shrink-smart concept for small communities that utilizes data-driven tools to assist them in actively planning for shrinkage. The objective of the planning phase is a pilot study to test the feasibility and reliability of such tools in Iowa. The pilot study will use data scraped from broadly available sources, such as social media, census, state, and municipal databases, for comparison with traditional metrics including unique baseline data from longitudinal polling in Iowa. This pilot study has three goals: 1) to demonstrate the feasibility of applying the shrink-smart concept to rural communities, 2) to assess the feasibility of measuring smart shrinkage through data-driven analysis, and 3) to test visualization methods for data analysis and communication to stakeholders.
The project’s central hypothesis is that data-driven techniques will identify proxy metrics for indicators of smart shrinkage by using broadly available data sources to estimate the results of qualitative measures such as longitudinal polling. These proxies will replace traditional methods of collecting quality-of- life data, which are time-consuming, expensive and incomplete over large geographic areas. In the planning phase, we will establish criteria for types of smart shrinkage and select 6-8 representative communities in Iowa for in-depth analysis. The research will be transformative for the study of small and shrinking communities because of its powerful integrated methodology that combines quantitative data-driven analysis with qualitative understanding of smart shrinkage that is verified through community engagement, spatial analysis, and on-the- ground data collection. This integrated methodology creates a new framework to help community stakeholders understand how and why some small and rural communities are able to protect their quality of life even as they lose population. This approach will also provide new opportunities for communities across the United States to make smart decisions that are likely to mitigate the negative effects of shrinkage before signs of decline appear. In addressing small and rural communities, this project brings attention to underrepresented cases in the research literature. This knowledge will be disseminated to stakeholders and the public through multiple venues in Iowa and beyond, including through Iowa State University Extension and Outreach. All of the extensible data pipelines and visualization techniques will be licensed through open source protocols.