Shrinking populations are usually seen as a sign of community decline. In this NSF-funded project, we explore small and shrinking rural communities in Iowa that have been able to protect quality of life for their residents even as they lose population.
We call these shrink-smart communities.
Our goal is to develop tools to help all small and shrinking communities actively plan for shrinkage before population loss affects their quality of life. To do this, we start with a unique data set, the Iowa Small Town Poll, that has collected quality-of-life data in ninety-nine Iowa communities since 1994. We have identified a group of shrinking communities that score higher than average on quality-of-life indicators in the poll data. Over the next year, we will study these communities using qualitative and quantitative methods to learn what makes them thrive.
We will use data-driven techniques, including novel applications of machine-learning methods, to identify proxy metrics for indicators of smart shrinkage by using broadly available data sources to estimate the results of the longitudinal polling. These proxies will replace traditional methods of collecting quality-of-life data and therefore allow us to work with any small and shrinking community interested in planning for population loss. As the research progresses, we intend to broaden our research into different areas of the United States and into larger communities.