Our Iowa Rural Cities

The map and table below focuses on fiscal capacity, hospitals (beds and tramua levels), post offices, schools, and fire departments. Cities with higher distance from the following tend to be higher in shrinkage in rural areas, while the lower the distances to have a less shinkage in rural areas. The graphs can be adjusted by year.

Closest Services:

Town Level Demographics:









County Level Demographics:


The following plot is called a Parallel Coordinate Plot (PCP). It is used to analyze multivariate numerical and categorical data, which will allow users to compare observations across multiple variables. Note: your city is highlighted and populated first as it compares to other cities in Iowa.

Similar Towns


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Select Similar Cities as a group

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Sac City


Elma


Bancroft


Corning


Everly


Mount Ayr


Last update

This site is updated once daily. There are several updates to insure data are accuate for your town. The Rural Shrink Smart Official Website, Our aim is to complement these resources with several interactive features, including the timeline function and the ability to overlay past outbreaks.

Background

Some communities continue to thrive as they lose population because they adapt and stay focused on quality of life, community services, and investing in the future. This is what we call rural smart shrinkage. With funding from the National Science Foundation, our research team is developing strategies and sharing examples of successful shrink-smart towns to help similar communities to improve quality of life for their residents.

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 identified a group of shrinking communities from among the poll towns that scored higher than average on quality-of-life indicators from the 1994, 2004, and 2014 polls. From 2017 to 2019, we studied these communities using qualitative and quantitative methods to learn what makes them great places to live in the opinions of the residents.

Code

Code and input data used to generate this Shiny tool are available on Github.

Sources

DSPG-ISU: DSPG projects at Iowa State University website.

Authors

Dr. Susan VanderPlas, University of Nebraska - Lincoln, Assistant Professor - Department of Statistics
Denise Bradford, University of Nebraska - Lincoln, PhD Student - Department of Statistics


Contact

denise.bradford@huskers.unl.edu

Attribution

This app uses font awesome icons under the Creative Commons Attribution 4.0 International license license . Icons may have been slightly modified or recolored from the original source.