The Index of Relative Rurality (IRR): US County Data for 2020

The Index of Relative Rurality (IRR) is a continuous, threshold-free, and unit-free measure of rurality. The original version of the IRR was proposed by Waldorf (2006, http://ageconsearch.umn.edu/handle/21383) as an alternative to the traditional discrete threshold-based classifications, such as the Rural-urban Continuum Code and the Urban Influence Code. Waldorf and Kim (2015) re-designed measuring the index and applied it to publish improved county-level IRR for 2000 and 2010. IRR 2020 was measured by the same method suggested in 2015 except for the network data (North American Roads*) due to the data availability. (* Bureau of Transportation Statistics (BTS), https://geodata.bts.gov/datasets/usdot::north-american-roads/about). The IRR has three significant advantages over typology-based rurality measures. (1) It is spatially flexible in that it can be designed for any spatial units; (2) it is a relative measure and thus embeds rurality in the broader system of settlements; (3) it is analytically more easily handled than threshold-based typologies. The IRR ranges between 0 (low level of rurality, i.e., urban) and 1 (most rural). Four steps are involved in its design: 1. Identifying the dimensions of rurality: population size, density, remoteness, and built-up area. 2. Selecting measurable variables to adequately represent each dimension: a. Size: logarithm of population size b. Density: logarithm of population density. c. Remoteness: network distance. d. Built-up area: urban area (as defined by the US Census Bureau) as a percentage of total land area. 3. Re-scaling the variables onto bounded scales that range from 0 to 1. 4. Selecting a link function: unweighted average of the four re-scaled variables. IRR 2020 - County-level Map Please cite this work: DOI: 10.5281/zenodo.7675745 For more information: Waldorf, Brigitte, and Ayoung Kim. 2015. "Defining and Measuring Rurality in the US: From Typologies to Continuous Indices." Commissioned paper prepared for the National Academies of Sciences Workshop on Rationalizing Rural Classifications, April 2015, Washington, DC. http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf Acknowledgment: ** This project was supported by the Agricultural and Food Research Initiative Competitive Program of the USDA National Institute of Food and Agriculture (NIFA), grant number 2020-67019-30772. Contact: Ayoung Kim | a.kim@msstate.edu | Dept. of Agricultural Economics | Mississippi State University Brigitte Waldorf | bwaldorf@purdue.ed