TY - JOUR
T1 - When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones
AU - Carpenter, Craig Wesley
AU - Lotspeich-Yadao, Michael C.
AU - Tolbert, Charles M.
N1 - Funding Information:
This project was supported by the Agricultural and Food Research Initiative Competitive Program of the USDA National Institute of Food and Agriculture (NIFA, https://nifa. usda.gov/), award numbers 2017-67023-26242 (CC) and 2018-68006-34968 (CC, ML, CT). NIFA funded the time and data collection for the coauthors.
Publisher Copyright:
© 2022 Carpenter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/7
Y1 - 2022/7
N2 - U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. We find CZs often reduce—but do not eliminate and can even increase—spatial autocorrelation for variables across categories. We then test the potential for regional variation in spatial autocorrelation with a series of maps and find variation based on the variable of interest. We conclude that the use of CZs does not eliminate the need to test for spatial autocorrection, but CZs may be useful for reducing spatial autocorrelation in many cases.
AB - U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. We find CZs often reduce—but do not eliminate and can even increase—spatial autocorrelation for variables across categories. We then test the potential for regional variation in spatial autocorrelation with a series of maps and find variation based on the variable of interest. We conclude that the use of CZs does not eliminate the need to test for spatial autocorrection, but CZs may be useful for reducing spatial autocorrelation in many cases.
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U2 - 10.1371/journal.pone.0270303
DO - 10.1371/journal.pone.0270303
M3 - Article
C2 - 35830393
AN - SCOPUS:85134355766
SN - 1932-6203
VL - 17
JO - PloS one
JF - PloS one
IS - 7 July
M1 - e0270303
ER -