A fine-grained, versatile index of remoteness to characterize place-level rurality
Rural-urban classifications are essential for analyzing geographic, demographic, environmental, or socioeconomic processes across the rural-urban continuum. However, existing county-level classifications may ignore the within-county variations of rurality, which can be problematic if the scale of in...
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creator | Uhl, Johannes H Leyk, Stefan Hunter, Lori M Talbot, Catherine B Connor, Dylan S Nieves, Jeremiah J Gutmann, Myron |
description | Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, or socioeconomic processes across the rural-urban
continuum. However, existing county-level classifications may ignore the
within-county variations of rurality, which can be problematic if the scale of
interest is at the place-level or finer. Moreover, existing rural-urban
classification are often inconsistent over time and thus, impede the long-term
analysis of rural-urban dynamics. We developed a distance-based method to
generate place-level remoteness estimates based on simple input data. We create
our remoteness index based on place-level population data for the U.S. from
1980 to 2010. The proposed index is generalizable to data-scarce environments
and earlier time periods and is based on the distances of a given place to the
nearest places of different population sizes, and allows for fine-grained,
temporally consistent analyses of rural-urban processes. |
doi_str_mv | 10.48550/arxiv.2202.08496 |
format | Article |
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demographic, environmental, or socioeconomic processes across the rural-urban
continuum. However, existing county-level classifications may ignore the
within-county variations of rurality, which can be problematic if the scale of
interest is at the place-level or finer. Moreover, existing rural-urban
classification are often inconsistent over time and thus, impede the long-term
analysis of rural-urban dynamics. We developed a distance-based method to
generate place-level remoteness estimates based on simple input data. We create
our remoteness index based on place-level population data for the U.S. from
1980 to 2010. The proposed index is generalizable to data-scarce environments
and earlier time periods and is based on the distances of a given place to the
nearest places of different population sizes, and allows for fine-grained,
temporally consistent analyses of rural-urban processes.</description><identifier>DOI: 10.48550/arxiv.2202.08496</identifier><language>eng</language><subject>Statistics - Applications</subject><creationdate>2022-02</creationdate><rights>http://creativecommons.org/licenses/by-nc-nd/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2202.08496$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2202.08496$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Uhl, Johannes H</creatorcontrib><creatorcontrib>Leyk, Stefan</creatorcontrib><creatorcontrib>Hunter, Lori M</creatorcontrib><creatorcontrib>Talbot, Catherine B</creatorcontrib><creatorcontrib>Connor, Dylan S</creatorcontrib><creatorcontrib>Nieves, Jeremiah J</creatorcontrib><creatorcontrib>Gutmann, Myron</creatorcontrib><title>A fine-grained, versatile index of remoteness to characterize place-level rurality</title><description>Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, or socioeconomic processes across the rural-urban
continuum. However, existing county-level classifications may ignore the
within-county variations of rurality, which can be problematic if the scale of
interest is at the place-level or finer. Moreover, existing rural-urban
classification are often inconsistent over time and thus, impede the long-term
analysis of rural-urban dynamics. We developed a distance-based method to
generate place-level remoteness estimates based on simple input data. We create
our remoteness index based on place-level population data for the U.S. from
1980 to 2010. The proposed index is generalizable to data-scarce environments
and earlier time periods and is based on the distances of a given place to the
nearest places of different population sizes, and allows for fine-grained,
temporally consistent analyses of rural-urban processes.</description><subject>Statistics - Applications</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71qwzAUhmEtHUraC-hUXUDtyrIkH48h9A8CgZLdHMlHiUCxg6yapFffNO30Dh988DD2UIlSgdbiGdMpzKWUQpYCVGtu2eeS-zBQsUt4Sf_EZ0oT5hCJh6GnEx89T3QYMw00TTyP3O0xocuUwjfxY0RHRaSZIk9fCWPI5zt24zFOdP_fBdu-vmxX78V68_axWq4LNI0pKi9RqcpS02q0AmSLYACEJlmDNuCFJXfZK69076z1pmksOuukBgcO6wV7_Lu9orpjCgdM5-4X111x9Q8rnEtD</recordid><startdate>20220217</startdate><enddate>20220217</enddate><creator>Uhl, Johannes H</creator><creator>Leyk, Stefan</creator><creator>Hunter, Lori M</creator><creator>Talbot, Catherine B</creator><creator>Connor, Dylan S</creator><creator>Nieves, Jeremiah J</creator><creator>Gutmann, Myron</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20220217</creationdate><title>A fine-grained, versatile index of remoteness to characterize place-level rurality</title><author>Uhl, Johannes H ; Leyk, Stefan ; Hunter, Lori M ; Talbot, Catherine B ; Connor, Dylan S ; Nieves, Jeremiah J ; Gutmann, Myron</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-1f2a441be795ab0829a868805e238568f0bec41b1f45dcbbf677bacbc258c8ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Statistics - Applications</topic><toplevel>online_resources</toplevel><creatorcontrib>Uhl, Johannes H</creatorcontrib><creatorcontrib>Leyk, Stefan</creatorcontrib><creatorcontrib>Hunter, Lori M</creatorcontrib><creatorcontrib>Talbot, Catherine B</creatorcontrib><creatorcontrib>Connor, Dylan S</creatorcontrib><creatorcontrib>Nieves, Jeremiah J</creatorcontrib><creatorcontrib>Gutmann, Myron</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Uhl, Johannes H</au><au>Leyk, Stefan</au><au>Hunter, Lori M</au><au>Talbot, Catherine B</au><au>Connor, Dylan S</au><au>Nieves, Jeremiah J</au><au>Gutmann, Myron</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A fine-grained, versatile index of remoteness to characterize place-level rurality</atitle><date>2022-02-17</date><risdate>2022</risdate><abstract>Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, or socioeconomic processes across the rural-urban
continuum. However, existing county-level classifications may ignore the
within-county variations of rurality, which can be problematic if the scale of
interest is at the place-level or finer. Moreover, existing rural-urban
classification are often inconsistent over time and thus, impede the long-term
analysis of rural-urban dynamics. We developed a distance-based method to
generate place-level remoteness estimates based on simple input data. We create
our remoteness index based on place-level population data for the U.S. from
1980 to 2010. The proposed index is generalizable to data-scarce environments
and earlier time periods and is based on the distances of a given place to the
nearest places of different population sizes, and allows for fine-grained,
temporally consistent analyses of rural-urban processes.</abstract><doi>10.48550/arxiv.2202.08496</doi><oa>free_for_read</oa></addata></record> |
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subjects | Statistics - Applications |
title | A fine-grained, versatile index of remoteness to characterize place-level rurality |
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