five

The Index of Relative Rurality (IRR) : US County Data for 2000 and 2010

收藏
DataCite Commons2025-12-18 更新2024-07-13 收录
下载链接:
https://purr.purdue.edu/publications/2960/1
下载链接
链接失效反馈
官方服务:
资源简介:
<p>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, <a href="http://ageconsearch.umn.edu/handle/21383">http://ageconsearch.umn.edu/handle/21383</a>) 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) designed an improved county level IRR for 2000 and 2010.</p> <p>The IRR has three major 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.  </p> <p>The IRR ranges between 0 (low level of rurality, i.e., urban) and 1 (most rural). Four steps are involved in its design:</p> <ol> <li>Identifying the dimensions of rurality: population size, density, remoteness, and built-up area.</li> <li>Selecting measureable variables to adequately represent each dimension: <ol style="list-style-type:lower-alpha;"> <li>Size: logarithm of population size</li> <li>Density:  logarithm of population density.</li> <li>Remoteness: network distance.</li> <li>Built-up area: urban area (as defined by the US Census Bureau) as a percentage of total land area.</li> </ol> </li> <li>Re-scaling the variables onto bounded scales that range from 0 to 1.</li> <li>Selecting a link function: unweighted average of the four re-scaled variable.</li> </ol> <p>For more information:</p> <p>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 <em>Workshop on Rationalizing Rural Classifications</em>, April 2015, Washington, DC <a href="http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf">http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf</a></p>
提供机构:
Purdue University Research Repository
创建时间:
2018-04-16
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作