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Indicators of Rurality: How Should we Define “Rural” and “Urban” Communities?

Updated: Apr 4

Over the last decade, there’s been a lot of talk about the “urban/rural divide” in a number of different contexts. People reference the digital divide, the political divide, the economic divide, healthcare disparities across the urban/rural divide, and more. Some argue that the idea of an urban/rural divide is counterproductive to positive community change, and others maintain that bridging the divide is a key tactic to advancing community well-being.

Regardless of whether the “urban/rural divide” is a useful frame, data support the fact that there are established differences in rural and urban communities, and therefore tailored strategies are needed to improve community conditions. It is helpful to understand whether a community is urban or rural when exploring data about a community to inform your work, and there are also often policy implications of whether a community is designated rural (e.g. in the Farm Bill). But, different data sources provide different measures for classifying communities as rural or urban.

In this post, we unpack several datasets that classify areas as either urban or rural, spell out how each source defines the terms, and outline some recommendations for leveraging each dataset. Our hope is that you’ll be equipped to use the dataset that makes the most sense within the context of your work.

At the end of the day, there is no replacement for direct community engagement to understand community conditions and specific challenges a community faces based on its rural character or otherwise. The reality is that we all live in a spectrum of urban to rural. Community members might perceive themselves as a rural community, while they may be classified otherwise. Through dialogue, the significance and implications of a community’s urban and/or rural character can be more deeply understood. In some cases, it may be most helpful to explore two or three indicators of rurality to see how one community is classified by a number of different datasets. Ground-truthing measures like those offered here alongside stories from those with lived experience provide a rich portrait of a given community.

Indicators of Rurality

Taken from the Decennial Census, Rural Population gives the percentage of the total population that lives in a rural-classified area within a defined geographic boundary (i.e. census tract, county). The U.S. Census Bureau’s urban-rural classification identifies rural as not an urban area which are characterized by a densely settled core, minimum population density, minimum population residing outside of institutional group quarters, and requirements about adjacent territories. This measure, Rural Population, is the only population measure and the only continuous measure in this list. Providing the rural population along other measures of rurality can be useful in characterizing an area.

The National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties classifies counties on a six-level scheme that distinguishes counties by their size and centrality to their urban core. Developed for its utility in studying health differences across urban/rural continuum, the scheme includes more metropolitan levels (4) than non-metropolitan levels (2), in order to support more levels of health analyses within metropolitan populations. Given its application to health assessment, the NCHS Urban-Rural Classification Scheme for Counties may be particularly relevant in community health contexts, especially in urban contexts.

The U.S. Department of Agriculture (USDA) Rural-Urban Continuum Codes classifies counties on a nine-level scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The scheme includes more non-metropolitan classes (6) than metropolitan classes (3), and is particularly useful for analysis of trends in non-metropolitan areas that are related to population density and metro influence.

The USDA Frontier and Remote (FAR) Area Codes classifies zip codes on a four-level scheme based on population size and distance dimensions, reflecting likely access to high order services. The FAR codes define levels by distance in travel time to urban areas of varied sizes. Designed to aid research and policy, FAR characterization of zip codes is relatable, and may be readily understood and communicated by general audiences. However, zip codes are not the most robust units of analysis, and using them judiciously in socio-economic applications is recommended.

Urban Influence Codes classifies counties on a 12-level scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan area. Urban Influence Codes include more non-metropolitan levels (10) than metropolitan levels (2). The scheme was developed to allow researchers to break county data into finer residential groups, beyond metro and non-metro, particularly for the analysis of trends in non-metro areas that are related to population density and metro influence. As such, Urban Influence Codes may be particularly useful when analyzing trends in non-metro areas; however the detailed non-metro classes may be overly complex for most applications, and nuances may be more confusing than insightful.

Rural-Urban Commuting Area Codes have a multi-level two-tier classification scheme that distinguishes U.S. census tracts using measures of population density, urbanization, and daily commuting. The primary tier comprises whole numbers (1-10) which delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows; the secondary further subdivides primary tier codes based on secondary commuting flows, providing flexibility in combining levels to meet varying definitional needs and preferences. This highly granular and detailed scheme is attractive for various analytic applications, but may be too detailed and complex for common use cases.

Do you have experience trying to use data to define a community or geographic area as rural or urban? Have you worked on a project or with a group of stakeholders where this issue has come up? We'd love to hear from you. Please comment below or get in touch to discuss more!

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