Standardizing Definitions:
The Urban/Rural Divide
By Izzy Youngs
This blog post is part of the Massive Data Institute’s Place-Based Indicators Project. Each blog post discusses an aspect of our research and efforts to produce a framework for understanding, evaluating, and developing place-based indicators. To contact us about the project, please email pbi@georgetown.edu.
In our first blog post, “When the Data Isn’t Perfect: Why we need higher quality place-based indicators”, we conducted a landscape analysis of major obstacles and quality issues for place-based indicators. The first obstacle we outlined was standardization, or the challenge that across fields and industries, indicators utilize different definitions for key terms. In this blog post, we will explore this first obstacle through a specific use case: the urban/rural divide.
The Center on Rural Innovation has previously written about how differences in definitions of rurality can frame the stories and narratives about rural America — and create a fractured landscape for federal funding eligibility. Globally, different definitions of urbanity have had a global impact on foreign aid and domestic spending. Despite the fact that urbanity is a spectrum and neither “urban” nor “rural” can be considered perfectly internally consistent and mutually exclusive categories, having a shared understanding of thresholds and criteria for determining rurality versus urbanity is critical for policymaking decisions and the distribution of funding.
We have organized types of urban and rural definitions into three buckets: Administrative, physical, and economic. These definitions support the multitude of questions about place being asked by community leaders and policymakers.
Administrative
States and countries often utilize statutory language to determine urban thresholds, which then govern how much funding different jurisdictions get. For example, states may classify political jurisdictions of 50,000 or more as being part of a specific population category, subjecting them to different service obligations and laws.
However, local and federal government services are not always population-based. For example, road maintenance is far more dependent on the utilization of roads and highways (which is a combination of many factors, including vehicle miles traveled, freight hub locations, and road area) than strictly on population. Likewise, utilities management, public transportation, police, and fire are all dependent on the land use and distribution of population, not just the total population number, for funding.
Physical
Other definitions of rurality and urbanity consider land use and physical characteristics, for instance, by using population density in combination with total population. Perhaps a place has a high population, but a low density, or vice versa. Neither might meet the threshold for an urban area. For example, the EU’s Organization for Economic Co-operation and Development (OECD) defines a “functional urban area” as a “contiguous geographic area with at least 50,000 inhabitants at an average population density of 1,500 people per square kilometer”.
Even within the criteria of population density, there are many ways to operationalize the term. For example, the Global Human Settlement Layer (GHSL) data includes datasets which measure the distribution of built-up surfaces, while others depict the spatial distribution of building heights per cell, while yet others look at built-up volume. The Center for International Earth Science Information Network (CIESIN) at Columbia University has developed a methodology for modeling global population density by using remote sensor data on land cover, urban extent, accessibility and more. However, there is no standard definition or methodology for measuring population density.
Economic
Further confusion arises with “metropolitan” and “urban” areas, where metropolitan areas include urban agglomerations with many jurisdictions, capturing a more economic relationship between multiple urban areas. Most definitions of metropolitan areas include commuting patterns to incorporate economic relationships. At a regional scale, this can be useful for differentiating between suburban or exurban areas of a larger metropolitan area versus a truly “rural” area that is not adjacent to any urban cores. These types of areas often experience significant differences in economic status and power. However, at the neighborhood scale, it can be hard to differentiate between urban cores and more residential areas without looking at economic flows. Proxies for economic flow include zoning and land use indicators, real-time (or near real-time) transportation or mobility data, and consumer purchase data. All of these have their own methodological and ethical considerations. Land use data may reflect the infrastructure that is there, but not the extent to which people are using it. Most mobility and transaction data are proprietary and not methodologically transparent. Furthermore, transaction data may not accurately capture in-person sales versus ecommerce, and only collect data on credit card sales, not cash or debit transactions (introducing serious equity concerns). Despite the importance of such data in understanding economic activity across communities, we must address the significant ethical and privacy issues that arise with the use of these data.
We encourage analysts exploring urbanity to consider an administrative, physical, or economic framing. When asking, “Are fewer vehicle miles traveled associated with greater urbanity?” seek an indicator that relies on a physical framing of rurality that uses population density measures. When asking “Are rural areas economically declining?” seek an indicator that considers the economic relationships between areas and qualifies a rural area depending on its adjacency to metropolitan or micropolitan areas. Utilizing new spatial data sources such as population density maps or mobility data can help to provide more detail on urbanity at various geographic levels.
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