Challenges in the Analysis of Rural Populations in the United States

Murdock, Steve H.; Cline, Michael E.; Zey, Mary

doi:10.1007/978-94-007-1842-5_2

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Disadvantages continue when considering the study and measurement of these populations to motivate appropriate policies. Demographers have long acknowledged the instability of measures concerning rural populations due to sparsity [32], often methodologically mitigated by substituting statistics from larger areas with similar population characteristics, making trends observed in specific rural areas, in fact, synthetic [33].…”

Section: Introductionmentioning

confidence: 99%

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Rama

Mejova

Tizzoni

et al. 2020

Proceedings of the Web Conference 2020

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In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging to track important statistics concerning this sparse, geographically dispersed population, resulting in a lack of reliable, up-to-date data. In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital "census" of over two billions of its users, in measuring potential rural-urban inequalities. We focus on Italy, a country where about 30% of the population lives in rural areas. First, we show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. To overcome such limitation, we propose an alternative methodology for estimating Facebook Ads audiences that nearly triples the coverage of the rural municipalities from 19% to 55% and makes feasible fine-grained sub-population analysis. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income. Extending the analysis to Facebook-specific user "interests" and behaviors, we provide further insights on the divide, for instance, finding that rural areas show a higher interest in gambling. Notably, we find that the most predictive features of income in rural areas differ from those for urban centres, suggesting researchers need to consider a broader range of attributes when examining rural wellbeing. The findings of this study illustrate the necessity of improving existing tools and methodologies to include under-represented populations in digital demographic studies -the failure to do so could result in misleading observations, conclusions, and most importantly, policies.

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Section: Introductionmentioning

confidence: 99%

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Rama

Mejova

Tizzoni

et al. 2020

Proceedings of the Web Conference 2020

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“…9 Small area data are more challenging to work with than are larger-scale data because of changing boundaries, lack of historical patterns of change to serve as a basis for estimation, reliability issues, and location-specific factors that can greatly affect calculations. 10,11 The team encountered many of these issues during the pilot project. Several main challenges emerged throughout the process, including during geocoding and assessing aggregation needs.…”

Section: Lessons Learnedmentioning

confidence: 99%

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

Werner

Strosnider

Kassinger

et al. 2018

Journal of Public Health Management and Practice

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National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

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“…Small area analyses face a number of unique challenges not commonly encountered at larger geographic scales: (1) the boundaries of small areas often change over time making timeseries analyses challenging; (2) many types of data, especially those covering more detailed population characteristics, are not tabulated for smaller areas, necessitating the use of proxy variables; (3) because of a paucity of data, there are often no discernible past patterns of change that can serve as a basis for estimation or projection, which may require the application of model rates based on areas for which data are available but which may not be directly comparable; (4) even when data are available for small areas, they may be less reliable because of smaller sample sizes and greater sampling variability; and (5) location-specific factors such as institutional populations, seasonal populations, facility closings, or changes in zoning have a greater impact on population changes in small areas (Smith and Morrison, 2005;Murdock et al, 2012). Because of these challenges, population estimates and projections for small areas necessitate different sets of tools than those applied for larger areas such as states and nations.…”

Section: Smallnessmentioning

confidence: 99%

Demographic Techniques: Small-area Estimates and Projections

Rayer

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

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Challenges in the Analysis of Rural Populations in the United States

Cited by 5 publications

References 25 publications

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

Demographic Techniques: Small-area Estimates and Projections

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