Rationalizing Rural Area Classifications for the Economic Research Service A Workshop Summary (2016) / Chapter Skim
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7 Changes in Social Science Data and Methods
7 Changes in Social Science Data and Methods
Pages 97-108
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prepared a commissioned paper, Evolving Spatial Analytics and Rural Area Classification, for the workshop. He summarized changing analytical possibilities, including Geographic Information System and spatial statistics, and increasingly powerful computing and advancement of technology.
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Spatial Analytics Murray defined spatial analytics as any of the quantitative methods to support analysis, policy, planning, and management involving geographic space. They support the systematic analysis of geographic data and are similar to and consistent with definitions of quantitative geography and geocomputation.
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Murray said that data in a digital environment comes from some source by some process. It has data quality issues in terms of the attributes derived and in terms of the positional accuracy.
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Murray closed by saying that sources of data, particularly spatial data and spatial analytics, have evolved over time and are very promising. However, many errors and uncertainty remain with little understanding of their implications for studying issues in rural areas or urban areas.
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Urban counties were defined as counties with more than 90 percent of the population in urban areas, an urban population of at least 50,000, and a population density of more than 500 people per square mile. He also defined mixed urban and mixed rural counties as those that were in between.
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Winkler pointed out there are other opportunities and alternative data sources to the ACS. Winkler discussed information on the geographic units and the available variables of interest in the ACS, and the margins of error associated with ACS data and how they vary for different geographies and variables.
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files come out of an agreement between the transportation planning community and the Census Bureau. Winkler identified two critical temporal issues to consider to use ACS for rural area classification: the residence rule and the timing of counting people in the ACS, which is different than the decennial census.
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to demonstrate data quality for three key variables: population estimates, percent in-migration from a metro county, and percent who commute to a metropolitan county or a micropolitan city for work. She said that for the population estimates, there were not any geographic units that displayed low reliability (CV > 40)
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STATEMENT BY MICHAEL RATCLIFFE Ratcliffe noted that Mark Perry, Census Bureau, collaborated with him to prepare this presentation about frequent availability of local-level data at lower levels of geography and geographic scale. The history of urban-rural classifications and especially the Census Bureau's urban-rural classifications since the late 19th century has been one of response to improvements in spatial resolution of data, increased amounts of data, and improved technology.
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impervious surface layer as a proxy to identify nonresidential urban land uses. Other Datasets for Defining Rural and Urban There are various other datasets, Radcliffe pointed out, as discussed throughout the workshop.
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OPEN DISCUSSION Michael Partridge referred to Isserman's classification and said for the kinds of analysis he does, it is conceptually correct to use nonmetropolitan as rural. In his studies, he said, rural is where the people are not functionally integrated with an urban center.
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Related to employment data, there are differential coverage rates across time and jurisdictions from Bureau of Economic Analysis versus Bureau of Labor Statistics data. The coverage fluctuates.
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