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Title: Operationalizing SPlaces
Citation Type: Book, Section
Publication Year: 2023
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ISSN:
DOI: 10.1007/978-3-031-24857-3_2
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Abstract: In Chap. 1 we described spaces as planes with mathematical properties that allow us to identify objects located in those planes. We further identified these objects as places with meaning that we may experience, live, and remember. Subsequently, we defined splaces as the intermingling of spaces and spaces that may enrich our understandings of processes, mechanisms, and the explanations of the outcomes observed. From this view, splaces then, eased our discussion of geographies of opportunity and disopportunity, feedback loops, sorting or self-selection, and multicollinearity issues. We, however, have so far limited these discussions to conceptual understandings and have yet to discuss and showcase how to operationalize these splaces. Accordingly, the purpose of this chapter is to introduce and apply these concepts into practical applications using real and publicly available data. To this end, we will discuss how to delimit and operationalize splaces with particular emphasis on data format structures and sources. We will also discuss tradeoffs associated with zooming in (i.e., going from higher level areas [counties] to lower level areas [census tracts]) into splaces, which, although may result in data points gains, may be computationally expensive. Finally, we close this chapter with examples of attributes readily available at the American Community Survey (ACS) that can be used to operationalize concentrated disadvantages.
Url: https://link.springer.com/chapter/10.1007/978-3-031-24857-3_2
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Authors: González Canché, Manuel S.
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Pages: 25-54
Volume Title: Spatial Socio-econometric Modeling (SSEM)
Publisher: Springer, Cham
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Data Collections: IPUMS USA
Topics: Methodology and Data Collection
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