2015
DOI: 10.1080/00045608.2015.1052335
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Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

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Cited by 74 publications
(51 citation statements)
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References 52 publications
(46 reference statements)
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“…For example, there is a notable absence of data about travel behaviour or occupation. However, in the future, there might be potential to use surveys or even social media data to create small area estimates, although the former approach raises issues of generalisation between spatial scales (Spielman and Singleton 2015) and the latter on representativeness (Arribas-Bel 2014).…”
Section: Ancillary Secondary Data As Indicators Of Likely Geodemograpmentioning
confidence: 99%
“…For example, there is a notable absence of data about travel behaviour or occupation. However, in the future, there might be potential to use surveys or even social media data to create small area estimates, although the former approach raises issues of generalisation between spatial scales (Spielman and Singleton 2015) and the latter on representativeness (Arribas-Bel 2014).…”
Section: Ancillary Secondary Data As Indicators Of Likely Geodemograpmentioning
confidence: 99%
“…The information is accessed via the IPUMS service at the University of Minnesota (Ruggles et al, 2010). The ACS is a large-scale annual survey of US households with a sample of more than 2 million individuals each year -essentially a (weighted) sample of 1 in 100 of the population (for more information on its strengths and limitations, see Spielman and Singleton, 2015). For this study, we use the microdata and construct indicators at the Metropolitan Statistical Area (MSA) level.…”
Section: The Datamentioning
confidence: 99%
“…The theoretical tenet of geodemographic classification relates to the principle of homophily, which in geographic terms is the tendency for individuals to be attracted to areas that contain others with similar characteristics to themselves (Sleight 1993;Webber and Craig 1978). As such, the methodological objective when creating a geodemographic classification is, therefore, to sort a set of small areas into clusters that share similar characteristics, with the output of such groupings providing a simplified and categorical representation of the overarching multidimensional geography (Spielman and Singleton 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Taking such perspectives into consideration, a typical objective of variable selection is therefore to achieve input parsimony, that is, the identification of the smallest subset of input variables that capture the most variation within the original dataset (Debenham 2002;Gale et al 2016;Harris, Sleight, and Webber 2005). This will typically be achieved by balancing both the theoretical and empirical rationale for variable inclusion (Spielman and Singleton 2015). For example, it is common that initial inputs are presented within a framework that draws upon wider literature, guiding the type and balance between different potential influences upon or outcomes of area differentiation.…”
Section: Introductionmentioning
confidence: 99%