Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

Spielman, Seth E.; Singleton, Alex

doi:10.1080/00045608.2015.1052335

Cited by 74 publications

(51 citation statements)

References 52 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 stability of geodemographic cluster assignments over an intercensal period

2016

View full text Add to dashboard Cite

A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many classifications, including that developed in this paper, are created entirely from data extracted from a single decennial census of population. Such classifications are often criticised as becoming less useful over time because of the changing composition of small geographic areas. This paper presents a methodology for exploring the veracity of this assertion, by examining changes in UK census-based geodemographic indicators over time, as well as a substantive interpretation of the overall results. We present an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Through this classification, we examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period.

show abstract

Section: Ancillary Secondary Data As Indicators Of Likely Geodemograpmentioning

confidence: 99%

The stability of geodemographic cluster assignments over an intercensal period

2016

View full text Add to dashboard Cite

show abstract

“…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%

Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities

Lee

Rodríguez‐Pose

2016

Annals of the American Association of Geographers

View full text Add to dashboard Cite

High-technology industries are seen as important in helping urban economies thrive, but at the same time they are often considered as potential drivers of poverty and social exclusion. However, little research has assessed how high-tech affects urban poverty and the wages of workers at the bottom of the pyramid. This paper addresses this gap in the literature and investigates the relationship between employment in high-tech industries, poverty and the labour market for non-degree educated workers using a panel of 295 Metropolitan Statistical Areas (MSAs) in the United States between 2005 and 2011. The results of the analysis show no real impact of the presence of high-technology industries on poverty. Yet there is strong evidence that tech-employment increases wages for nondegree educated workers and, to a lesser extent, employment for those without degrees. These results suggest that while tech employment has some role in improving welfare for non-degree educated workers, tech-employment alone is not be enough to reduce poverty.

show abstract

“…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%

A Principal Component Analysis (PCA)-based framework for automated variable selection in geodemographic classification

Liu

Singleton

Arribas‐Bel

2019

Geo-spatial Information Science

Self Cite

View full text Add to dashboard Cite

A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography. However, such representations are influenced by the methodological choices made during their construction. Of particular debate are the choice and specification of input variables, with the objective of identifying inputs that add value but also aim for model parsimony. Within this context, our paper introduces a principal component analysis (PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of variables. The proposed methodology is exemplified in the context of variables from the UK 2011 Census, and its output compared to the Office for National Statistics 2011 Output Area Classification (2011 OAC). Through the implementation of the proposed methodology, the quality of the cluster assignment was improved relative to 2011 OAC, manifested by a lower total withincluster sum of square score. Across the UK, more than 70.2% of the Output Areas (OAs) occupied by the newly created classification (i.e. AVS-OAC) outperform the 2011 OAC, with particularly strong performance within Scotland and Wales.

show abstract

Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

Cited by 74 publications

References 52 publications

The stability of geodemographic cluster assignments over an intercensal period

The stability of geodemographic cluster assignments over an intercensal period

Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities

A Principal Component Analysis (PCA)-based framework for automated variable selection in geodemographic classification

Contact Info

Product

Resources

About