The stability of geodemographic cluster assignments over an intercensal period

Singleton, Alex; Pavlis, Michail; Longley, Paul

doi:10.1007/s10109-016-0226-x

Cited by 31 publications

(40 citation statements)

References 24 publications

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“…becomes "geographically-encouraged," but not explicitly constrained or forced to respect pre-conceived geographical structure. Developments in "spatially-encouraged machine learning" will have profound consequences for geodemographics, a domain heavily involved in dimension reduction in cases where a compromise between geographical regularity, temporal stability, and feature homogeneity can be difficult to strike (Voas and Williamson 2001;Singleton and Spielman 2014;Singleton, Pavlis, et al 2016), as well as other urban data science topics. Clustering the results of a hybrid geographical-manifold dimension reduction, where nearby observations are both similar and feature-near, may yet blend the dichotomy between spatial constraint and feature optimality (Knaap et al 2019).…”

Section: Resultsmentioning

confidence: 99%

“…A widespread practice in the literature is the application of ML methods to reduce multivariate neighborhood data into a smaller, more tractable and interpretable dataset. Such dimensionality reduction techniques have been common in the urban social sciences for decades, with sociologists gravitating toward factor analysis in the form of "ecometrics" (Raudenbush and Sampson 1999;O'Brien et al 2015) and geographers favoring cluster analysis often termed "geodemographics" (Singleton, Pavlis, et al 2016;Knaap et al 2019) but rarely has either of these traditions attempted to incorporate spatial information into the modeling frameworks 1 . Thus while the longevity of the neighborhood analysis literature provides strong evidence for the value of classic and emerging aspatial machine learning methods, the fact that they lack consideration of geography's first law suggests there is a wide berth for improvement (Tobler 1970).…”

Section: Introduction: Manifold Learning In Geographymentioning

confidence: 99%

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Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Wolf¹,

Knaap²

2019

Preprint

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Dimension reduction is one of the oldest concerns in geographical analysis. Despite significant, longstanding attention in geographical problems, recent advances in non-linear techniques for dimension reduction, called manifold learning, have not been adopted in classic data-intensive geographical problems. More generally, machine learning methods for geographical problems often focus more on applying standard machine learning algorithms to geographic data, rather than applying true "spatially-correlated learning," in the words of Kohonen. As such, we suggest a general way to incentivize geographical learning in machine learning algorithms, and link it to many past methods that introduced geography into statistical techniques. We develop a specific instance of this by specifying two geographical variants of Isomap, a non-linear dimension reduction, or "manifold learning," technique. We also provide a method for assessing what is added by incorporating geography and estimate the manifold's intrinsic geographic scale. To illustrate the concepts and provide interpretable results, we conducting a dimension reduction on geographical and high-dimensional structure of social and economic data on Brooklyn, New York. Overall, this paper's main endeavor--defining and explaining a way to "geographize" many machine learning methods--yields interesting and novel results for manifold learning the estimation of intrinsic geographical scale in unsupervised learning.

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Section: Resultsmentioning

confidence: 99%

Section: Introduction: Manifold Learning In Geographymentioning

confidence: 99%

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Wolf¹,

Knaap²

2019

Preprint

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“…Various current research trends are discernible in geodemographic classification, of which we note increasing variety of input data, creation of open classifications (Singleton and Spielman 2014;Singleton et al 2016) and an increasing interest in aspects of temporality. Longley and Adnan (2016), for example, explore the creation of a geodemographic classification based on Twitter usage, making the important observation that these data are no longer tied only to residential locations.…”

Section: Reviewmentioning

confidence: 99%

Developing a National Geodemographic Classification of Workplace Zones

Cockings

Martin

Harfoot

2020

Appl. Spatial Analysis

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Geodemographics conventionally refers to the classification of geographical areas based on the socioeconomic characteristics of their residents. In this paper, we develop the novel concept of a classification based on the characteristics of workers and workplaces. The paper describes the implementation of this concept at the small area level for the whole of the UK, which has involved reconciliation of three slightly different national censuses. It presents a summary of the resulting classification (a Classification of Workplace Zones for the UK (COWZ-UK)) and an innovative validation exercise based on comparison with a very large digital mapping dataset containing specific workplace locations. The openly available classification provides important new insights into the characteristics of workers and workplaces at the small area level across the UK, which will be useful for analysts in a range of sectors, including health, local government, transport and commerce. The generic concept of a classification based on the characteristics of workers and workplaces within a set of workplace zones is transferable to other countries, with refinement to reflect context- and country-specific phenomena. The concept can be readily implemented by census agencies or other data providers where individual level worker and workplace data are available.

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“…While these are just now coming within reach for advanced statistical studies (Bradley et al, 2017), the extent to which these regions represent intelligible socially-experienced geographies is currently unknown. Thus, while some analyses do aim to critically consider uncertainties and measurement (Harris et al, 2007;Gale and Longley, 2013;Singleton et al, 2016;Knaap, 2017), practical consideration of the uncertain structure of urban regions in this literature is surprisingly rare given the issue's longstanding theoretical attention.…”

Section: The Fuzzy Urban Regionmentioning

confidence: 99%

The Dynamics of Urban Neighborhoods: A Survey of Approaches for Modeling Socio-Spatial Structure

Knaap¹,

Wolf²,

Rey³

et al. 2019

Preprint

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For close to a century, researchers from across the disciplines of Urban Studies have developed empirical models for understanding the spatial extent and social composition of urban neighborhoods--and how these dimensions change over time. Unfortunately, however, these techniques have often been developed within disciplinary silos and without broad exposure to other potentially interested constituencies. In this paper, we traverse the literatures of social science, computer science, and statistics to examine a variety of modeling techniques for understanding neighborhood dynamics. We begin our review by examining early concepts of spatial structure first outlined in the Chicago School and discuss how the notions of social ecology and quantitative neighborhood analysis permeated the urban studies for several decades to come. Our survey continues by reviewing contemporary statistical approaches for identifying urban neighborhoods, culminating with the state of the art in subfields known as `geodemographics' and `regionalization'. Following this review, we offer insight into the field's persistent conceptual issues, identify areas ripe for additional research, and highlight newly-developed computational methods that can inform more just and socially equitable public policy, community development, and accountable governance.

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The stability of geodemographic cluster assignments over an intercensal period

Cited by 31 publications

References 24 publications

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

Developing a National Geodemographic Classification of Workplace Zones

The Dynamics of Urban Neighborhoods: A Survey of Approaches for Modeling Socio-Spatial Structure

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