The Cartography of Opportunity: Spatial Data Science for Equitable Urban Policy

Knaap, Elijah

doi:10.1080/10511482.2017.1331930

Cited by 17 publications

(19 citation statements)

References 51 publications

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“…The finer the spatial data, the greater the possibilities for analyzing various scales, starting with exploratory analysis. Mapping sociospatial inequality using microgeographic data makes it possible to reveal and investigate smallscale spatial patterns (vom Berge et al 2014), whereas larger scales remain important for mapping spatial opportunity structure (Knaap 2017). More accurate geographic data provide information on both the microlocations where exposure to other people starts (around one's home) and how the population to which individuals are potentially exposed to changes in continuous space.…”

mentioning

confidence: 99%

Multiscale Measures of Population: Within- and between-City Variation in Exposure to the Sociospatial Context

Petrović

Ham

Manley

2018

Annals of the American Association of Geographers

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mentioning

confidence: 99%

Multiscale Measures of Population: Within- and between-City Variation in Exposure to the Sociospatial Context

Petrović

Ham

Manley

2018

Annals of the American Association of Geographers

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“…GIS has not been sufficiently reconciled with neighbourhood effects studies. An exception is the work of Knaap (2017), who mapped the spatial opportunity structure to link the geography of opportunity with the mechanisms of neighbourhood effects. GIS expresses geography as a series of layers, capturing unique but related features.…”

Section: The Role Of Microgeographic Data In Future Contextual Effmentioning

confidence: 99%

Freedom from the tyranny of neighbourhood: Rethinking sociospatial context effects

Petrović

Manley

Ham

2019

Progress in Human Geography

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Theory behind neighbourhood effects suggests that people’s spatial context potentially affects individual outcomes across multiple scales and geographies. We argue that neighbourhood effects research needs to break away from the ‘tyranny’ of neighbourhood and consider alternative ways to measure the wider sociospatial context of people, placing individuals at the centre of the approach. We review theoretical and empirical approaches to place and space from diverse disciplines, and explore the geographical scopes of neighbourhood effects mechanisms. Ultimately, we suggest how microgeographic data can be used to operationalise sociospatial context, where data pragmatism should be supplanted by a theory-driven data exploration.

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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%

“…Substantively, this push is driven by two social trends. The first is a growing recognition of the importance and pervasiveness of "neighborhood effects" in shaping social inequality and helping to produce a wide variety of stratified outcomes in areas like health (Diez Roux, 2001;Diez Roux and Mair, 2010), educational attainment (Garner and Raudenbush, 1991;Burdick-Will et al, 2010), cognitive development (Sampson et al, 2008;Sharkey and Elwert, 2011), employment (Mendenhall et al, 2006;Galster, 2017), and economic mobility (Chetty et al, 2014(Chetty et al, , 2015, among a wide variety of others (Sampson et al, 2002;Sampson, 2012a;Galster, 2012;Sharkey and Faber, 2014;Knaap, 2017;Galster and Sharkey, 2017). The second is the rise of "data science," and computational research methods, particularly the growing subfield of geographic or spatial data science, and the increasing adoption of advanced quantitative techniques for studying urban areas.…”

Section: Introductionmentioning

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 Cartography of Opportunity: Spatial Data Science for Equitable Urban Policy

Cited by 17 publications

References 51 publications

Multiscale Measures of Population: Within- and between-City Variation in Exposure to the Sociospatial Context

Multiscale Measures of Population: Within- and between-City Variation in Exposure to the Sociospatial Context

Freedom from the tyranny of neighbourhood: Rethinking sociospatial context effects

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

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