Structure and information in spatial segregation

Chodrow, Philip S.

doi:10.1073/pnas.1708201114

Cited by 57 publications

(59 citation statements)

References 36 publications

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“…The attribute affinity structure may be a straightforward attribute kernel (e.g. Gaussian, bisquare, or triangular kernels) or may reflect various compositional affinities using more complicated divergence measures (Chodrow 2017). Regardless, when combined, typical spectral clustering methods can be applied to and clusters of varying spatial cohesion may be obtained.…”

Section: Discussionmentioning

confidence: 99%

“…As a clustering technique, it has often been applied to graph-embedded data (White and Smyth 2005), and recently been applied to questions of D R A F T geodemographic analysis of segregation and sorting (Chodrow 2017). The spectral analysis of geographic data is not a new concept in quantitative spatial science (Tobler 1966).…”

Section: Introductionmentioning

confidence: 99%

“…Recent methods, such as local information scoring (Chodrow 2017) follow in a long line of post-hoc measures of attribute separation between identified clusters (Rousseeuw 1987). In this way, most measures of attribute similarity or spatial contiguity are either the byproduct of cluster fitting or are computed about solutions after their fit.…”

Section: Introductionmentioning

confidence: 99%

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Spatially-encouraged spectral clustering: a technique for blending map typologies and regionalization

Wolf¹

2018

Preprint

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Spatially-constrained clustering is a central concern in geographic data science. It finds applications in epidemiology, sociological neighborhood study, criminology, operations research, statistics, and econometrics, to name a few. One recently-developed method provides a powerful technique that ostensibly allows contiguity and attribute similarity to be balanced. Using a single parameter, this method suggests that geography and feature information can be balanced to generate regions that compromise on geographic regularity and feature coherence. In this paper, I investigate a free parameter omitted in their discussion. I find this free parameter has a significant impact on the solution structure; in addition to the single parameter considered therein, this parameter also affects the balance of contiguity and attribute similarity in identified clusters. This is because what the technique uses are two separate kernels -one for spatial similarity and one for attribute similarity. Both kernels affect solution quality. Exploiting this realization, I create a generalization that leverages this behavior and apply it in two empirical examples with data of differing spatial support. This generalization, called "spatially-encouraged spectral clustering," embeds the fact that spatial and attribute information can be combined in a variety of ways, and parameters must be available for both sets of information in order to use them effectively in the quintessential geographic data science problem of cluster detection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatially-encouraged spectral clustering: a technique for blending map typologies and regionalization

Wolf¹

2018

Preprint

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“…We however recall that the notion of multi-scalar process is otherwise very broad, and can manifest itself in scaling laws for example (West, 2017). An approach closer to the one we took is given by Chodrow (2017) which measures intrinsic scales to segregation phenomenons by using measures from Information Theory. We also propose to explore the possible property of multi-scalar processes by the extraction of endogenous scales in the data.…”

Section: Variations Of the Estimated Correlationsmentioning

confidence: 99%

An Urban Morphogenesis Model Capturing Interactions Between Networks and Territories

Raimbault¹

2019

The Mathematics of Urban Morphology

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Urban systems are composed by complex couplings of several components, and more particularly between the built environment and transportation networks. Their interaction is involved in the emergence of the urban form. We propose in this chapter to introduce an approach to urban morphology grasping both aspects and their interaction. We first define complementary measures, study their empirical values and their spatial correlations on European territorial systems. The behavior of indicators and correlations suggest underlying non-stationary and multi-scalar processes. We then introduce a generative model of urban growth at a mesoscopic scale. Given a fixed exogenous growth rate, population is distributed following a preferential attachment depending on a potential controlled by the local urban form (density, distance to network) and network measures (centralities and generalized accessibilities), and then diffused in space to capture urban sprawl. Network growth is included through a multi-modeling paradigm: implemented heuristics include biological network generation and gravity potential breakdown. The model is calibrated both at the first (measures) and second (correlations) order, the later capturing indirectly relations between networks and territories.

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“…Clustering based on distributions itself is non-trivial, and the scale of our scenario adds a layer of complexity. We build upon a recent breakthrough in social segregation analysis [7] to define the weighted diversity of two distributions, and use it in a scalable two-phase process.…”

Section: Application To the France Case Studymentioning

confidence: 99%

Urban Vibes and Rural Charms: Analysis of Geographic Diversity in Mobile Service Usage at National Scale

Singh

Fiore

Marina

et al. 2019

The World Wide Web Conference

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We investigate spatial patterns in mobile service consumption that emerge at national scale. Our investigation focuses on a representative case study, i.e., France, where we find that: (i) the demand for popular mobile services is fairly uniform across the whole country, and only a reduced set of peculiar services (mainly operating system updates and long-lived video streaming) yields geographic diversity; (ii) even for such distinguishing services, the spatial heterogeneity of demands is limited, and a small set of consumption behaviors is sufficient to characterize most of the mobile service usage across the country; (iii) the spatial distribution of these behaviors correlates well with the urbanization level, ultimately suggesting that the adoption of geographically-diverse mobile applications is linked to a dichotomy of cities and rural areas. We derive our results through the analysis of substantial measurement data collected by a major mobile network operator, leveraging an approach rooted in information theory that can be readily applied to other scenarios.

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Structure and information in spatial segregation

Cited by 57 publications

References 36 publications

Spatially-encouraged spectral clustering: a technique for blending map typologies and regionalization

Spatially-encouraged spectral clustering: a technique for blending map typologies and regionalization

An Urban Morphogenesis Model Capturing Interactions Between Networks and Territories

Urban Vibes and Rural Charms: Analysis of Geographic Diversity in Mobile Service Usage at National Scale

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