Clustering moves: Spatial network analysis in residential mobility

In the past sixty years, network analysis has become a valuable analytic tool in the study of interpersonal relationships and also in the construction of spatial relationships within urban geographies. Researchers have applied network analysis in a wide array of spatial applications; however, the power of network analysis has yet to be applied to understanding patterns in residential mobility. Residential movement patterns take place over longer time scales and potentially greater distances than daily journeys or commutes, but equally create networks connecting places and people. This research demonstrates how applying network analysis to residential moves may lend insight to open questions in the study of residential mobility, using fine-grained data on residential moves held by the city of Zurich, Switzerland. By creating a weighted directed network linking locations on a 400 meter coordinate grid with residents moving between them, the work demonstrates that network analysis can reveal geographies that overwrite the political boundaries commonly used to categorize residential spaces in the city. The new geographies produced by this method capture a wholistic picture of moving behaviour independent of reported residential choices. By tracking completed moves rather than stated aims, the uncovered mobility geographies sidestep the common categorisation of voluntary versus structurally determined residential choices, aggregating accumulated choices to demonstrate some of the geographic specificities of residential moving behaviour.

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Publication Details

Journal
Environment and Planning B Urban Analytics and City Science
Published
2024-12-19
DOI
https://doi.org/10.1177/23998083241309992
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
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article

Clustering moves: Spatial network analysis in residential mobility

Susannah Cramer-Greenbaum
Environment and Planning B Urban Analytics and City Science
Human Mobility and Location-Based Analysis
article

Clustering moves: Spatial network analysis in residential mobility

Susannah Cramer-Greenbaum
article en

Abstract

In the past sixty years, network analysis has become a valuable analytic tool in the study of interpersonal relationships and also in the construction of spatial relationships within urban geographies. Researchers have applied network analysis in a wide array of spatial applications; however, the power of network analysis has yet to be applied to understanding patterns in residential mobility. Residential movement patterns take place over longer time scales and potentially greater distances than daily journeys or commutes, but equally create networks connecting places and people. This research demonstrates how applying network analysis to residential moves may lend insight to open questions in the study of residential mobility, using fine-grained data on residential moves held by the city of Zurich, Switzerland. By creating a weighted directed network linking locations on a 400 meter coordinate grid with residents moving between them, the work demonstrates that network analysis can reveal geographies that overwrite the political boundaries commonly used to categorize residential spaces in the city. The new geographies produced by this method capture a wholistic picture of moving behaviour independent of reported residential choices. By tracking completed moves rather than stated aims, the uncovered mobility geographies sidestep the common categorisation of voluntary versus structurally determined residential choices, aggregating accumulated choices to demonstrate some of the geographic specificities of residential moving behaviour.

Environment and Planning B Urban Analytics and City ScienceVol. 52(7)
ETH Zurich (CH), University College London (GB)
Openalex Percentile: Top 79%
Human Mobility and Location-Based Analysis
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Clustering moves: Spatial network analysis in residential mobility — Susannah Cramer-Greenbaum · Environment and Planning B Urban Analytics and City Science (2024) | TGRS Research Map | TGRS