Redrawing the Map of Great Britain from a Network of Human Interactions

By Carlo Ratti, Stanislav Sobolevsky, Francesco Calabrese, Clio Andris, Jonathan Reades, Mauro Martino, Rob Claxton, Steven H. Strogatz. PLoS ONE, 2010

412 citations recorded by the source corpus.

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

Subfields: Economics and Econometrics, Sociology and Political Science, Transportation

View Paper: https://www.semanticscholar.org/paper/ab06765ddc41ae8b9ebeacb6e2a7f6080988f150

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Citations in the corpus

Detecting the dynamics of urban structure through spatial network analysis (2014, background): “2012), telephone data sets at national scales (Ratti et al .”
Uncovering space-independent communities in spatial networks (2010, background): “…economical or administrative borders based on human mobility or economical interactions instead of historical or ad-hoc reasons [36, 37, 38, 39]; ii) the modeling of historical or pre-historical interactions based on limited archaeological evidence [40, 41]; iii) the identiﬁcation of…”
Geo-located Twitter as proxy for global mobility patterns (2013, background): “Furthermore, the finding that clusters were spatially continuous and reflected common world regions is in line with the findings of previous studies with mobile phone data (Blondel 2011; Blondel, Krings, and Thomas 2010; Ratti et al. 2010; Sobolevsky, Szell, et al. 2013).”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “Recently, much literature has introduced community detection methods to regionalize a study area [52,57].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “…patterns (Gonz´alez et al., 2008; Isaacman et al., 2010), the use of urban spaces (Reades et al., 2007), travel demand during special events (Calabrese et al., 2010), social network structure (Onnela et al., 2007) and geographical dispersal of mobile communications (Lambiotte et al., 2008).”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “Many researchers have applied community detection and other network science methods on a national scale trying to determine whether existing administrative boundaries are still reasonable (Ratti et al., 2010; Thiemann et al., 2010; Liu et al., 2014), exploring the relations between commuting propert”
Discovering Urban Functional Zones Using Latent Activity Trajectories (2015, methodology): “, spectral clustering), however, employed interaction data, such as economic transactions, communication records [31] and people’s movement between regions.”