Ranking places in attributed temporal urban mobility networks

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can furt...

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Bibliographic Details
Published in:PloS one 2020-10, Vol.15 (10), p.e0239319-e0239319
Main Authors: Nanni, Mirco, Tortosa, Leandro, Vicent, José F, Yeghikyan, Gevorg
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
Cities
Computer and Information Sciences
Computer science
Earth Sciences
Economic conditions
Economics
Evolution
Geospatial data
Graph theory
Graphical representations
Heterogeneity
Medicine and Health Sciences
Mobility
Nodes
Physical Sciences
Research and Analysis Methods
Social mobility
Social networks
Social Sciences
Socioeconomics
Urban areas
Visualization
Workflow
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Summary:Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0239319