Ranking places in attributed temporal urban mobility networks

Nanni M., Tortosa L., Vicent J. F., Yeghikyan G.

Geography  Hotspots  Research and Analysis Methods  Science  Commerce  Spatial Autocorrelation  Mathematics  Ciencia de la Computación e Inteligencia Artificial  Retail  Q  R  Human Mobility  Statistical Distributions  Network Analysis  Patterns  Multidisciplinary  Spatio-temporal heterogeneity  Research Article  Food  Economics  Socio-economic activity  Earth Sciences  Social Sciences  Nutrition  Centrality  Data Management  Medicine and Health Sciences  Simulation and Modeling  Probability Theory  Computer and Information Sciences  Algorithm  Medicine  Urban mobility  Physical Sciences  Algorithms  Biology and Life Sciences  Diet  Temporal OD networks  Human Geography  Origin-destination (OD) matrices  Data Visualization  Applied Mathematics  Streets  Geoinformatics 

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.

Source: PloS one 15 (2020). doi:10.1371/journal.pone.0239319

Publisher: Public Library of Science, San Francisco, CA , Stati Uniti d'America

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