Ranking places in attributed temporal urban mobility networks

Abstract

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 socioeconomic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socioeconomic 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 socioeconomic 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.

A Group-Based Centrality for Undirected Multiplex Networks: A Case Study of the Brazilian Car Wash Operation

“…We also find strategy proposals for specific goals like classifying nodes in urban mobility networks [Nanni et al, 2020]. For such a use-case, the authors locate places in a city by designing weighted directed graphs whose nodes denote city locations, and weighted edges represent the number of trips between them.…”

Recurrent origin–destination network for exploration of human periodic collective dynamics

“…Also, the geographical meaning of a node is required for the construction of the OD network. We use the most common uniform grid measuring 1 km × 1 km as the unit region (Nanni, Tortosa, Vicent, & Yeghikyan, 2020), while the scale size can be adapted according to the applications. Also, a non-uniform partition such as traffic analysis zones can be applied if the information is available (Yu, 2019).…”