Ranking places in attributed temporal urban mobility networks.

Mirco Nanni,Gevorg Yeghikyan,José F Vicent,Leandro Tortosa,Alireza Abbasi

doi:10.1371/journal.pone.0239319

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 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.

Highlights

The ever-growing availability of large scale data sources pertaining to human activities in contemporary cities and the fact that the socio-economic and technological systems lend themselves adequately to representation through discrete elements and interactions between them have led recent years to witness an unprecedented increase in modelling of such complex systems using network theory [1].In urban science, there has been a significant research interest towards understanding urban systems through modelling road structures, human mobility, traffic flow, and economic activity through a complex networks approach [2,3,4]
We focus on the spatial arrangement of the most central nodes of the OD network as identified by the Adapted PageRank Algorithm (APA) [17] considering activity related to food and retail services over time in Rome and London
After the city territories have been tessellated into 1x1km grid cells, the raw GPS data has been processed, trip origins and destinations have been extracted and the OD networks have been built for each hour of the day both in London and Rome, we proceed to computing the location centralities with the Adapted PageRank Algorithm

Summary

Introduction

The ever-growing availability of large scale data sources pertaining to human activities in contemporary cities and the fact that the socio-economic and technological systems lend themselves adequately to representation through discrete elements and interactions between them have led recent years to witness an unprecedented increase in modelling of such complex systems using network theory [1]. Since many kinds of real-world networks call for such node attributes, several centrality measures have recently been proposed extending the widely used centrality measures to accommodate node attributes [17,18,19] This becomes especially relevant in urban modelling, as locations in a city possess quantitative and qualitative characteristics irrespective of the connectivity structure of the network of interactions with other locations. The temporal evolution of this interplay remains an unexplored area of research Having this gap as motivation, the objectives of this paper are to analyse and study the spatial distribution of the central nodes by activity type over time in urban origin-destination (OD) networks.

Related work

Findings

Conclusion