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 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.

[1]  Alessandro Vespignani,et al.  The Structure of Interurban Traffic: A Weighted Network Analysis , 2005, physics/0507106.

[2]  Eleni I. Vlahogianni,et al.  Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities , 2015 .

[3]  Mohsen Ramezani,et al.  Revealing latent characteristics of mobility networks with coarse-graining , 2019, Scientific Reports.

[4]  Enrique Frías-Martínez,et al.  Uncovering the spatial structure of mobility networks , 2015, Nature Communications.

[5]  Yuren Zhou,et al.  Understanding Urban Human Mobility through Crowdsensed Data , 2018, IEEE Communications Magazine.

[6]  Xiang Li,et al.  Detecting and Analyzing Mobility Hotspots using Surface Networks , 2014, Trans. GIS.

[7]  Christine Largeron,et al.  Centrality for graphs with numerical attributes , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[8]  Dietmar Plenz,et al.  powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions , 2013, PloS one.

[9]  V. Latora,et al.  The Network Analysis of Urban Streets: A Primal Approach , 2006 .

[10]  Gourab Ghoshal,et al.  From the betweenness centrality in street networks to structural invariants in random planar graphs , 2017, Nature Communications.

[11]  T. V. Ramachandra,et al.  Insights to urban dynamics through landscape spatial pattern analysis , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[13]  Jianping Wu,et al.  Traffic speed prediction using deep learning method , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[14]  Leandro Tortosa,et al.  An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector , 2012, Appl. Math. Comput..

[15]  Yunhao Liu,et al.  Spatio-temporal analysis and prediction of cellular traffic in metropolis , 2017, 2017 IEEE 25th International Conference on Network Protocols (ICNP).

[16]  Enrique Frías-Martínez,et al.  Comparing and modelling land use organization in cities , 2015, Royal Society Open Science.

[17]  N. Taleb,et al.  Gini Estimation Under Infinite Variance , 2017, Physica A: Statistical Mechanics and its Applications.

[18]  Feng Mao,et al.  Mining spatiotemporal patterns of urban dwellers from taxi trajectory data , 2016, Frontiers of Earth Science.

[19]  V. Latora,et al.  Street Centrality and Densities of Retail and Services in Bologna, Italy , 2009 .

[20]  M. Barthelemy,et al.  The spatial organization of the population density in cities , 2018, 1804.00855.

[21]  Alex Bavelas,et al.  Communication Patterns in Task‐Oriented Groups , 1950 .

[22]  Leandro Tortosa,et al.  New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks , 2016, Appl. Math. Comput..

[23]  Harvey J. Miller,et al.  Time-space transformations of geographic space for exploring, analyzing and visualizing transportation systems , 2007 .

[24]  Denise Pumain,et al.  From theory to modelling: urban systems as complex systems , 2006 .

[25]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[26]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[27]  Franco Scarselli,et al.  Inside PageRank , 2005, TOIT.

[28]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[29]  Dino Pedreschi,et al.  Discovering the Geographical Borders of Human Mobility , 2012, KI - Künstliche Intelligenz.

[30]  Yu-Ru Lin,et al.  Mesoscopic Structure and Social Aspects of Human Mobility , 2012, PloS one.

[31]  Stephen Law,et al.  Connected or informed?: Local Twitter networking in a London neighbourhood , 2015, Big Data Soc..

[32]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[33]  Charo I. del Genio,et al.  Analysis of the communities of an urban mobile phone network , 2017, PloS one.

[34]  Wei-Chien-Benny Chin,et al.  Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network , 2015, PloS one.

[35]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[36]  K. Arrow,et al.  The New Palgrave Dictionary of Economics , 2020 .

[37]  F. Pedroche,et al.  MÉTODOS DE CÁLCULO DEL VECTOR PAGERANK , 2008 .

[38]  Yixiang Chen,et al.  A trajectory clustering approach based on decision graph and data field for detecting hotspots , 2017, Int. J. Geogr. Inf. Sci..

[39]  Neeraj Tiwari,et al.  Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic , 2006, International Journal of Health Geographics.

[40]  Pavel Berkhin,et al.  A Survey on PageRank Computing , 2005, Internet Math..

[41]  V. Latora,et al.  Predicting Urban Innovation from the Workforce Mobility Network in US , 2019, 1911.00436.

[42]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[43]  L. Bettencourt,et al.  A unified theory of urban living , 2010, Nature.

[44]  Padhraic Smyth,et al.  Modeling human location data with mixtures of kernel densities , 2014, KDD.

[45]  R. Ahas,et al.  Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data , 2010 .

[46]  Jean-Claude Thill,et al.  Comparison of planar and network K-functions in traffic accident analysis , 2004 .

[47]  Sergio J. Rey,et al.  A spatial decomposition of the Gini coefficient , 2013 .

[48]  Hani S. Mahmassani,et al.  A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks , 2017 .

[49]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[50]  M. Barthelemy,et al.  From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.

[51]  Jiwon Kim,et al.  Identification of communities in urban mobility networks using multi-layer graphs of network traffic , 2017 .

[52]  Qingyun Du,et al.  A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China , 2015 .

[53]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .