Suchergebnisse

La disponibilité croissante de données dans divers domaines a permis d'entrevoir ou de renouveler les approches quantitatives pour de nombreux phénomènes. Cela est particulièrement vrai pour les systèmes urbains pour lesquels différents dispositifs à différentes échelles produisent une très grande quantité de données potentiellement utiles pour construire une « nouvelle science des villes ». Un nouveau problème que nous devons résoudre est alors d'extraire des informations utiles de ces énormes ensembles de données et de construire des modèles théoriques pour expliquer les observations empiriques. Dans cet article, nous discutons une approche inspirée par la physique statistique et l'illustrons d'exemples de la répartition spatiale de l'activité dans les villes et de la mobilité urbaine. Classification JEL : C00, C18, R00.

Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- 1 From Complex to Spatial Networks -- 1.1 Early Days -- 1.2 Complex Networks -- 1.3 Space Matters -- 1.4 Definition and Representations -- 1.4.1 Spatial Networks -- 1.4.2 Representations of Networks -- 1.5 Planar Graphs -- 1.5.1 Planarity and Crossing Number -- 1.5.2 Basic Results -- 2 Irrelevant and Simple Measures -- 2.1 Irrelevant Measures -- 2.1.1 Degree -- 2.1.2 Length of Segments -- 2.1.3 Clustering, Assortativity, and Average Shortest Path -- 2.1.4 Empirical Illustrations -- 2.2 Simple Measures -- 2.2.1 Topological Indices: α and γ Indices -- 2.2.2 Organic Ratio and Ringness -- 2.2.3 Cell Areas and Shape -- 2.2.4 Route Factor, Detour Index -- 2.2.5 Cost, Efficiency, and Robustness -- 3 Statistics of Faces and Typology of Planar Graphs -- 3.1 Area and Shape of Faces -- 3.1.1 Characterizing Blocks -- 3.1.2 A Typology of Planar Graphs -- 3.2 Approximate Mapping of a Planar Graph to a Tree -- 3.3 An Exact Bijection Between a Planar Graph and a Tree -- 4 Betweenness Centrality -- 4.1 Definition of the BC -- 4.2 General Properties -- 4.2.1 Numerical Calculation: Brandes' Algorithm -- 4.2.2 The Average BC -- 4.2.3 Edge Versus Node BC -- 4.2.4 Adding Edges -- 4.2.5 Scaling of the Maximum BC -- 4.3 The Spatial Distribution of Betweenness Centrality -- 4.3.1 Regular Lattice and Scale-Free Networks -- 4.3.2 Giant Percolation Cluster -- 4.3.3 Real-World Planar Graphs -- 4.3.4 Summary: Stylized Facts -- 4.4 The BC of a Loop Versus the Center: A Toy Model -- 4.4.1 Approximate Formulas -- 4.4.2 A Transition to a Central Loop -- 4.5 The BC in a Disk: The Continuous Limit -- 5 Simplicity and Entropy -- 5.1 Simplicity -- 5.1.1 Simplest Paths -- 5.1.2 The Simplicity Index and the Simplicity Profile -- 5.1.3 A Null Model -- 5.1.4 Measures on Real-World Networks -- 5.2 Information Perspective

In empirical studies, trajectories of animals or individuals are sampled in space and time. Yet, it is unclear how sampling procedures bias the recorded data. Here, we consider the important case of movements that consist of alternating rests and moves of random durations and study how the estimate of their statistical properties is affected by the way we measure them. We first discuss the ideal case of a constant sampling interval and short-tailed distributions of rest and move durations, and provide an exact analytical calculation of the fraction of correctly sampled trajectories. Further insights are obtained with simulations using more realistic long-tailed rest duration distributions showing that this fraction is dramatically reduced for real cases. We test our results for real human mobility with high-resolution GPS trajectories, where a constant sampling interval allows one to recover at best 18% of the movements, while over-evaluating the average trip length by a factor of 2. Using a sampling interval extracted from real communication data, we recover only 11% of the moves, a value that cannot be increased above 16% even with ideal algorithms. These figures call for a more cautious use of data in quantitative studies of individuals' movements. ; R.G. has received funding from the SESAR Joint Undertaking under grant agreement no. 699260 included in the European Union's Horizon 2020 research and innovation programme. R.L. acknowledges support from the James S. McDonnell Foundation through a Postdoctoral Fellowship. ; No

© 2015 Macmillan Publishers Limited. All rights reserved. The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure. ; The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement 318367 (EUNOIA project). M.L. and J.J.R. received partial financial support from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634). The work of M.L. has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013–2017. ; Peer Reviewed

Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geolocated tweets, check-ins from Foursquare or geotagged photos, have contributed to this purpose at different scales, from cities to countries, in different world areas. Many previous works lacked, however, details on the individuals' attributes such as age or gender. In this work, we analyze credit-card records from Barcelona and Madrid and by examining the geolocated credit-card transactions of individuals living in the two provinces, we find that the mobility patterns vary according to gender, age and occupation. Differences in distance traveled and travel purpose are observed between younger and older people, but, curiously, either between males and females of similar age. While mobility displays some generic features, here we show that sociodemographic characteristics play a relevant role and must be taken into account for mobility and epidemiological modelization. ; Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017. JJR acknowledges funding from the Ramón y Cajal program of MINECO. ; Peer reviewed

Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geolocated tweets, check-ins from Foursquare or geotagged photos, have contributed to this purpose at different scales, from cities to countries, in different world areas. Many previous works lacked, however, details on the individuals' attributes such as age or gender. In this work, we analyze credit-card records from Barcelona and Madrid and by examining the geolocated credit-card transactions of individuals living in the two provinces, we find that the mobility patterns vary according to gender, age and occupation. Differences in distance traveled and travel purpose are observed between younger and older people, but, curiously, either between males and females of similar age. While mobility displays some generic features, here we show that sociodemographic characteristics play a relevant role and must be taken into account for mobility and epidemiological modelization. ; Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017. JJR acknowledges funding from the Ramón y Cajal program of MINECO. ; Peer Reviewed

The advent of geolocated information and communication technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data are scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses. ; Partial financial support was received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under project INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects LASAGNE and INSIGHT. The work of M.L. was funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational programme for 2013–2017. J.J.R. acknowledges funding from the Ramón y Cajal program of MINECO. ; Peer Reviewed

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census, and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density, and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2×2 and 1×1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered. ; Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. ML acknowledges funding from the Conselleria d'Educació, Cultura i Universitats of the Government of the Balearic Islands, and JJR from the Ramón y Cajal program of MINECO. ; Peer Reviewed