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.
The street network is an important aspect of cities and contains crucial information about their organization and evolution. Characterizing and comparing various street networks could then be helpful for a better understanding of the mechanisms governing the formation and evolution of these systems. Their characterization is however not easy: there are no simple tools to classify planar networks and most of the measures developed for complex networks are not useful when space is relevant. Here, we describe recent efforts in this direction and new methods adapted to spatial networks. We will first discuss measures based on the structure of shortest paths, among which the betweenness centrality. In particular for time-evolving road networks, we will show that the spatial distribution of the betweenness centrality is able to reveal the impact of important structural transformations. Shortest paths are however not the only relevant ones. In particular, they can be very different from those with the smallest number of turns—the simplest paths. The statistical comparison of the lengths of the shortest and simplest paths provides a nontrivial and nonlocal information about the spatial organization of planar graphs. We define the simplicity index as the average ratio of these lengths and the simplicity profile characterizes the simplicity at different scales. Measuring these quantities on artificial (roads, highways, railways) and natural networks (leaves, insect wings) show that there are fundamental differences—probably related to their different function—in the organization of urban and biological systems: there is a clear hierarchy of the lengths of straight lines in biological cases, but they are randomly distributed in urban systems. The paths are however not enough to fully characterize the spatial pattern of planar networks such as streets and roads. Another promising direction is to analyze the statistics of blocks of the planar network. More precisely, we can use the conditional probability distribution of the shape factor of blocks with a given area, and define what could constitute the fingerprint of a city. These fingerprints can then serve as a basis for a classification of cities based on their street patterns. This method applied on more than 130 cities in the world leads to four broad families of cities characterized by different abundances of blocks of a certain area and shape. This classification will be helpful for identifying dominant mechanisms governing the formation and evolution of street patterns.
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
Zugriffsoptionen:
Die folgenden Links führen aus den jeweiligen lokalen Bibliotheken zum Volltext:
Urban inequality is a major challenge for cities in the 21st century. This inequality is reflected in the spatial income structure of cities which evolves in time through various processes. Gentrification is a well-known illustration of these dynamics in which the population of a low-income area changes as wealthier residents arrive and old-settled residents are expelled. Less understood but very important is the reverse process of gentrification through which areas of cities get impoverished. Gentrification has been widely studied among social sciences, especially in case studies, but there have been fewer quantitative analyses of this phenomenon, and more generally about the spatial dynamics of income in cities. Here, we first propose a quantitative analysis of these income dynamics in cities based on household incomes in 45 American and nine French Functional Urban Areas (FUA). We found that an important ingredient that determines the evolution of the income level of an area is the income level of its immediate neighboring areas. This empirical finding leads to the idea that these dynamics can be modeled by the voter model of statistical physics. We show that such a model constitutes an interesting tool for both describing and predicting evolution scenarios of urban areas with a very limited number of parameters (two for the United States and one for France). We illustrate our results by computing the probability that areas will change their income status in the case of Boston and Paris at the horizon of 2030.
The process of urbanization is one of the most important phenomenon of our societies and it is only recently that the availability of massive amounts of geolocalized historical data allows us to address quantitatively some of its features. Here, we discuss how the number of buildings evolves with population and we show on different datasets (Chicago, 1930–2010; London, 1900–2015; New York City, 1790–2013; Paris, 1861–2011) that this 'fundamental diagram' evolves in a possibly universal way with three distinct phases. After an initial pre-urbanization phase, the first phase is a rapid growth of the number of buildings versus population. In a second regime, where residences are converted into another use (such as offices or stores for example), the population decreases while the number of buildings stays approximately constant. In another subsequent phase, the number of buildings and the population grow again and correspond to a re-densification of cities. We propose a stochastic model based on these simple mechanisms to reproduce the first two regimes and show that it is in excellent agreement with empirical observations. These results bring evidences for the possibility of constructing a minimal model that could serve as a tool for understanding quantitatively urbanization and the future evolution of cities.
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
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