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Structure and Dynamics of Cities: Urban Data Analysis and Theoretical Modeling [Kõva köide]

(Centre Commissariat à l'Energie Atomique (CEA), Saclay)
  • Formaat: Hardback, 278 pages, kõrgus x laius x paksus: 252x180x17 mm, kaal: 690 g, 7 Tables, black and white; 68 Halftones, black and white; 44 Line drawings, black and white
  • Ilmumisaeg: 24-Nov-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107109175
  • ISBN-13: 9781107109179
  • Formaat: Hardback, 278 pages, kõrgus x laius x paksus: 252x180x17 mm, kaal: 690 g, 7 Tables, black and white; 68 Halftones, black and white; 44 Line drawings, black and white
  • Ilmumisaeg: 24-Nov-2016
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107109175
  • ISBN-13: 9781107109179
"With over half of the world's population now living in urban areas, the ability to model and understand the structure and dynamics of cities is becoming increasingly valuable. Combining new data with tools and concepts from statistical physics and urbaneconomics, this book presents a modern and interdisciplinary perspective on cities and urban systems. Both empirical observations and theoretical approaches are critically reviewed, with particular emphasis placed on derivations of classical models and results, along with analysis of their limits and validity. Key aspects of cities are thoroughly analyzed, including mobility patterns, the impact of multimodality, the coupling between different transportation modes, the evolution of infrastructure networks, spatial and social organisation, and interactions between cities. Drawing upon knowledge and methods from areas of mathematics, physics, economics and geography, the resulting quantitative description of cities will be of interest to all those studyingand researching how to model these complex systems"--

Arvustused

'Every so often along comes a book that attempts a grand synthesis. Marc Barthelemy has put together many ideas from statistical physics with theory in urban economics, fashioning an approach that demonstrates its essential logic and empirical relevance. A book that must be absorbed by urbanists of every persuasion and used to advance our science of cities.' Michael Batty, University College London 'Collective effects are often counterintuitive and defeat our imagination. We need specific models to anticipate financial crashes, traffic jams, mass panics. The spontaneous organization of cities falls in the same category of phenomena created by ourselves, humans, but that -- paradoxically we struggle to understand.  This wonderful book summarizes a large number of data and ideas about how cities grow and self-organize, sometimes not in the most efficient way.  In his plea for a new science for cities, Marc Barthelemy musters methods from statistical physics for a problem that concerns an ever-growing fraction of humanity.' Jean-Philippe Bouchaud, Capital Fund Management, Paris ' a multi-disciplinary effort to describe and understand the numerous structural aspects of cities and their evolution This book makes an effort to bring these different points of view together, to find a common scientific language, and to look at cities as systems that show typical features such as complexity, self-organisation and emergence which can be described in the language of statistical physics. The whole text is a well-written scientific essay and fully referenced to scientific publications from a broad range of disciplines. The data and models are presented with mathematical rigour and illustrated by numerous black-and-white figures. The book is highly interesting for its multi-disciplinary approach as well as for the data presented, and can be recommended to a wide interested readership with a general understanding of mathematics and statistical physics.' Manuel Vogel, Contemporary Physics 'Marc Barthelemy refreshes ideas and opens new avenues for further research in urban/economic quantitative geography. Without ignoring 'Founding Fathers' in geography, he suggests inspiring ideas anchored in physics for modelling urban realities. A path toward multidisciplinary analysis, which has still a long way to go before success.' Isabelle Thomas, Université catholique de Louvain

Muu info

Presents a modern and interdisciplinary perspective on cities that combines new data with tools from statistical physics and urban economics.
Preface xi
Acknowledgments xvii
1 Urban systems
1(24)
1.1 A science of cities
2(6)
1.1.1 The nature of the problem
2(2)
1.1.2 What is a city? Origins and definitions
4(4)
1.2 Spatial and temporal scales
8(12)
1.2.1 Population
8(4)
1.2.2 Area, density, and volume of cities
12(7)
1.2.3 Time scales
19(1)
1.3 Naive scaling
20(5)
1.3.1 Surface area
21(1)
1.3.2 Total length of roads
21(2)
1.3.3 Total daily commuting distance
23(2)
2 Models and methods
25(22)
2.1 Statistical physics of complex systems
25(1)
2.2 The shape of a science of cities
26(2)
2.3 How many parameters?
28(3)
2.3.1 Statistical physics and relevant parameters
28(1)
2.3.2 Modeling cities
29(2)
2.4 Critiques of urban economics
31(6)
2.4.1 Interactions and equilibrium
32(1)
2.4.2 Invariance with respect to utility choice
33(4)
2.5 Data
37(8)
2.5.1 Sources
38(1)
2.5.2 Different types of data
39(4)
2.5.3 Data are not enough: models
43(2)
2.6 The barriers to interdisciplinarity
45(2)
3 The spatial organization of cities
47(31)
3.1 Optimal locations
47(5)
3.1.1 Distribution of public facilities
47(2)
3.1.2 Distribution of retail stores
49(3)
3.2 Measuring a polycentric structure
52(5)
3.2.1 Definition
52(2)
3.2.2 Identifying and counting hotspots
54(3)
3.3 Polycentricity: Classical approaches
57(8)
3.3.1 The Fujita--Ogawa model
57(6)
3.3.2 The edge-city model
63(2)
3.4 Revisiting the Fujita--Ogawa model
65(13)
3.4.1 A complex quantity described as random
65(2)
3.4.2 Monocentric-polycentric transition
67(1)
3.4.3 Number of centers
68(2)
3.4.4 Consequences for mobility
70(2)
3.4.5 CO2 emission and gasoline consumption
72(3)
3.4.6 Urban villages
75(1)
3.4.7 The most economical population distribution
76(2)
4 Infrastructure networks
78(51)
4.1 Roads and streets: patterns
78(17)
4.1.1 Length of the network
79(2)
4.1.2 Statistics of blocks
81(8)
4.1.3 Structure of paths
89(6)
4.2 Evolution of the road network
95(16)
4.2.1 Basic properties
96(3)
4.2.2 Simplicity profile
99(2)
4.2.3 Betweenness centrality impact
101(1)
4.2.4 Evolving patterns of betweenness centrality
102(2)
4.2.5 Modeling the road network
104(7)
4.3 Subways
111(14)
4.3.1 All large cities have a subway system
111(1)
4.3.2 Convergence to a universal structure
112(8)
4.3.3 Scaling and modeling for subways
120(5)
4.4 Digression: Railroads
125(4)
4.4.1 Scaling
125(2)
4.4.2 Are subways and railroads the same?
127(2)
5 Mobility patterns
129(32)
5.1 Typology of origin--destination matrices
130(8)
5.1.1 Extracting coarse-grained information from OD matrices
132(2)
5.1.2 Comparing mobility networks
134(4)
5.2 Modeling mobility patterns
138(13)
5.2.1 Statistics of flows: from gravity to radiation
138(4)
5.2.2 Commuting and income
142(9)
5.3 Human mobility: Levy flights or accelerated walkers?
151(10)
5.3.1 Back to basics: empirical observations
152(3)
5.3.2 Modeling the hierarchy of modes
155(6)
6 Multimodality in cities
161(32)
6.1 A multilayer network view of urban navigation
162(10)
6.1.1 Empirical observations of multimodality
162(3)
6.1.2 Characterizing the multilayer system
165(7)
6.2 The effect of coupling
172(13)
6.2.1 A toy model
173(4)
6.2.2 Optimal velocity for the road--subway system
177(8)
6.3 Information perspective on navigation in cities
185(8)
6.3.1 Simplest paths
186(1)
6.3.2 Information entropy
186(2)
6.3.3 Information threshold: 8 bits
188(2)
6.3.4 Effect of multimodal couplings
190(3)
7 Socioeconomic aspects
193(32)
7.1 Classical models of urban economics
194(9)
7.1.1 Why discuss these models here?
194(1)
7.1.2 The Alonso--Muth--Mills model
194(6)
7.1.3 Beckmann's model: space and the social network
200(3)
7.2 Segregation and income structure of cities
203(7)
7.2.1 A null model for spatial segregation
204(1)
7.2.2 The emergent social stratification of cities
205(5)
7.3 Modeling segregation
210(9)
7.3.1 Transportation modes in the Alonso--Muth--Mills model
210(3)
7.3.2 A simple model for tie formation
213(1)
7.3.3 Statistical physics of the Schelling model
214(3)
7.3.4 Collective versus individual dynamics
217(2)
7.4 Scaling in urban systems
219(6)
7.4.1 What is scaling?
219(3)
7.4.2 Theoretical approaches
222(3)
8 Systems of cities
225(17)
8.1 Population distribution
225(11)
8.1.1 The number of cities and the largest city
226(2)
8.1.2 Gibrat, Gabaix, and diffusion with noise
228(8)
8.2 Central place theory and spatial fluctuations
236(6)
8.2.1 Outline of Christaller's theory
236(1)
8.2.2 Spatial fluctuations
237(5)
9 Toward a new science of cities
242(6)
9.1 What is our "understanding"?
242(2)
9.2 Measuring the death and life of great cities
244(1)
9.3 The future of the city
245(2)
9.4 Concluding thoughts
247(1)
References 248(13)
Index 261
Marc Barthelemy is a senior researcher at the Institute of Theoretical Physics in Saclay (CEA) and a member of the Center of Social Analysis and Mathematics (EHESS). He has worked on applications of statistical physics to complex networks, epidemiology, and more recently, spatial networks, and is the co-author, with Alain Barrat and Alessandro Vespignani, of Dynamical Processes on Complex Networks (2008). Focusing on both data analysis and modeling, he is currently working on various aspects of the emerging science of cities.