Marc Barthelemy (Universite Paris-Saclay, CNRS, CEA, Institut de Ph, Vincent Verbavatz (Universite Paris-Saclay, CNRS, CEA, Institut de
Statistics and Dynamics of Urban Populations
Empirical Results and Theoretical Approaches
Marc Barthelemy (Universite Paris-Saclay, CNRS, CEA, Institut de Ph, Vincent Verbavatz (Universite Paris-Saclay, CNRS, CEA, Institut de
Statistics and Dynamics of Urban Populations
Empirical Results and Theoretical Approaches
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This book describes all aspects of quantitative approaches to urban population growth, ranging from measures and empirical results such as the famous Zipf law, to the mathematical description of their evolution.
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This book describes all aspects of quantitative approaches to urban population growth, ranging from measures and empirical results such as the famous Zipf law, to the mathematical description of their evolution.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Oxford University Press
- Seitenzahl: 176
- Erscheinungstermin: 21. März 2024
- Englisch
- Abmessung: 246mm x 183mm x 23mm
- Gewicht: 506g
- ISBN-13: 9780192867544
- ISBN-10: 0192867547
- Artikelnr.: 69234227
- Verlag: Oxford University Press
- Seitenzahl: 176
- Erscheinungstermin: 21. März 2024
- Englisch
- Abmessung: 246mm x 183mm x 23mm
- Gewicht: 506g
- ISBN-13: 9780192867544
- ISBN-10: 0192867547
- Artikelnr.: 69234227
Dr Marc Barthelemy is a former student of the École Normale Superieure of Paris and graduated at the University of Paris with a thesis in theoretical physics. His research focuses on complex systems with an emphasis on cities and networks. MB is research director at the Institute of Theoretical Physics (CEA) in Saclay and a member of the Center of Social Analysis and Mathematics (CAMS) at the Ecole des Hautes Etudes en Sciences Sociales (EHESS). Dr Vincent Verbavatz is a former student of the École polytechnique and graduated at the University of Paris-Saclay with a thesis in statistical physics. His research focuses the modelling of cities, notably of the modelling of urban growth. VV is an associate researcher at Institut des systèmes complexes de Paris Île-de-France.
PART I COUNTING PEOPLE
1: Urban population
1.1 Defining the city
1.2 An historical example: Paris
1.3 Functional and morphological denitions
1.4 Gridded population of the world
2: Why does population matter?
2.1 Population is a good start
2.2 Scaling in cities
PART II RANKING CITIES
3: The distribution of urban populations
3.1 Power-laws
3.2 Zipf's law for cities
3.3 How to t a power-law?
3.4 Revisiting Zipf's law for cities
4: Dynamics of ranking
4.1 Stable versus unstable ranking
4.2 Modelling the ranking dynamics
4.3: Rank variations of cities
PART III MODELS OF URBAN GROWTH
5: Stochastic calculus
5.1 Brownian motion
5.2 Itô and Stratonovich prescriptions
5.3 Fokker-Planck equation
6: Stochastic models of growth
6.1 Yule-Simon's model of growth
6.2 Gibrat's law for cities
6.3 Gabaix's mode
7: Models with migration
7.1 A modied Yule-Simon model
7.2 A master equation approach
7.3 Diusion with noise: the Bouchaud-Mezard model
PART IV HOW CITIES TRULY GROW
8: The generalized central limit theorem and Levy stable laws
8.1 The central limit theorem and its generalization
8.2 Levy stable laws
8.3 The generalized central limit theorem
9: From First principles to the growth equation
9.1 Building a bottom-up equation
9.2 Gravitational model
9.3 Minimal model for the inter-urban migration flows
10: About city dynamics
10.1 Solving a new kind of equation
10.2 Analysis and scaling of the solution
10.3 Rank dynamics
11: Outlook: Beyond Zipf's law
11.1 Zipf's law: the end?
11.2 And space?
References
Index
1: Urban population
1.1 Defining the city
1.2 An historical example: Paris
1.3 Functional and morphological denitions
1.4 Gridded population of the world
2: Why does population matter?
2.1 Population is a good start
2.2 Scaling in cities
PART II RANKING CITIES
3: The distribution of urban populations
3.1 Power-laws
3.2 Zipf's law for cities
3.3 How to t a power-law?
3.4 Revisiting Zipf's law for cities
4: Dynamics of ranking
4.1 Stable versus unstable ranking
4.2 Modelling the ranking dynamics
4.3: Rank variations of cities
PART III MODELS OF URBAN GROWTH
5: Stochastic calculus
5.1 Brownian motion
5.2 Itô and Stratonovich prescriptions
5.3 Fokker-Planck equation
6: Stochastic models of growth
6.1 Yule-Simon's model of growth
6.2 Gibrat's law for cities
6.3 Gabaix's mode
7: Models with migration
7.1 A modied Yule-Simon model
7.2 A master equation approach
7.3 Diusion with noise: the Bouchaud-Mezard model
PART IV HOW CITIES TRULY GROW
8: The generalized central limit theorem and Levy stable laws
8.1 The central limit theorem and its generalization
8.2 Levy stable laws
8.3 The generalized central limit theorem
9: From First principles to the growth equation
9.1 Building a bottom-up equation
9.2 Gravitational model
9.3 Minimal model for the inter-urban migration flows
10: About city dynamics
10.1 Solving a new kind of equation
10.2 Analysis and scaling of the solution
10.3 Rank dynamics
11: Outlook: Beyond Zipf's law
11.1 Zipf's law: the end?
11.2 And space?
References
Index
PART I COUNTING PEOPLE
1: Urban population
1.1 Defining the city
1.2 An historical example: Paris
1.3 Functional and morphological denitions
1.4 Gridded population of the world
2: Why does population matter?
2.1 Population is a good start
2.2 Scaling in cities
PART II RANKING CITIES
3: The distribution of urban populations
3.1 Power-laws
3.2 Zipf's law for cities
3.3 How to t a power-law?
3.4 Revisiting Zipf's law for cities
4: Dynamics of ranking
4.1 Stable versus unstable ranking
4.2 Modelling the ranking dynamics
4.3: Rank variations of cities
PART III MODELS OF URBAN GROWTH
5: Stochastic calculus
5.1 Brownian motion
5.2 Itô and Stratonovich prescriptions
5.3 Fokker-Planck equation
6: Stochastic models of growth
6.1 Yule-Simon's model of growth
6.2 Gibrat's law for cities
6.3 Gabaix's mode
7: Models with migration
7.1 A modied Yule-Simon model
7.2 A master equation approach
7.3 Diusion with noise: the Bouchaud-Mezard model
PART IV HOW CITIES TRULY GROW
8: The generalized central limit theorem and Levy stable laws
8.1 The central limit theorem and its generalization
8.2 Levy stable laws
8.3 The generalized central limit theorem
9: From First principles to the growth equation
9.1 Building a bottom-up equation
9.2 Gravitational model
9.3 Minimal model for the inter-urban migration flows
10: About city dynamics
10.1 Solving a new kind of equation
10.2 Analysis and scaling of the solution
10.3 Rank dynamics
11: Outlook: Beyond Zipf's law
11.1 Zipf's law: the end?
11.2 And space?
References
Index
1: Urban population
1.1 Defining the city
1.2 An historical example: Paris
1.3 Functional and morphological denitions
1.4 Gridded population of the world
2: Why does population matter?
2.1 Population is a good start
2.2 Scaling in cities
PART II RANKING CITIES
3: The distribution of urban populations
3.1 Power-laws
3.2 Zipf's law for cities
3.3 How to t a power-law?
3.4 Revisiting Zipf's law for cities
4: Dynamics of ranking
4.1 Stable versus unstable ranking
4.2 Modelling the ranking dynamics
4.3: Rank variations of cities
PART III MODELS OF URBAN GROWTH
5: Stochastic calculus
5.1 Brownian motion
5.2 Itô and Stratonovich prescriptions
5.3 Fokker-Planck equation
6: Stochastic models of growth
6.1 Yule-Simon's model of growth
6.2 Gibrat's law for cities
6.3 Gabaix's mode
7: Models with migration
7.1 A modied Yule-Simon model
7.2 A master equation approach
7.3 Diusion with noise: the Bouchaud-Mezard model
PART IV HOW CITIES TRULY GROW
8: The generalized central limit theorem and Levy stable laws
8.1 The central limit theorem and its generalization
8.2 Levy stable laws
8.3 The generalized central limit theorem
9: From First principles to the growth equation
9.1 Building a bottom-up equation
9.2 Gravitational model
9.3 Minimal model for the inter-urban migration flows
10: About city dynamics
10.1 Solving a new kind of equation
10.2 Analysis and scaling of the solution
10.3 Rank dynamics
11: Outlook: Beyond Zipf's law
11.1 Zipf's law: the end?
11.2 And space?
References
Index