City networks as adaptive networks

GEOGRAPHIE-CITES

Denise Pumain, Global systems as networks of networks, Florence, 2012

City networks as adaptive

networks

Denise Pumain

Université Paris I

Institut Universitaire de France


A World connected through « Social » Networks


Behind networks: Infrastructures (IP addresses)


…and strong income inequalities

Internet intensity rate, 2007


Future dynamics of urban networks

Convergence towards equilibrium?

Or continuation of hierarchical dynamics?

The answer is in network effects


Urban systems as multi-level ICT systems

Pumain D.

Hierarchy in

Natural and

Social

Sciences

Springer,

2006


Rozenblat, 2008

Networks

(Intertwined

Individual

and

institutional

strategies)

Urban systems as multi-level networks


Urban systems as societal adaptors/creators

Meso-level: the city

critical time (length of travel) 1 hour (Zahavi)

low speed networks (x by 5 since 1800)

strong interactions (3/person/day)

density and price gradient (centre-periphery), fractal spatial organisation, functional zoning and social differentiation

Macro-level: system of cities

critical time (length of travel) 1 day (E. Reclus)

high speed networks (x by 40 since pre-industrial)

weak interaction (less frequent)

hierarchy of sizes, scaling between size and number of activities, functional geodiversity


Pumain, 2006

Multi-levels

interactions

Adaptation and innovation through interactions

Pumain D.

Hierarchy in

Natural and Social

Sciences

Springer, 2006


Urban dynamics as network processes

Urban space is an agent-activity space, where activities are increasing over time in number, diversity and range of action

Agents are revisiting their activities through branched communication networks of increasing speed and capacity

Transportation and communication networks are mutually reinforcing

Hierarchical differentiation is increasing in urban systems

Hypothesis: « agglomeration economies» proceed from dynamic « network effects»


Innovation and urban systems

D. Lane, D. Pumain, S. van der Leeuw, G. West

Complexity perspectives in Innovation and Social change, Springer, 2009

How innovation is accepted and propagated in a spatial system?

How the organisation of urban systems is produced and evolves through innovations that it contributes to generate?


Consequences of network effects on urban dynamics

Non linear accumulation processes

scaling laws

Distributed urban growth plus hierarchical

selection and urban specialisation

Increasing socio-economic inequalities

inside and between cities


An example of urban scaling laws

(F. Paulus, C. Vacchiani-Marcuzzo)

France USA Number of cities * *

- 354 “aires urbaines” ** 737 metropolitan areas** areas**

Employment data

-- 32 economic sectors (1999, NES) (1999, NES) -- 31 Occupational groups groups -(1999)

-- 26 economic sectors (2000, (2000, NAICS) -- 23 Occupational Groups (2000) Groups (2000)

* Urban agglomeration (core) and its periphery defined

according to the intensity of commuting links

** Tests have been done with the same number of cities in France and USA and

the results lead to the same interpretation.

Employment data for economic sectors have been collected for 6 dates in France

(1968 to 1999) and 4 dates in USA (1970-2000)


Functional geodiversity in USA

Paulus,

Vacchiani-

Marcuzzo,

2010

(PCA

Projection)

APS, FIRE, Higth tech and communication

Old activities

Manufacturing

Services


The old differentiation tertiary/ industrial cities remains the

major source of economic geodiversity in 2000

PCA – scores

on 1st factor

Fabien Paulus

& Céline

Vacchiani-

Marcuzzo, 2011


The second factor differentiates cities with recent economic activities

(yellow) from those with and older economic base (blue)

This new

differentiation is

partially correlated

to the hierarchy of

city sizes

(Fabien Paulus

& Céline

Vacchiani-

Marcuzzo, 2011)

PCA – 2nd

component


Trajectories of US cities 1970-2000

(co-evolution)

PCA

projection

Paulus

C.V-M.

2010


Trajectories of French cities 1960-2000

(functional diversity space, PCA projection)

F. Paulus, 2003


Scaling parameters and stage of sectors

in the innovation cycle : Similar in US and France

Power-law exponent (β)

Stages in technological development

Innovation cycle

Economic sector

(NAICS / NES) France US

- Professional; scientific; and technical services / Consultancy and assistance activities

1.21 1.21

- Finance and insurance / Financial activities 1.15 1.14

- Wholesale trade / Wholesale trade 1.11 1.09 Innovation

- Administrative and support and waste management services / Renting and other business activities

1.07 1.11

- Accommodation and food services / Hotels and restaurants

1.04 0.98 Common place (adapting)

- Construction / Construction 0.99 1.01

- Retail trade / Retail trade 0.97 0.98

- Health care and social assistance / Health, social work 0.96 0.96 Mature

- Manufacturing / Manufacturing 0.92 1.00

Pumain, Paulus, Vacchiani-Marcuzzo, 2009 (ISCOM)


Innovation cycles and substitution process

Succession of innovation cycles

Connexion with scaling laws

Paulus, Vacchiani, Pumain

Cybergeo, 2006


Cities and systems of cities as generalised ICT • Cities as places for innovation through individual and collective interaction

• Cities connect through their networks a variety of complementary resources; they create new resources thus reduce local and conjonctural uncertainties

• Innovation diffuses through circulation of information between cities within the system of cities

impulses for urban capital accumulation (gains from adaptation, attraction of new resources)

Feed back effect from the system: incitation to innovate because of interurban competition

• Hierarchical process of innovation diffusion and substitution of activities scaling with city sizes

• Path dependent process in socio-spatial evolution (accumulation of physical, human, symbolic capital) leading to increased levels of complexity in society and technology


Urban dynamics including bifurcations

X = pit/Pt

Y = pit+1/Pt+1

Cities trajectories

in total urban

system population


Cities trajectories in innovation space


Innovation networks in information society: Observed research links between European cities

(FPs contracts in NBIC)

source: PhD Marie-Noelle Comin, 2009


Anticipating the next cycle:

NBIC research networks(M.-N. Comin)


Scaling: NBIC research networks at European level

M.-N. Comin, 2009

Population size of urban agglomerations

Number of

participations

in FP projects

(1986-2006)

NBIC research


NBIC networks: scaling by country

Country β Number of cities R² CI

Slovaquia 1,7 6 0,67 0,5 - 2,9

Bulgaria 1,7 7 0,77 0,9 - 2,5

Poland 1,6 19 0,81 1,2 - 2,0

Hungary 1,5 9 0,83 1,1 - 1,9

Sweden 1,4 29 0,49 1,0 - 1,9

Rep.Tcheque 1,4 9 0,67 0,7 - 2,1

Austria 1,4 9 0,67 0,7 - 2,1

Finland 1,4 17 0,70 1,0 - 1,8

Denmark 1,4 14 0,85 1,1 - 1,7

Romania 1,4 13 0,77 1,1 - 1,7

Spain 1,3 46 0,70 1,1 - 1,5

Ireland 1,3 12 0,79 0,9 - 1,6

Greece 1,2 17 0,70 0,9 - 1,5

Netherlands 1,2 25 0,25 0,5 - 1,9

France 1,2 75 0,57 1,0 - 1,4

Germany 1,1 70 0,47 1,0 - 1,3

Portugal 0,9 6 0,90 0,6 - 1,3

Italy 0,9 62 0,41 0,7 - 1,3

United Kingdom 0,9 55 0,52 0,7 - 1,1

Belgium 0,6 13 0,17 0,3 - 1,6

Europe 1986-2006 1,02 512 0,51 0,96-1,13

M.-N. Comin, 2009


Specialisation in NBIC from research networks

Comin, 2012


Flows of steamer vessels among world regions in

1890

Cesar Ducruet 2012

Project 1: networks


The global hierarchy of port cities in 1890

Cesar Ducruet 2012


Hierarchical integration of urban systems

Strong urban hierarchies

in bouded countries

Emerging global but weak

urban hierarchy when

boundaries disappear

Strong urban hierarchy

under global spatial

integration

C. Rozenblat, 2007


Investigating multinational firms global networks

A programme lead by Céline Rozenblat

Including a data base on the financial

organisation of the 3000 largest (turnover)

world corporations (source = bureau Van

Dijk)

Network of 800 000 links between owner

firms and their subsidiaries


Nestlé Danone Kraft food

Peugeot Toyota Fiat

Sources: Orbis, 2007


Position of cities in the multinational firms networks

=> Aggregation of ties (owner-subsidiaries)

PARIS

LONDON

NEW

YORK

Rozenblat, 2012


Céline Rozenblat, 2012

Organisation of global inter-firm financial linkages 2010

INTRA-NATIONAL

URBAN LINKS

66%

INTRA-URBAN

LINKS

25%

INTER-CONTINENTAL

URBAN LINKS

20%

INTRA-CONTINENTAL

URBAN LINKS

80%


Russian cities in entrepreneurial networks

Cottineau,

Jayet, Rozenblat,

2012

(Orbis data base)


Interpreting locations through innovation diffusion

Cottineau, Jayet, Rozenblat, 2012


Level of integration of cities in the worldwide urban agrofood multinationals networks

Gautier, 2012


The Indian urban system

Kolkata

Delhi

Mumbai Hyderabad

Chennai Bangalore

6.000

agglomerations

in 2001

Elfie Swerts, 2012

Elfie Swerts, 2012


Modelling for simulating urban dynamics

Hypothesis: changes are produced through

interactions at system level (interurban networks)

When there is too little information about urban

interactions (trade flows and exchanges, goods,

people, information…)

Reconstruction of urban exchange networks

through simulation in order to reproduce urban

changes = the SIMPOP series of models


SIMPOP: a multi-agents system

first application of MAS in geography !

Bura, Guérin-Pace, Mathian, Pumain, Sanders

Multi-agent systems and the dynamics of a settlement system. Geographical Analysis, 1996, 2, 161-178

Main results:

No emergence if no spatial interactions

Emergence of a polycentric hierarchised system of cities even if homogeneous initial conditions

A renewed innovation flow is necessary for maintaining structural properties of the system of cities

But: 400 settlements only

two levels only (meso-macro)


Exemple of the SIMPOP model:

simulation of the emergence of a polycentric system of cities

t=100

t=1700 t=2000

Starting from a rather regular distribution of settlements, a system of cities

emerges, with a strong hierarchical and spatial organization

Source: Bura, Guérin-Pace, Mathian, Pumain, Sanders, 1996, 1997


Emerging hierarchical differentiation of the

settlement system (rank-size distribution)

Source: SIMPOP model

t = 0

t = 2000

t = 1600


Originality of SIMPOP2 Model

Scale: national or continental integrated urban systems, long term

Cities are agents : collective, immobile, heterogenous, evolving entities

Main attributes: location, resources (labour force, capital), functions (10 types)

Three levels: individual (firm or mayor, for scenarios), cities (local governance), national or multinational (global governance)

Rules : stylised facts from comparative study of the observed evolution of integrated urban systems


Aim of the model

-To improve our evolutionary theory of urban

systems (a generic model)

-The model as a filter:

To analyse the role of geographical features and

processes (location, access to resources, situation

in information networks, mobility speed, range and

intensity, innovation adoption, selection) in shaping

urban dynamics


MAS for measuring urban dynamics properties

and predicting future evolution in a diversity of

urban systems

Europe: « full world » system

United States: « Frontier » urbanization

South Africa: « Frontier » and dual system

India: « full world » and dual

China: « full world », dual, and planned!


Historical data-driven simulations

Historical data trends serve as commands for the System

Example

Command : INNOV3 (t=1950) = (50.000, 400$)

Model Dynamics : Compute a spatial repartition

Simulation

time

Historical data injection Outputs observations

Classic

approach

Our

approach

S0 S1 SF St

Data injection Outputs observations

S0 SF


Simpop2 (2002-2007)

http://www.simpop.parisgeo.cnrs.fr

Helène

Mathian

Thomas

Louail

Lena

Sanders

Anne

Bretagnolle

http://www.simpop.parisgeo.cnrs.fr/


the Simpop instances


SIMPOP2: contrasting the evolutions of

European and US urban systems

To catch the specific history of urban settlements in US, parcimonious adaptation of rules from the generic SIMPOP model (as calibrated on European system) was necessary:

-belated emergence of central places

-strong incentive from international exchanges on urban growth

-frontier effect: momentary effect on urban growth (mushroom cities)

-migration of new urban functions to new cities stronger than reinvestment on old sites


Simulated and observed city sizes in 2000

(initial situation: observed locations in 1300)

Bretagnolle, Pumain, 2010


Urban sizes in 2000, United States

(observed and simulated with adapted SIMPOP2)

(Bretagnolle, 2007)


SimpopNano : location of the Simpop2

urban functions agents at intra-city scale

CENTRAL1 $ CENTRAL2 $ CENTRAL3 $ CENTRAL4 $

INNOV1 $ INNOV2 $ INNOV3 $ INNOV4 $

RESIDENT1 $ RESIDENT2 $

ADMIN1 $ ADMIN2 $

#employees

wealth

Neighborhoods

spatial

agents

Urban functions

employees

City Level

Neighborhoods and

urban functions dynamics

System of cities level

Thomas Louail, 2010


Comparing Urban Dynamics on different spatial

patterns

Milano, IT Phoenix, AZ

PhD Thomas Louail, 2010

and Bretagnolle, Delisle, 2009


1- Agrarian economy

Local resources

(SimpopLocal)

2- Market economy

Network returns

(SimpopNet)

3- Knowledge economy

Environmental intelligence

(SimpopClim)

Urban

transition

Industrial

Revolution

Simulating relationships between urban systems and environmental

resources: three stages in urban dynamics (series of SIMPOP models)

Environ

mental

transition

Clara Schmitt, Sébastien Rey, PhD

SIMPOP models: France Guérin-Pace, Lena Sanders, Hélène Mathian

with Stéphane Bura, Benoît Glisse, Thomas Louail (and Jacques Ferber, Alexis

Drogoul, Jean-Louis Giavitto, Guillaume Hutzler)

and Anne Bretagnolle


Further research in urban GeoDiverCity

1 A dedicated platform for multiscale model validation: SimProcess (S. Rey, T. Louail, H. Mathian, A. Banos, M. Leclaire, R. Reuillon, R. Cura)

2 Integration of micro-level economic and spatial processes in the dynamics of geographical entities (F. Paulus, C. Vacchiani-Marcuzzo, C. Rozenblat, M.-N. Comin, B. Gautier)

3 Settlement systems and global change: how policies about sustainability can reorient the future of cities? A comparison US-Europe-India-China-Russia-Brazil (E. Swerts, C. Schmitt, C. Cottineau, A. Ignazzi)

4 Co-evolution of networks and settlements over human evolution (L. Sanders, H. Mathian, A. Bretagnolle, C. Vacchiani-Marcuzzo, S. Martin, F. Delisle, S. Baffi)


Thank you for attention!

www.simpop.parisgeo.cnrs.fr

http://geodivercity.cnrs.fr

IGUL/Recherche/Etudes urbaines/CITADYNE

(Céline Rozenblat)

http://www.unil.ch/igul/page76265.html

http://www.simpop.parisgeo.cnrs.fr/

http://geodivercity.cnrs.fr/

City networks as adaptive networks

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