Archives de catégorie : Results

Multilevel comparison of large urban systems

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New publication, in Cybergeo, European Journal of Geography

Pumain D., Swerts E., Cottineau C., Vacchiani-Marcuzzo C., Ignazzi C.A., Bretagnolle A.,  Delisle F., Cura R., Lizzi L., Baffi S., 2015, « Multilevel comparison of large urban systems », Cybergeo : European Journal of Geography [En ligne], Systèmes, Modélisation, Géostatistiques, document 706, URL : http://cybergeo.revues.org/26730 ; DOI : 10.4000/cybergeo.26730

Abstract. For the first time the systems of cities in seven countries or regions among the largest in the world are made comparable through the building of spatio-temporally standardised statistical databases. We first explain the concept of a generic evolutionary urban unit (“city”) and its necessary adaptations to the information provided by each national statistical system. Second, the hierarchical structure and the urban growth process are compared at macro-scale for the seven countries with reference to Zipf’s and Gibrat’s model: in agreement with an evolutionary theory of urban systems, large similarities shape the hierarchical structure and growth processes in BRICS countries as well as in Europe and United States, despite their positions at different stages in the urban transition that explain some structural peculiarities. Third, the individual trajectories of some10,000 cities are mapped at micro-scale following a cluster analysis of their evolution over the last fifty years. A few common principles extracted from the evolutionary theory of urban systems can explain the diversity of these trajectories, including a specific pattern in their geographical repartition in the Chinese case. We conclude that the observations at macro-level when summarized as stylised facts can help in designing simulation models of urban systems whereas the urban trajectories identified at mico-level are consistent enough for constituting the basis of plausible future population projections.

Key-words. Urban systems, Zipf, Gibrat, Cities trajectories, BRICS

Half a billion simulations: Evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

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New publication, in Environment and Planning B.

Schmitt C, Rey-Coyrehourcq S, Reuillon R, Pumain D, 2015, « Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model » Environment and Planning B: Planning and Design, 42(2), 300-315. http://www.envplan.com/abstract.cgi?id=b130064p

Abstract. Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.

Keywords: simulation model, multiagent system, calibration, evolutionary algorithm, geographical modelling, high-performance computing, model validation

Multi-agent modeling of urban growth distribution in systems of cities

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A strong regularity in urban systems has long been identified : the hierarchical distribution of city sizes. Moreover, a closer observation of the evolution of this distribution shows that in the majority of city systems, there is a trend towards a more and more unequal distribution of city sizes. Why does the majority of urban systems show those strong regularities? What are the common growth processes involved? Several dynamic growth models have been proposed but no consensus has yet been reached because of the under-determination of models by those empirical laws. In this presentation we describe a new method of agent-based parsimonious modeling that we think can contribute to the identification of the common urban growth processes. This modeling method is based  on  intensive model exploration for quantitative evaluation of implemented mechanisms. The exploration tools were first developed for the evaluation of SimpopLocal, a model of the organization of urban systems when cities first emerged. The use of those exploration tools was then generalized into a modeling method that was applied for the first time with the construction of the MARIUS family of models which aims at reproducing the evolution of Soviet urbanisation between 1959 and 1989. Those two examples show how this new modeling method can help the construction of urban theories by helping the evaluation of assumptions made on urban processes.

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Communication at the seminar Quanturb, ISC-PIF (Paris), November 19th.

Clara Schmitt and Paul Chapron

Selective investments of foreign transnational firms in the French system of cities revealed by scaling laws

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Many economic links connect the cities embedded in systems of cities (for example commercial links, financial ownership links…). They participate in the intrinsic complexity of these systems. The interactions and connections between places due to the economic stakeholders can substantially impact the shape and the dynamics of any system of cities. Especially, networks built up by transnational firms by the ownership of subsidiaries located beyond their territorial borders into foreign systems of cities, could shape the future of related cities. These foreign investors can provide these cities new jobs by the creation or extension of establishments but also sometimes weaken them through a massive control on their total employment, transnational investments being expected to be more volatile than national investments. A new source of data providing interesting insights on the effective location of these impacts on French cities is now available (see box below).

An original database

Three data bases were combined to assign the establishments (physical location of production, selling points) controlled by foreign capital in French cities (=“aires urbaines” i.e. SMAs):

  • the ORBIS data base (produced by Bureau van Dijk, augmented by Céline Rozenblat – UNIL – GeoDiverCity) that contains all financial linkages between companies into the 3,000 major groups worldwide,

  • the LIFI data base (produced by INSEE), a French data base similar to the previous one yet extended to smaller groups,

  • the CLAP data base (also produced by INSEE), to add the level of establishments (the most suitable one for our geographical approach, whereas ORBIS and LIFI are limited to companies).

 

A strong hierarchical effect…

Our first analyses outline the high concentration of foreign-controlled employment in the biggest cities of the system (figure 1). We could expect this result by observing these networks at the level of the companies, as it is known that their headquarters are more concentrated than their physical locations. Yet even when these jobs are located at the level of establishments, the foreign-controlled activities scale superlinearly (as indicated by the scaling law exponent higher than 1). Foreign-controlled employment is consequently much more concentrated in largest cities. The distribution of these establishments is not proportional to the size of the cities, as it benefits first and foremost to the biggest cities of the system.

Figure 1: Important role of the urban hierarchy in the distribution of foreign-controlled jobs in the French system of cities

residus-total-anglais

… and regional effects

Taking into account the power law relationship and the quality of fit which remains nevertheless medium, another major trend appear in our first analysis. The amount of foreign-controlled jobs in the smallest cities is highly variable, some of them being deeply invested by the transnational firms while some others are almost avoided. A more detailed study of this variability show strong regional effects in addition to the hierarchical ones (figure 2). Cities with higher amounts of foreign-controlled jobs than expected by the scaling law are almost always located in Northern and Eastern parts of the country or located near Paris; cities where this amount is lower than expected are located in Southern and Western parts of the country. Thus foreign investors adapt to former spatial trends of urban functional specialization in France.

Figure 2: Regional effects in the distribution of foreign-controlled jobs within the smallest cities of the system

doubleresidus-anglais-2

Further analyses

As urban hierarchy doesn’t explain the distribution on its own, further analyses will investigate the effects of the economic specialization of cities, the closeness to bigger cities, or the shape of the networks where these cities are embedded or not. Our intention is to formulate some stylized facts identifying emerging properties and network dynamics that characterize the distribution of the foreign transnational firms into the French system of cities at an unprecedented level of detail. Besides classical multivariate analysis and networks analysis, scaling laws will constitute one of our main analysis tools, as they shed new light on the connection between urban functions, city sizes and economic innovation cycles (Pumain et al., 2006). This thesis, part of the GeoDiverCity program, will contribute an approach historicizing the scaling laws parameters relating them to the innovation cycles and the hierarchical diffusion of innovations theory in the system of cities.

Olivier Finance, PhD student under the supervision of Denise Pumain and Fabien Paulus.

OpenMOLE 1.0

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 It’s finally there!

After 6 years of hard work we made it to 1.0 (Heroic Hippo). It combines stability and ergonomic interfaces to provide the best model exploration tool ever made. OpenMOLE is a workflow engine designed to leverage the computing power of distributed execution environments for naturally parallel processes. A process is said naturally parallel if the same computation runs many times for a set of different inputs, such as model experiment or data processing…

We are very proud of this success, that has been made possible thanks to the effort and support of a dynamic community of developers, users and also several academic funding.

This version has mostly focus on stabilization, with some novelties nevertheless:
* the distributed computing feature of OpenMOLE are now very robust and efficient,
* the evolutionary algorithm part has matured and is usable to explore models from the console API,
* a new distributed environments (SLURM) is now available.

In the next versions:
* the graphical interface will be multi-user, web based and run in the browser,
* new environments will be provided (Condor, SGE),
* advanced methods such as genetic algorithm will be usable from the graphical interface,
* it will be possible to generate console scripts from a graphical workflow,
* bootstrapping methods that quickly and automatically embed your models in OpenMOLE will be implemented.

The OpenMOLE team

The first image of functional specializations among Chinese urban agglomerations

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Until now the industrial landscape of urban China was described only for the 647 “cities” as they were officially named (shi). Thanks to the ChinaCities data base built by Elfie Swerts using standardized delineation of urban agglomerations (that are thus comparable with other urban entities similarly defined in other regions in the world) it is now possible to provide a general view of employment profiles[ref] Population figures are computed in the Chinacities data base; data about employment in 14 activity sectors are extracted from Chinese census at district level (Source: China data center, University of Michigan). The 14 sectors are following: Mining and Quarrying –  Production and Supply of Electric Power – Gas and Water – Geological Prospecting and Water Conservancy – Manufacturing –  Construction –  Transport, Storage, Postal and Telecommunication Services –  Wholesale & Retail Trade and Catering Services –  Finance and Insurance –  Real Estate –  Social Services – Health Care, Sports and Social Welfare –  Education, Culture and Arts, Radio, Film, Television – Scientific Research and Polytechnic Services – Governments Agencies, Party agencies and Social Organizations. [/ref]peculiarities within the whole Chinese urban system.

As in other countries in every part of the world, the major difference among cities’ roles in Chinese economy is between “central places” ensuring the provision of services to the local and surrounding population together with the politico-administrative territorial control, and the manufacturing centers producing material goods.

Central places (in blue on the map) are disseminated according to a rather regular pattern whose density follows roughly the density of population. Among them, the smallest towns (in dark blue color) are relatively more specialized in services with a low share of their employment in other sectors, whereas the largest (in light blue) also include other types of activity, namely manufacturing, construction and real estate that accompany their bursting growth.

On the contrary, cities that are specialized in manufacturing activities with an average proportion of almost two thirds of employment (in red) – a very high ratio for large cities that is today typical of developing countries- are remarkably concentrated along the Eastern coastline, from the Pearl River delta to the Yangtze estuary. Their spatial distribution is mainly explained by the location of the Special Economic Zones since 1978 that generated higher rates of urban growth (6 % per year between 1982 and 2000) compared to the other agglomerations of the urban system (4.5%). A few exceptions are located in more central parts of the country, along the north-south axis and in Ex Manchuria or in the Western desert areas. Most of them are very specialized mining towns with at least 40% of their employment in extractive activities or probably other mono-activity manufacturing centers (Yanbianchaoxianzu and Helong in Jilin province, Qitaihe in Heilongjiang, Tiefa in Liaoning)

This spatial pattern is not surprising, knowing the almost absolute dedication of Chinese manufacturing production to exported goods, but as the cities in question are very large for most of them (1 to 5 millions of inhabitants), this extreme spatial concentration raises interrogation about their further evolution when the internal Chinese market will develop. Which infrastructure and logistic reorganization will allow the re-equilibration of urban development and functional portfolio of Chinese cities in the next coming decades?

Elfie Swerts

Singularities of urban systems in China and India

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We show for the first time comparable and exhaustive maps of cities for the largest two countries of the world[ref] Urban data bases have been built by Elfie Swerts from a variety of statistical and geographical sources according to a unified concept of urban agglomeration (continuously built up area). [/ref]. Around year 2000 there represent the precise location of about 11 000 cities above 10 000 inhabitants in China and 7000 in India, regrouping respectively 690 and 380 million of urban citizens (figure 1 and 2).

Two subcontinents of megacities and small towns

A major singularity common to both urban systems when compared to the rest of the world is the combination of huge metropolises with a high density of numerous smaller towns. Each country has at least three megacities above 10 million inhabitants (Shanghai, Beijing, Guanzhou, Delhi, Mumbai, Kolkata) and Shanghai and Delhi are even each above 20 million. No doubt that in the next decades more very large urban centers will emerge, many of the largest cities of the world will be found in Asia and even a handful of gigantic conurbations called “megalopolis”.

But the distinctive feature of these urban systems is their dense underlying pattern of small towns with population between 10 000 and 50 000 inhabitants, of which there are almost 9 000 in China and 5 400 in India. One third of the urban population of each country lives in these relatively small settlements (whereas the proportion is only 25% in Europe and 5% in the United States of America). These small settlements are disseminated in every region, according to a rather more regular spatial pattern in India compared to the asymmetry between sporadic Western and denser Eastern urbanization in China (due to environmental and historical factors).

Figure 1 Population sizes of urban agglomerations in China (year 2000)

Source : Swerts Chinacities, urban agglomerations above 10 000 inhabitants

Figure 2 Population sizes of urban agglomerations in India (year 2001)

Source : Swerts, Indiapolis and IndiaCensus, urban agglomerations above 10 000 inhabitants

Two different types of urban hierarchies

Even if both countries share with all other urban systems a strong differentiation of city sizes according to a Zipf’s distribution, China appears as an exception in the world. Although the share of urban population is higher in China (urbanization rate = 60% against 40% only in India) the index which measures the level of inequalities of city sizes in the system (that is the slope of the adjusted rank-size curve) is only 0.8 in China (against 0.9 in India) (figure 3). Moreover, while the general trend in India, in conformity with observations in other parts of the world is an increase of these urban size differences during the historical urbanization process, the slope has been decreasing during the last forty years in China. That can certainly be explained by the strength of urban and spatial planning policies in this country – even if an uncertainty remains about the identification of urban population because of the hukou system. It is also a demonstration that an evolution toward more and more hierarchy in urban systems is not as inescapable as it may appear from uncontrolled urban dynamics in most world countries.

Figure 3 Rank-size distributions of cities in China and India around 2000

Sources : China : Swerts, ChinaCities (2000), India : Swerts, Indiapolis (2001)

Elfie Swerts


The Integration of the cities in the networks of multi-national firms in the agri-business industry

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The development of the agrofood sector has always taken place alongside the process of urbanisation (Bairoch, 1988).  The agrofood sector, today the primary global manufacturing sector, offers evidence of former processes of integration of cities undergoing globalisation.  Each city, according to its size, its attractivity, its power in the network, its capacity or lack of capacity to connect with the economic actors, occupies a particular position in these agrofood companies’ networks

Starting from original information about the networks of the subsidiaries of the largest agrofood companies, we developed indicators describing the weight and the centrality of the cities in these networks . Data about the financial linkages between agrofood firms deriving from the Orbis base, 2010, that we prepared (Orbis, Bureau Van Dijk, 2010 ; Rozenblat, 2010).  To highlight the position of each city in the global strategies of agrofood companies we carried out a principal component analysis and an ascendant hierarchical classification according to six variables:

  • The population;
  • The number of subsidiaries present in each city;
  • The intra-urban connectivity corresponding to the mean number of the relations of a subsidiary in a city;
  • The centrality of intermediarity (betweenness centrality)
  • The degree (total number of relations), In-Degree (relations entering) and Out-Degree (relations going out);

An indicator of power as being the difference between relations going out and relations entering, relativised by the total number of relations.

The principal component analysis brings out two main dimensions, which focus on 80% of the information contained in the data.  The first factor, representing 62.5% of the information, distinguishes between the cities according to their attractivity and their local and global centrality in the network (inter-urban dimension).  The second factor summarizes 16.5% of the information, opposing small and middle-sized cities benefiting from a strength associated with their strong intra-urban connectivity to cities with a large population but less attractive (intra-urban dimension).

The ascendant hierarchical classification identifies 6 classes of cities, and a Chi2 test confirms a significant relationship between the continental membership of the city and its classification.

  • Class 1 is represented by small to middle-sized cities with weak centrality and weak attractivity.  This class brings together 70% of the cities in our sample.  All the continents are represented in it in a homogeneous fashion.
  • Class 2 is made up of cities with a strong population and weak scores for attractivity and centrality in the network. It brings together 33 cities ; the Asian cities are over-represented in this class :  they represent 2/3 of the  group, followed by the cities of South America, also over-represented (7 cities).
  • Class 3 describes small to middle-sized cities, controlling (strong indications of power) with a strong intra-urban connectivity. The European cities are largely over-represented, as they represent 70% of the group (119 cities). The Asian and South American cities are under-represented.
  • Class 4 represents middle-sized cities with strong indications of centrality. Once again the European cities are over-represented, they represent 70% of the group.
  • Class 5 defines cities with very strong centrality indications of degree and of ‘intermediarity’ (Betweenness). 12 cities belong in this class:  5 European cities, 3 Asian cities and 3 North American cities; 1 African city (Johannesburg).
  • Class 6 describes the cities of Paris and London, which have an exceptional position in the network, with a very strong betweenness centrality, and of relatively weak ‘in-and-out’ degrees.

These results demonstrate that the cities do not have the same attractivity in relation to their population size.  With equal populations, the parameters of the power functions of the scaling laws of the systems of the North American and European cities are twice as strong as those of the system of Asian cities.  These relationships demonstrate that the economies of agglomerations are superior in the North American and European cities, which is probably linked to the quality of their infrastructures, to the diversity of their economic actors, and to their position in the global value chains.

This typology also brings to light a strong centre-to-periphery structure, with at its head, London, the cradle of the food-processing industry, the most attractive and central city; then Paris, less attractive than London, but which plays the particular role of international bridge in the agrofood companies’ networks. In the second position are found some international cities that are integrated and central, but whose influence varies from the intra-continental scale to the national scale.  The periphery is defined by the cities of Groups 1 and 2.  Not surprisingly, the cities of this group are for the most part located in poor countries : most of them being Asian cities, cities of Africa, South America, and the Pacific coast of the North American continent.

Bérengère Gautier

BAIROCH P. (1988) Cities and economic development: From the dawn of history to the present, University of Chicago Press, 596 pages

GAUTIER B. (2012) « Intégration et développement des villes méditerranéennes par les réseaux de firmes multinationales du secteur agroalimentaire », Université de Lausanne, Thèse de doctorat, 322 pages. http://serval.unil.ch/?id=serval:BIB_942F9B17ECDC

ROZENBLAT C. (2010) “Opening the Black Box of Agglomeration Economies for Measuring Cities’ Competitiveness through International Firm Networks”, Urban Studies, Vol 47, n°13, pp  2841-2865

100 years of computation

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It’s what it takes to calibrate the SimpopLocal model, that simulates the dynamical hierarchical and spatial organization of settlements at the time when cities emerged, a few thousand years after the emergence of agriculture. Even if this model has been built using a few simple mechanisms, 7 parameters have no known empirical value. To find suitable values for those parameters, an automated calibration algorithm has been designed. In doing so, three quantitative goals have been defined in order to measure the quality of the output of a simulation, hence the quality of a set of parameters. One goal targets the shape of the distribution of the size of settlements, another one the size of the biggest settlement of the distribution and a last one the length of the simulation, or number of iterations, required to achieve those goals. These criteria are evaluated by computing 30 replications (independent execution) of the model (due to its stochasticity). Using the OpenMOLE framework (www.openmole.org) a genetic algorithm (a global optimization algorithm) has been distributed on the European grid EGI, federating computing power all over the world. After running about 10 million model executions, which would take more than 100 years of computation on a bleeding edge computer, the algorithm has finally converged after one week of computation and found suitable sets of parameters for the model calibration. The modelers have validated them and are now taking benefit from the calibrated model to better understand the implications of the mechanisms chosen to simulate a stylized emergence of urbanism.

Romain Reuillon, Sébastien Rey-Coyrehourcq and Clara Schmitt

The dispersion of added value in Russia through multinational networks

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This map is made out from an analysis of the multinational firms’ networks in Russian cities (ORBIS database, Bureau van Dijk, 2010; C. Rozenblat). The links between owners and subsidiaries are aggregated into urban agglomerations, and differentiated by activity sectors (NACE). Company groups working in Russia have been constituted : chains of ownership were formed where subsidiaries are owned with a share of at least 50%. The map shows mean points and standard distances of head groups’ locations in Russia. The more we travel East from Moscow, the less the share of added value in production: The barycentre for finance, information and communication activities (that is : advanced or metropolitan services) is the most western one, the closest to Moscow, and their standard distance is 2000km;  further to the East is the barycentre of trade groups, similarly scattered, followed by manufacturing industries, that are less concentrated; eventually, mining and transport groups have a remote gravity centre located at thousand kilometres to the East, with the largest spatial dispersion.

Cyril Jayet and Clémentine Cottineau