How to estimate the singularity of (post-)Soviet cities?

Revue d'Economie Regionale et Urbaine - n°1 - 2017 New Article, in RERU – Revue d’Économie Régionale et Urbaine.


Cottineau C., 2017, “How to estimate the singularity of (post-)Soviet cities?”, Revue d’Économie Régionale et Urbaine, n°1-2017.

Abstract. Although the urbanisation of the Former Soviet Union is a unique experience, this article questions the possibility of estimating the share of the generic processes of urban growth, spatial location and economic specialisation, the share of the particular processes of urbanisation linked to the size and political organisation of the country, and the share of the singular processes that cannot be explained without a local knowledge of the events which happened in the cities under study. Using several types of models at different scales, we identify the residual as the element that “resists modelling” and illustrates the singular evolutions of the Soviet Union and its cities. To do so, we built a harmonised urban database and fitted hierarchical, spatial and regression models. We conclude that city size inequality increased in a generic manner compared to other systems of cities, that the spatial distribution of cities resemble that of vast countries (especially with the increased reliance on sub-surface resources), and that there exists a set of singular urban trajectories.

Key-words. System of cities, Soviet Union, Singularity, Models, Urban growth, Urban trajectories, Urbanization.

Link. Revue d’Économie Régionale et Urbaine

Urban Dynamics and Simulation Models

Urban Dynamics and Simulation Models | ERC GeoDiverCityNew book, published by Springer International.


Pumain D., Reuillon R. (eds.), 2017, Urban Dynamics and Simulation Models, Springer, 123p.
DOI 10.1007/978-3-319-46497-8

Authors. Chapron P., Chérel G., Cottineau C., Cura R., Leclaire M., Pumain D., Rey-Coyrehourcq S., Reuillon R., Schmitt C., Swerts E.

Abstract. This monograph presents urban simulation methods that help in better understanding urban dynamics. Over historical times, cities have progressively absorbed a larger part of human population and will concentrate three quarters of humankind before the end of the century. This “urban transition” that has totally transformed the way we inhabit the planet is globally understood in its socio-economic rationales but is less frequently questioned as a spatio-temporal process. However, the cities, because they are intrinsically linked in a game of competition for resources and development, self organize in “systems of cities” where their future becomes more and more interdependent. The high frequency and intensity of interactions between cities explain that urban systems all over the world exhibit large similarities in their hierarchical and functional structure and rather regular dynamics. They are complex systems whose emergence, structure and further evolution are widely governed by the multiple kinds of interaction that link the various actors and institutions investing in cities their efforts, capital, knowledge and intelligence. Simulation models that reconstruct this dynamics may help in better understanding it and exploring future plausible evolutions of urban systems. This would provide better insight about how societies can manage the ecological transition at local, regional and global scales. The author has developed a series of instruments that greatly improve the techniques of validation for such models of social sciences that can be submitted to many applications in a variety of geographical situations. Examples are given for several BRICS countries, Europe and United States. The target audience primarily comprises research experts in the field of urban dynamics, but the book may also be beneficial for graduate students.

Key-words. Simulation, Simulation models, Simpop, Systems of Cities, Complexity, Urban systems, Urban systems dynamics, BRICS.

Link. http://www.springer.com/us/book/9783319464954

The rising stars of urban growth in China

The unbridled and three-dimensional development of Chinese cities is spectacular. It has been initially concentrated in the coastal area, since the economic reorientation of the Chinese economy and its productive system has at first been based on export-oriented manufacturing. The coastal cities, more open to the World than the central and western ones, have provided leverages for this development. Their driving role has been strongly reinforced by the creation of the Special Economic Zones (SEZ) since the 1980’s, firstly implanted in this area. However, the recent evolution of the Chinese economy and society could shift the scheme of an economic development based on coastal cities, and the current highest growth could be very different from the cities growth economic potential. A typology of the demographic trajectories of Chinese cities from 1982 to 2010 (Swerts & Liao, 2016) including all cities above 10 000 inhabitants according to an harmonized definition (Swerts, 2013) highlighted that cities whose demographic weight increases in the Chinese system are not only located in the East coast, but also in Central and Western China (figure 1). In terms of size categories, the most dynamic Chinese cities were both million plus cities and small cities (from 10,000 to 100,000 inhabitants).

The fastest growing small towns are mostly located at short distances of about 200 km around the largest metropolises. The increasing weight of some small towns and the stability of some large cities, as well as the current development of the Central and Western cities, could partly result from the strong emergence of an internal market, the increasing role of the tertiary sector in the Chinese economy and the more frequent creation of SEZ in Central and Western part of the country since the 1990’s. On the other hand, while the development of Chinese cities is highly linked to the evolution of the administrative system (Ma, 2005; Lin, 2005), the Chinese government has decreed successive decentralization reforms. These reforms could have fostered the economic development of the prefecture level cities and of the district level cities, the smallest ones.

Fig. 1. Trajectories of the Chinese cities from 1982 to 2010 (ChinaCities database, Swerts, 2013)Trajectories Chinese Cities 1982-2010

These changes could also generate long-term transformations of the functional organization of the Chinese urban system: including the expansion of the tertiary sector and the diversification of the cities’ economy and a strongest development of industrial activities out of the Eastern coast. The cities of the central and western part of China are mostly cities whose economy is diversified, some of them with an overrepresentation of manufacturing activities, and others that are mostly cities of less than 100,000 inhabitants, with an overrepresentation of tertiary activities (figure 2). Nowadays, the economic profile of the most dynamic cities is very diversified. The strongest potential for economic growth could thus shift from the Eastern coast to all over the country, although a strong growth still remains a characteristic of industrial coastal cities, in particular those in the Yangzi river delta, the Pearl River Delta and the area around Beijing.

Fig. 2. Functional specialization of Chinese Cities in 2000 (ChinaCities databases, Swerts, 2013)Functional spcecialisation Chinese Cities

International newspapers recently pointed out nine cities that reflect the strong potential of development of western and central cities, including a few non-metro cities. These cities are Chongqing (7.3 millions of inhabitants), Chengdu (6.4 million), Zhengzhou (4.1 million), Guiyang (2.4 million), Huainan (1.4 million), Xiangyyang (1.1 million), Suqian (274,594), Hengyang (165,824) and Zhuozhou (152,709). They are all cases illustrating the spatial deconcentration of the Chinese economic development (Fig. 2 & http://geodivercity.parisgeo.cnrs.fr/blog/2013/10/the-first-image-of-functional-specializations-among-chinese-urban-agglomerations/), as all are located in the Central and Western part of China (Fig 3).

Fig.3. Chinese rising stars?Chinese Rising Stars

These cities are very different in size, from some 150,000 inhabitants to more than 7 millions. This diversity is representative of the dynamics primed in China since the 1980’s. Some of these cities are province capitals, as Chengdu and Zhengzhou – and the Province level city Chongqing. The others cities are located between 50 and 250 km from their Province capitals. Zhuozhou excepted, all of them are prefecture level cities. In average, the Chinese cities annual growth rate between 1982 and 2010 is 3.3 % while the annual growth rate of these cities vary from 2% (Guiyang) to more than 5% (Chongqing or Suqian).

Tabl.1Chinese Rising Stars Table

These nine cities all have a diversified economy with an overrepresentation of the innovative tertiary sector, Huaibei and Hengyang excepted. In Huaibei, 40% of the employed population (source ChinaCities) is engaged in manufacturing activities, whereas 22% of the employed population of Hengyang is engaged in manufacturing, and 23% in building industry. This last city’s economy may be not so sustainable. From an investor point of view, urban growth rates over 4 or 5% or even 10% are attractive, but it is well known that rapid urban growth always occur with high fluctuations in space and time. Knowledge about deliberate national policies may help to secure predictions but more detailed information about the evolution of economic profiles on comparative basis also may enlighten further perspectives.

Elfie Swerts

How large are Chinese and Indian cities?

Reliable figures of cities’ population sizes are a great and useful by-product of comparative geographical analysis. We have identified which are now the major urban concentrations in the largest two countries of the world. Table 1 below provides the list of the population in top 30 Indian cities in 1981 and 2011 and top 30 Chinese cities in 1982 and 2010. In this table the last column on the right side enables comparing our results with figures that are given in official censuses of each country.

Table 1. Population of the 30 largest cities in India 1981 and 2011 and in China in 1982 and 2010Population 30 largest cities India ChinaSource: E. Swerts, 2013, for India : Indiacities database and Indian census; for ChinaCities database and Chinese Census

 

Towards mega-cities and megalopolises of a new kind

The multisecular Chinese and Indian urban development that intensified since the 1980’s fascinates the observers, largely because it has produced supersized cities, as Shanghai, Delhi and Beijing counting each around 20 million inhabitants. These cities are and will remain on the list of the 10 largest Urban Agglomerations in the World. They do not yet reach the size of formerly developed urban concentrations around Tokyo (38 millions in 2015 according to the United Nations, 43 according to PopulationData.net) or even New York (19 millions in 2015 according to the United Nations, 23 according to PopulationData.net ) but due to their growth rate it is very likely that during the next decades, gigantic conurbations will organize around them: in China three megalopolises are already forming between Beijing and Tianjin, from Shanghai to Nanjing and between Guangzhou and Hong-Kong (including Shenzhen, Dongguan and Zhuhai) (figure 1). In India, the same could occur around Delhi and Kolkata, and from Mumbai to Pune (figure 2). In Kerala the deviations between our database and the census in tentative sizing the agglomerations of Kochi or Thiruvananthapuram reflects the very peculiar expansion of urbanization in that region, which tend to mix very dense urban and rural areas in a way slightly different from Indonesian or Chinese “desakota” (McGee, 1991).

Figure 1. The 30 Largest Cities in China in 201030 largest cities ChinaSource: Chinacities database, E. Swerts (2013)

 

Figure 2. The 30 Largest Cities in India in 201130 largest cities IndiaSource: Indiacities database, E. Swerts (2013)

 

Safer data for research as well

Gigantic efforts have been made by many authors for establishing safer figures on the size of urban agglomerations in a comparable way and avoiding big mistakes as in papers electing Chongqing as the largest “city” in the world. Chongqing was supposed to reach from 32 to 34 million of inhabitants 1 because of a confusion in translating the Chinese word for “municipality” that may indeed denote a “province” (in that case covering 82 300 km2, approximately the size of Austria!) whereas the population of the agglomeration, although already large enough, oscillate between 7 or 10 millions according to different sources.

Of course for comparing cities and assessing dynamic urbanization processes building data base where the size of cities is comparable in time and space is absolutely necessary and this step of the work was duly accomplished in GeoDiverCity’s work, for Europe (Rozenblat, 1992; Guerois et al. 2009), Former Soviet Union (Cottineau, 2014), India (Swerts, 2013), China (Swerts, 2013), United States (Bretagnolle et al., 2015), Brazil (Ignazzi, 2015) and South Africa (Baffi, 2016, Vacchiani-Marcuzzo, 2005). All these data bases will be made freely accessible in due time, including detailed information about their methodology and metadata. The methodology employed by Elfie Swerts for building the data bases on India and China is made explicit in the annex in the full post.

Elfie Swerts


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The Brazilian Urban System: the trajectories of Brazilian cities between general dynamics and specific peculiarities

Brazil-citiesNew Article, in Cybergeo, European Journal of Geography.


Ignazzi C. A., 2015, «The Brazilian Urban System: the trajectories of Brazilian cities between general dynamics and specific peculiarities», Cybergeo : European Journal of Geography, Systems, Modelling, Geostatistics, document 754, DOI:10.4000/cybergeo.27349

Abstract. This article analyzes the dynamic of Brazilian system of cities illustrating the growth of Brazilian population through its own historical peculiarities like internal and international immigration, industrialization, metropolization. Urban hierarchy is revealed by means of the Zipf’s law and Gibrat’s model is used to describe the mechanisms of urban growth. Deviations from this model were explained by studying the specific trajectories of five different classes of cities, on various time scales (from 1872 to 2010 and from 1960 to 2010). These deviations highlight different kinds of factors (political, economic, localization of resources and historical accidents) that shaped the Brazilian system during the last century.

Key-words. Spatial autocorrelation, Urbanization, Brazil, Urban Hierarchy, Trajectories, Zipf’s law, Gibrat’s law, Markov Chains.

A modular modelling framework for hypotheses testing in the simulation of urbanisation

New Article, in Systems.


Cottineau C., Reuillon R., Chapron P., Rey-Coyrehourcq R., Pumain D., 2015, « A Modular Modelling Framework for Hypotheses Testing in the Simulation of Urbanisation », Systems, 3, Special Issue Agent-Based Modelling of City Systems, pp. 348-377, DOI:10.3390/systems3040348

Abstract. In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1) formalise complementary and alternative hypotheses of urbanisation and 2) explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate—efficiently and parsimoniously—a system of cities (between 1000 and 2000 cities in the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter space along with the space of model structures (the different combinations of mechanisms).

Key-words. system of cities; ABM; simulation; former soviet union; equifinality; multimodelling

Cities within systems of cities are the best tool for ensuring global sustainability

The COP21 that meet in Paris this December will discuss new agreements between countries for regulating activities that are detrimental to the global environment. Cities, because they organize in systems of cities are the best tool (that was invented long ago by societies) for solving environmental problems: first by circulating in a top-down way the international and national directives, second by sharing bottom-up ingenious local inventions to reduce pollution and save resource. This should no longer be conceived as a competition between cities for being considered as “the smarter” but as a collective challenge for territorial intelligence through interurban emulation. That message was presented last September in a conference at a meeting organized in Versailles by ESRI France that you can find here.

Denise Pumain

Growing Models from the Bottom Up. An Evaluation-Based Incremental Modelling Method (EBIMM) Applied to the Simulation of Systems of Cities

MARIUS_Fig1New Paper, in JASSS


Cottineau C., Chapron P., Reuillon R., 2015, « Growing Models from the Bottom Up. An Evaluation-Based Incremental Modelling Method (EBIMM) Applied to the Simulation of Systems of Cities », Journal of Artificial Societies and Social Simulation 18 (4) 9. doi:10.18564/jasss.2828

Abstract. This paper presents an incremental method of parsimonious modelling using intensive and quantitative evaluation. It is applied to a research question in urban geography, namely how well a simple and generic model of a system of cities can reproduce the evolution of Soviet urbanisation. We compared the ability of two models with different levels of complexity to satisfy goals at two levels. The macro-goal is to simulate the evolution of the system’s hierarchical structure. The micro-goal is to simulate its micro-dynamics in a realistic way. The evaluation of the models is based on empirical data through a calibration that includes sensitivity analysis using genetic algorithms and distributed computing. We show that a simple model of spatial interactions cannot fully reproduce the observed evolution of Soviet urbanisation from 1959 to 1989. A better fit was achieved when the model’s structure was complexified with two mechanisms. Our evaluation goals were assessed through intensive sensitivity analysis. The complexified model allowed us to simulate the evolution of the Soviet urban hierarchy.

Key-words. ABM, Model-Building, System of Cities, Former Soviet Union, Evaluation, Incremental


Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns

diversityNew Paper, in PLoS ONE


Chérel G., Cottineau C., Reuillon R., 2015, « Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns », PLoS ONE 10(9): e0138212. doi:10.1371/journal.pone.0138212

Abstract. Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.

Key-words. Simulation and Modelling, Evolutionary Algorithm, Population Growth, Space Exploration, Urbanisation, Complex Systems.

A New Method to Evaluate Simulation Models: The Calibration Profile (CP) Algorithm

New publication, in JASSS : Journal of Artificial Societies and Social Simulation


Reuillon R., Schmitt C., De Aldama R., Mouret J.-B., 2015, « A New Method to Evaluate Simulation Models: The Calibration Profile (CP) Algorithm », JASSS : Journal of Artificial Societies and Social Simulation, Vol. 18, Issue 1, http://jasss.soc.surrey.ac.uk/18/1/12.html

Abstract. Models of social systems generally contain free parameters that cannot be evaluated directly from data. A calibration phase is therefore necessary to assess the capacity of the model to produce the expected dynamics. However, despite the high computational cost of this calibration it doesn’t produce a global picture of the relationship between the parameter space and the behaviour space of the model. The Calibration Profile (CP) algorithm is an innovative method extending the concept of automated calibration processes. It computes a profile that depicts the effect of each single parameter on the model behaviour, independently from the others. A 2-dimensional graph is thus produced exposing the impact of the parameter under study on the capacity of the model to produce expected dynamics. The first part of this paper is devoted to the formal description of the CP algorithm. In the second part,we apply it to an agent based geographical model (SimpopLocal). The analysis of the results brings to light novel insights on the model.

Key-words. Calibration Profile, Model Evaluation