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No “Prêt à Porter” but a Multi-scalar Perspective of “Smart Cities”
Abstract
We argue that there is no one-fits-all “smart city” recipe to address the sustainability and socio-economic challenges of our ever-urbanizing world. If smartness is the ability to deliver useful information to citizens and urban actors in order to adapt their behaviors and policies dynamically and interactively in view of a particular social, economic or environmental objectives, we here suggest that each city should not prioritize the same type of information and infrastructure. Because large cities are often seen as centers of innovation and modernity, it is very tempting for urban investors to propose, and for policy makers to follow these investment paths and develop information systems irrespective of the characteristics and size of the city. This potential mismatch may limit the uptake or the most relevant and useful information needed for a city to develop more sustainably and equally. We suggest that smart cities cannot ignore scaling effects nor the evident deviations to these laws. We hence propose to cross tabulate a smart city typology of infrastructure and information with a set of urban archetypes based on key dimensions of cities, including their spatial forms and extents but also their relative positioning within their regional setting, within the urban hierarchy and within their path-dependent trajectories. We see this cross-tabulation as a first step to anchor (big) data realities and smart city practices in geographic knowledge and urban complexity theory. We advocate that tailor-made smart city policies are necessary to monitor and manage cities given their geodiversity.KeywordsUrban diversityMulti-scalar typologyPath dependent urban trajectoriesTailor-made adaptive pathway
... It seems that the excessive mobility associated with the fragmentation of the value chains of globalized production has progressively decoupled urban expansion from the proper management of planetary resources (Rozenblat, 2018). It is unlikely that the technological solutions that are provided under the label of "smart cities" could easily solve these problems (Caragliu et al., 2011;Kourtit et al., 2020), especially because of the huge diversity of cities internal layouts and their already established networks operating internal and external interactions (Caruso et al., 2022). ...
Sustainable Development Goals are intrinsically competing, and their embedding into urban systems furthermore emphasises such compromises. When observed at the scale of systems of cities, such concern is considered as a series of innovations that challenges the adaptive capacity of urban systems. The spatial complexity, the non-optimal nature of such systems, and the multi-objective aspects of their agents, are among the reasons that raise difficulties when trying to adjust local policies through promoting innovation in order to satisfy at least a couple of SDGs simultaneously. As we lack enough empirical evidence, we propose in this paper to use a stylised simulation model for systems of cities, focused on innovation diffusion and population dynamics, to show how trade-offs may operate at such a scale. We proceed in particular to a bi-objective optimisation of emissions and innovation utilities, and show that no single urban optimum exists, but a diversity of regimes forming a compromise between the two objectives.
The contribution analyses the forms of regional knowledge, its sources and the characteristics of information that can be collected, filtered, represented and managed to extract knowledge to benefit territorial decision makers. Regional knowledge emerges and develops over long periods of time, shaping types of regional activities, vocations and industry structures. Thus, the main aspects of the definition of regional knowledge are the geographic and temporal dimensions. Starting from a categorization of regional knowledge into conscious knowledge, that is explicit knowledge held by individuals; objectified knowledge, that is explicit knowledge held by organizations and experts; preconscious knowledge, that is automatic knowledge held by individuals expressed unintentionally; and collective knowledge, implicit knowledge expressed in the practice of organizations, three frameworks for representing and managing information of different nature are indicated as suitable to extract different forms of knowledge, namely: non classical logics, and specifically fuzzy logic, to represent and reason on imprecise conscious and collective knowledge; machine learning to extract preconscious knowledge implicit in geo big data and created by both IoT and within social networks; Petri Nets to formalize objectified knowledge in the form of laws and regulations. For each form of knowledge case studies are illustrated and discussed highlighting the benefits of the adopted formal frameworks. Conclusions summarize the main message and open issues.KeywordsForms of knowledgeSources of regional knowledgeFuzzy logicsSpatio-temporal clusteringPetri nets
This paper aims to map out the significance of blockchain technology systems for smart spatial (urban and regional) strategies and applications. After a general introduction to blockchain application domains, it offers a critical review of, as well as operational design principles for blockchain possibilities in a smart spatial context. Next, a recent blockchain method to manage confidential spatial data easily and securely is described, including the use of the generic technology that enables applications with self-sovereign properties, called the PrivateSky Self-Sovereign Application (PS-SSApp). The design features of this blockchain system are then evaluated and related to the needs of data governance in a smart region or city, by comparison with existing technologies and methods, followed by retrospective and prospective observations on the potential of blockchain applications for smart cities and regions.KeywordsSelf-sovereignSmart cities and regionsBlockchainDigitalBig dataGovernanceDecentralization
Knowledge management technology is now ubiquitous in many businesses, yet it is much less common among local authorities. Following a “brainstorm meeting” in Lyon on knowledge management for regional planning and policymaking, a proposal was developed to set up a research agendum for future development of this topic. The main difficulty we identified is that regional knowledge encompasses many topics beyond business issues, especially those related to long-term sustainability. The goal of this paper is twofold, first to clarify the proper definitions and boundaries of regional knowledge and technology, and second, to identify the ways of using this technology effectively for governance and decision-making.KeywordsGeoAIRegional planningRegional policymakingKnowledge managementKnowledge representationKnowledge engineeringResearch agenda
As a central component of the smart city, sensor infrastructures locate and measure a wide range of variables in order to characterise the urban environment. Perhaps the most visible expression of the smart city, sensor deployment is a key equity concern. As new sensor technologies and Big Data interact with social processes, they have the potential to reproduce well‐documented spatial injustices. Contrary to promises of providing new knowledge and data for cities, they can also create new gaps in understanding about specific urban populations that fall into the interstices of data collection – sensor deserts. Building on emerging data justice debates, specifically considering distributional, recognition, and procedural forms of injustice, we conceptualise and analyse sensor deserts through two case studies, Newcastle's Urban Observatory (UK) and Chicago's Array of Things (USA). Open sensor locations are integrated with small‐area, socio‐economic data to evidence the demographic configuration and spatialities of sensor deserts across each city. We illustrate how the structural processes via which inequality is reinforced by smart agendas manifest as uneven social and spatial outcomes. In doing so, the paper opens up a new conceptual space in which to consider what it means (not) to count in the smart city, bringing a demographic perspective to critical debates about smart urbanisms. Contrary to promises of providing new knowledge for cities, smart city sensor technologies can create new gaps in understanding about urban populations that fall into the interstices of data collection – sensor deserts. Building on data justice debates, we conceptualise and analyse sensor deserts in two case studies, Newcastle's Urban Observatory (UK) and Chicago's Array of Things (USA), using small‐area socio‐economic data. We illustrate how the structural processes via which inequality is reinforced by smart agendas manifest as uneven social and spatial outcomes, bringing a demographic perspective to critical debates about smart urbanisms.
European cities may be regarded as having formed a system of well-interconnected entities over many centuries. The peculiarities of their hierarchical functional organization and territorial patterns have been extensively analysed. This study details a few contributions from the science of complex systems meant to formalize this knowledge. This includes a representation of the metropolisation process occurring within a system of cities with the help of scaling laws and network analysis. We define the metropolisation process – not at the local level of one metropolitan area but at a macro-geographical level – as the ability of larger cities to capture the activities related to innovation waves and to be the first to benefit from these in terms of population growth. A series of urban attributes are used to quantify, through exponents of scaling laws the differentiated behaviour of urban hierarchies when opening up to the global networks that characterize the most recent wave of innovation. Network analysis provides another type of formalism that helps us construct a better understanding of how globalization processes, especially the spread of multinational firms, have diffused in the Eastern part of the European urban system.
Advances in technology and the proliferation of data are providing new opportunities for monitoring and tracking the progress of the United Nations (UN) Sustainable Development Goals (SDGs) 1. As the latest framework for assessing and monitoring the alleviation of poverty, inequalities and environmental degradation, progress on meeting the 17 SDGs is evaluated through reporting on a hierarchy of 169 targets and 232 indicators 2 (Box 1). Here we argue that data produced through 'citizen science' 3 , which is the involvement of citizens in scientific research and/or knowledge production, can complement and ultimately improve the SDG reporting process. We demonstrate the value of using data from citizen science for the SDGs, providing concrete examples of how such data are currently being adopted in support of existing SDG indicators and their potential for contributing to future indicators. We start by examining issues related to traditional data used in SDG reporting and how the emergence of new sources can fill data gaps. We then place citizen science in the broader context of these non-traditional data streams available for SDG reporting, and highlight the value of citizen-science data for the SDGs. We conclude with a roadmap containing a set of actions for mainstreaming the use of data from citizen science into official SDG reporting at global and national levels, with a proposal for supporting activities at the local level. [THIS ARTICLE IS OPEN ACCESS: https://www.nature.com/articles/s41893-019-0390-3]
The size and form of cities influence their social and environmental impacts. Whether cities have the same form irrespective of their size is still an open question. We analyse the profile of artificial land and population density, with respect to the distance to their main centre, for the 300 largest European cities. Our analysis combines the GMES/Copernicus Urban Atlas 2006 land use database at 5 m resolution for 300 larger urban zones with more than 100,000 inhabitants and the Geostat population grid at 1 km resolution. We find a remarkable constancy of radial profiles across city sizes. Artificial land profiles scale in the two horizontal dimensions with the square root of city population, while population density profiles scale in three dimensions with its cube root. In short, cities of different size are homothetic in terms of land use and population density, which challenges the idea that larger cities are more parsimonious in the use of land per capita. While earlier literature documented the scaling of average densities (total surface and population) with city size, we document the scaling of the whole radial distance profile with city size, thus liaising intra-urban radial analysis and systems of cities. Our findings also yield homogenous spatial definitions of cities, from which we can re-question urban scaling laws and Zipf’s law for cities.
Despite its growing ubiquitous presence, the smart city continues to struggle for definitional clarity and practical import. In response, this study interrogates the smart city as a global discourse network by examining a collection of key texts associated with cities worldwide. Using a list of 5,553 cities, a systematic webometric exercise was conducted to measure hit counts produced by searching for ‘smart city’. Consequently, 27 cities with the highest validated hit counts were selected. Next, 346 online texts were collected from among the top 20 hits across each of the selected cities, and comprehensively analysed both quantitatively and qualitatively using AntConc software. The findings confirm, on one hand, the presence of a strong globalising narrative which emphasises world cities as ‘best practice’ models. On the other, they reveal the smart city’s association – beyond the quest for incremental, technical improvements of current urban systems and processes – with a pronounced transformative governance agenda. The article identifies five critical junctures (interlocking discourses) at the heart of the evolving smart city discourse regime; these shed light on the ongoing boundary work in which the smart city is engaged and which contain significant unresolved tensions. The paper concludes with a discussion of resulting implications for research, policy and practice.
The chapter develops a theory that encompasses as far as possible the existing state of knowledge in urban sciences for identifying regularities at the world scale and enabling worldwide comparisons of urban systems. Relying on the specific concepts in geography that consider cities as complex systems which self-organize and coevolve in systems of cities through their socio-spatial interactions, it provides an explanation of the emerging common properties of these systems, including their hierarchical organization and functional diversity. Major driving processes are the creative emulation in urban competition and the spatial diffusion of innovation that are renewing opportunities and challenges to urban stakeholders. Cities and systems of cities acting as socio-spatial adaptors can be considered as expressing a collective territorial intelligence because of the resilience of urban systems over long time periods. According to variations in space and time, such generic processes when contextualized and taking into account the path dependence effects are compatible with a geohistorical interpretation of urban diversity.
Zipf’s rank-size rule, lognormal distribution and Gibrat’s urban growth models are considered as summarizing fundamental properties of systems of cities. In this paper they are used as statistical benchmarks for comparing the shapes of urban hierarchies and evolutionary trends of seven systems of cities in the world including BRICS, Europe and United States. In order to provide conclusions that avoid the pitfalls of too small samples or uncontrolled urban definitions, these models are tested on some 20 000 urban units whose geographically significant delineations were harmonized in each country over fifty years between 1960 and 2010. As a result, if the models appear not always statistically valid, their usefulness is confirmed since the observed deviations from empirical data remain limited and can often be interpreted from the geohistorical context of urbanism proper to each world region. Moreover, the paper provides new free software which authorizes the reproducibility of our experiments with our data bases as well as with complementary data.
Socioeconomic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socioeconomic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socioeconomic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences.
This paper proposes an empirical analysis of the sensitivity of Discrete Choice Model (DCM) to the size of the spatial units used as choice set (which relates to the well-known Modifiable Areal Unit Problem). Job's location choices in Brussels (Belgium) are used as the case study. DCMs are implemented within different Land Use and Transport Interactions (LUTI) models (UrbanSim, ILUTE) to forecast jobs or household location choices. Nevertheless, no studies have assessed their sensitivity to the size of the Basic Spatial Units (BSU) in an urban context. The results show significant differences in parameter estimates between BSUs. Assuming that new jobs are distributed among the study area proportionally to the utility level predicted by the DCM for each BSU (as in a LUTI model), it is also demonstrated that the spatial distribution of these new jobs varies with the size of the BSUs. These findings mean that the scale of the BSU used in the model can influence the output of a LUTI model relying on DCM to forecast location choices of agents and, therefore, have important operational implications for land-use planning.
This second report is dedicated to the concept of ‘place’ revisited in the context of smart cities. Some recent studies suggest that today’s digital cities rely more on an approach to the urban context based on a network of connected places than on an approach to the city built on areal spaces. Does it mean that there are more places and fewer spaces in spatially enabled cities? Is the intelligence of a city mainly related to its ability to sound out the genesis of urban places? These issues raise questions about the design of spatial models used to build GIS, as well as place-based urban design methods and tools. This second report explores these questions from the standpoint of GISciences.
Despite the ongoing discussion of the recent years, there is no agreed definition of a ‘smart city’, while strategic planning in this field is still largely unexplored. Inspired by this, the purpose of this paper was to identify the forces shaping the smart city conception and, by doing so, to begin replacing the currently abstract image of what it means to be one. The paper commences by dividing the recent history of smart cities into two large sections – urban futures and the knowledge and innovation economy. The urban futures strand shows that technology has always played an important role in forward-looking visions about the city of the future. The knowledge and innovation economy strand shows that recent technological advancements have introduced a whole new level of knowledge management and innovation capabilities in the urban context. The paper proceeds to explicate the current technology push and demand pull for smart city solutions. On one hand, technology advances rapidly and creates a booming market of smart city products and solutions around it. On the other hand, there is demand on the side of cities that seek to address the problems of efficiency and sustainability, making the ground fertile for a smart city product economy. The research route of this paper eventually allows the identification of the underlying – and often forgotten – principles of what it means to be ‘smart’ in an urban context and yields conclusions about strategic planning for the development of smart cities today.
We analyze eight dimensions of the spatial structure of metropolitan regions in Europe. Our research is based on recent literature on the quantification of internal configurations of densities. We compute eight spatial structure indicators to achieve a classification of the major metropolitan regions (Urban Audit’s "Larger Urban Zones" with over 500,000 inhabitants), using the density grid of the European Environment Agency (2002). This work provides useful insights into the diversity of metropolitan structures in Europe, and sheds new light on the comparison between the actual structure of metropolitan regions and theoretical compact and polycentric spatial structures. Our findings emphasize the need to better account for metropolitan spatial structure through a multidimensional approach in order to formulate sustainable planning models fitted to the local context.
In this paper, we explore the hypothesis that contemporary geographies of economic activities within metropolitan areas in Europe are moving into polycentric configurations. Our contribution is based on results derived from an analysis of 7 metropolises that were brought together within the EU-funded research programme “COMET” (namely Amsterdam, Barcelona, Berlin, Brussels, Copenhagen, Strasbourg and Vienna). We first build on an enquiry of enterprises in these 7 cities, then document the nature and localisation of the multiple metropolitan (sub)centres by bringing together the expertise of scholars and institutional partners in each city - case study. The paper ends up with a discussion on the implications of contemporary intra-metropolitan reconfigurations for both urban researchers and policy-makers. Our results confirm the starting hypothesis, that is, far from a scenario of generalized dilution of the urban form, European metropolitan areas show the emergence of (diverse) configurations wherein urban functions are distributed among a series of (sub-)centres, with CBDs and inner-city districts no longer acting as sole location for key economic activities.
Two recent techniques are here used to delineate the urban agglomeration of Brussels : a fractal methodology for extracting the border between urban and not-urban surfaces, and a network analysis based on modularity applied on two different interaction matrices (telephone fluxes, commuting). In order to define spatial subsets in which relationships between places are more intense. These methods have the advantage of not depending upon a priori defined thresholds : thresholds are fixed by the methods and data themselves (endogenous). These methods have been applied on the entire territory of Belgium (Blondel, Krings and Thomas, 2010 ; Tannier and Thomas, 2012) and are here discussed for Brussels only. Results are further compared to more traditional delineations. Observed differences are mainly observed at the outskirts of the agglomeration but are very important in terms of policy : the urban-rural limit depends upon the criterion (definition, threshold) and method used. The two methods used here have the advantage of not depending upon arbitrary choices.
The design of regional plans is a difficult task combining political, economic, sociological and technological aspects. From decades, computers have been used for this task: at the beginning for mapping, then for spatial analysis and information systems, and now with artificial intelligence in the design of smart regions. The objective of this paper is to show some potentialities and promises of using artificial intelligence for regional planning, especially by using knowledge management, rule-based systems, case-based reasoning and machine learning. However, some limitations are still hindering its use, among them let us mention sociological acceptance and the existence of technological barriers.KeywordsArtificial intelligenceSmart regionsKnowledge managementMachine learningRule-based systemsUrban planningRegional planning
This paper provides a discussion of the choices that need to be made to classify locations and aggregate them into larger units such as cities or metropolitan areas. It also selectively surveys related literature with a focus on the contributions published in this special issue.
Cities’ delineation remains a hot topic of debate in a time where comparisons between cities are becoming increasingly based on different issues that address various scales of interventions and thus different concepts of cities. Aiming to compare cities and their insertion into globalization, we suggest that the “urban field of influence” is the best way to approach cities for this specific perspective. However, after reviewing the different existing possible concepts, we extend this concept with four complementary approaches: political entities, morphological agglomerations, functional urban areas and conurbations/Mega city regions. We discuss the top-down and bottom-up existing initiatives launched at the world scale and then use a mixed top-down and bottom-up approach to propose a new delineation of a large urban region (LUR), denoting a concept close to the conurbation or Mega city-region concept. The compositions of these LURs are published as an initial draft of an open database that can be criticized and improved by further contributions, suggesting the need for further critical comments and feed-back to improve them.
Scaling laws are simple, easily usable and proven relevant models used in geography for validating various urban theories. These non-linear relationships may reveal physical constraints on the structure and evolution of complex systems, and underline the relationship between urban functions, size of cities and innovation cycles. In this contribution, we examine to what extent scaling laws are transferable towards urban theories and in which specific fields of urban geography these models may be relevant. We thus focus on the accuracy of scaling laws when exploring structures and processes of systems of cities, the diffusion of innovation, metropolization and intra-urban dynamics. We therefore use several examples taken in different regions of the world, embedded in various historical, political and economic contexts. However, in some cases, care must be taken not to over-interpret the results obtained from scaling laws and not to give scaling laws more explanatory power than they can describe. We illustrate this point by providing recommendations relying for instance on the sensitivity of measurements to the delineation of each object of the system under study and to the definition of the system itself. These recommendations can help to get robust results in order to understand the generic evolutionary mechanisms in urban systems.
This book provides a thorough discussion about fundamental questions regarding urban theories and modeling. It is a curated collection of contributions to a workshop held in Paris on October 12th and 13th 2017 at the Institute of Complex Systems by the team of ERC GeoDiverCity. There are several chapters conveying the answers given by single authors to problems of conceptualization and modeling and others in which scholars reply to their conception and question them. Even, the chapters transcribing keynote presentations were rewritten according to contributions from the respective discussions. The result is a complete “state of the art” of what is our knowledge about urban processes and their possible formalization.
Scaling laws are simple, easily usable and proven relevant models used in geography for validating various urban theories. These non-linear relationships may reveal physical constraints on the structure and evolution of complex systems, and underline the relationship between urban functions, size of cities and innovation cycles. In this contribution, we examine to what extent scaling laws are transferable towards urban theories and in which specific fields of urban geography these models may be relevant. We thus focus on the accuracy of scaling laws when exploring structures and processes of systems of cities, the diffusion of innovation, metropolization and intra-urban dynamics. We therefore use several examples taken in different regions of the world, embedded in various historical, political and economic contexts. However, in some cases, care must be taken not to over-interpret the results obtained from scaling laws and not to give scaling laws more explanatory power than they can describe. We illustrate this point by providing recommendations relying for instance on the sensitivity of measurements to the delineation of each object of the system under study and to the definition of the system itself. These recommendations can help to get robust results in order to understand the generic evolutionary mechanisms in urban systems.
This paper proposes a novel methodology for delineating urban areas based on a machine learning algorithm that groups buildings within portions of space of sufficient density. To do so, we use the precise geolocation of all 12 million buildings in Spain. We exploit building heights to create a new dimension for urban areas, namely, the vertical land, which provides a more accurate measure of their size. To better understand their internal structure and to illustrate an additional use for our algorithm, we also identify employment centers within the delineated urban areas. We test the robustness of our method and compare our urban areas to other delineations obtained using administrative borders and commuting-based patterns. We show that: 1) our urban areas are more similar to the commuting-based delineations than the administrative boundaries but that they are more precisely measured; 2) when analyzing the urban areas’ size distribution, Zipf’s law appears to hold for their population, surface and vertical land; and 3) the impact of transportation improvements on the size of the urban areas is not underestimated.
L’urbanisation russe et soviétique a été tardive et accélérée, en comparaison d’autres ensembles territoriaux ; un grand nombre de villes nouvelles ont été créées par le régime soviétique qui a promu la fonction socialiste de la ville, l’organisation rationnelle de l’espace et la gestion planifiée de l’économie. Ces particularités urbaines et les multiples événements démographiques et politiques du XXe siècle font des villes de l’espace post-soviétique un ensemble qui pose question quant à sa comparabilité et à ses ruptures. Cette thèse vise à montrer que l’on peut appliquer le concept de système de villes et les méthodes générales de la géographie urbaine pour rendre compte de l’évolution urbaine dans l’espace post-soviétique à long terme, c’est-à-dire mieux comprendre et éventuellement prédire leur évolution. Nous avons confronté plusieurs modèles statistiques avec la dynamique urbaine observée et montré que la structure macro-géographique des villes était comparable à celle d’autres systèmes de villes. On a aussi observé des trajectoires particulières liées à la dimension du territoire, ses ressources naturelles, sa dynamique démographique récente, etc. La connaissance des évolutions observées a été intégrée dans une démarche incrémentale de simulation multi-agents qui vise, à partir d’hypothèses théoriques sur les mécanismes générateurs de l’urbanisation, à reproduire des faits stylisés généraux et particuliers, le plus parcimonieusement possible. L’évaluation progressive de modèles de plus en plus complexes a abouti à la reproduction de certaines évolutions observées et mis en valeur les trajectoire plus particulières de villes qui « résistent » à la modélisation.
Both theoretical and empirical studies have shown the ability of scaling laws to reveal processes of emergence in urban systems. Nevertheless, a controversy about the robustness of results obtained with these models on empirical cases remains, regarding for instance the definition of the ‘city’ considered or the way the estimations are performed. Another source of bias is highlighted in this contribution, with respect to the non-ubiquitous character of some urban attributes (i.e. their partial absence from several cities of the system). The problem with the zero count for cities where these attributes are absent is that the technical necessities of usual estimation procedures make the analysis ignore them altogether even when they represent some valid information. This could seriously impact the results. A precise exploration of the effects of this arbitrary filtering is conducted here, and several solutions are proposed to overcome this limitation. In a case study about foreign investment towards French cities, we show that some erroneous conclusions about a hierarchical diffusion could be drawn when adopting the classical ordinary least squares approach. The framework we suggest specifies how it is possible to avoid misinterpretations deriving from the exclusion of zero values by using methods of analysis which deal with zero values specifically. The conclusion of a diffusion of foreign investment in the French urban system is then rejected.
Urban systems share with other complex systems constraints on their dynamics that are revealed by pervasive structural features, among which scaling laws. Scaling laws are relationships between cities’ attributes and their size (here measured by their population). When the relationship is non proportional with exponents larger than 1, scaling laws indicate the relative concentration of some urban functions at the higher levels of urban hierarchies. Superlinear scaling thus reveals the metropolisation trends that are produced in the urban system, according to our evolutionary theory perspective, by the hierarchical diffusion of innovation waves. Considering the current urban changes linked with the globalisation processes as an ‘innovation’ that is likely to diffuse hierarchically in urban systems, we analyse the relationships between 25 indicators expressive of their position in globalisation processes and the size of European cities (356 largest functional urban areas of the 28 European Union member states plus Switzerland and Norway). When summarised in a single metropolisation factor, we expected to find a unique superlinear scaling relationship that would reveal the hierarchical structure of the unifying European system of cities. We instead identify two distinct metropolisation gradients for each of the Western and Eastern subsystem that we interpret according to the delayed globalisation process in the latter. This provides a demonstration of the usefulness of scaling laws for summarising stages in the process of hierarchical diffusion of innovation in systems of cities.
This paper stresses the lack of attention paid to the geographical definitions of cities in LUTI models as one key detrimental aspect to transferring and generalising LUTI results. First, the argumentation develops from a meta-analysis of peer-reviewed publications about LUTI applications in European cities. We show that most authors do not assess findings against potential geographical biases. Second, theoretical simulations are conducted with UrbanSim applied to a synthetic urban area. By varying the geographical limits of the system and population endowments, our simulations confirm that the absence of control on city delineation weakens the results. Finally, the paper suggests methodological guidelines to improve the comparability of LUTI applications and push forward their theoretical agenda.
Scaling laws are powerful summaries of the variations of urban attributes with city size. However, the validity of their universal meaning for cities is hampered by the observation that different scaling regimes can be encountered for the same territory, time and attribute, depending on the criteria used to delineate cities. The aim of this paper is to present new insights concerning this variation, coupled with a sensitivity analysis of urban scaling in France, for several socio-economic and infrastructural attributes from data collected exhaustively at the local level. The sensitivity analysis considers different aggregations of local units for which data are given by the Population Census. We produce a large variety of definitions of cities (approximatively 5000) by aggregating local Census units corresponding to the systematic combination of three definitional criteria: density, commuting flows and population cutoffs. We then measure the magnitude of scaling estimations and their sensitivity to city definitions for several urban indicators, showing for example that simple population cutoffs impact dramatically on the results obtained for a given system and attribute. Variations are interpreted with respect to the meaning of the attributes (socio-economic descriptors as well as infrastructure) and the urban definitions used (understood as the combination of the three criteria). Because of the Modifiable Areal Unit Problem (MAUP) and of the heterogeneous morphologies and social landscapes in the cities' internal space, scaling estimations are subject to large variations, distorting many of the conclusions on which generative models are based. We conclude that examining scaling variations might be an opportunity to understand better the inner composition of cities with regard to their size, i.e. to link the scales of the city-system with the system of cities.
This third report examines interfaces as a key element enabling spatial skills, and development of new forms of digital spatialities for smart cities. Digital technology is becoming consubstantial to urban materiality, but map interfaces are particularly central tools for indexing (geographic) knowledge and expertise, accessing informational components of digital cities, and actively engaging digital dimensions of urban places.
Scaling laws, when applied to geographical entities, reveal the configuration of the dynamic processes that generate inequalities in dimension. Two interpretations of their application to city systems are discussed here. According to physicists, the exponent value of power laws could differentiate the urban activities that are liable to achieve scale economies, i.e. those with exponent values smaller than one, from those that are merely proportional to the population because they meet universal needs, while others, with exponents greater than one, are seen as being accompanied by increasingly rapid growth and the risk of crises. This cross-sectional interpretation in terms of the longitudinal trajectory of an individual city assumes that the city system is ergodic. Yet this hypothesis is not consistent with an evolutionary theory of urban systems integrating the spatial distribution of labour and the hierarchical diffusion of innovation.
The spatial economy can be visualised as a web of overlapping markets or functional areas linked to different activities. Unfortunately, very often administrative regions and local authority areas are used as proxies for functional areas, but they have serious shortcomings for spatial economic analysis and the implementation of local policies. The spatial economy is viewed here as a complex network of economic flows within a hierarchical urban system. This paper constructs a comprehensive geography of functional economic areas by first using consistent criteria based on commuting and migration flows for England. The analysis next superimposes a central place hierarchy derived from retail sales and office centre rents. It then classifies these functional economic areas offering insights into the spatial economy of England. The geography queries the relevance of core/surrounding models of urban form as large local labour market areas comprise in some cases more than 20 housing market areas. While the research uses England as a case study, the analysis is of generic significance to the spatial economies of developed countries. The paper also raises a number of methodological and data issues for further development.
The relationship between urban development and transport is not simple and one way but complex and two way and is closely linked to other urban processes, such as macroeconomic development, interregional migration, demography, household formation, and technological innovation. In this chapter, one segment of this complex relationship is discussed: the two-way interaction between urban land use and transport within urban regions. The chapter looks at integrated models of urban land use and transport, i.e., models that explicitly model the two-way interaction between land use and transport to forecast the likely impacts of land use and transport policies for decision support in urban planning. The discussion starts with a review of the main theories of land-use transport interaction from transport planning, urban economics, and social geography. It then gives a brief overview of selected current operational urban models, thereby distinguishing between spatial-interaction location models and accessibility-based location models, and discusses their advantages and problems. Next, it reports on two important current debates about model design: are equilibrium models or dynamic models preferable, and what is the most appropriate level of spatial resolution and substantive disaggregation? This chapter closes with a reflection of new challenges for integrated urban models likely to come up in the future.
The multilevel comparison of spatial and hierarchical organisations of urban systems over the world highlights some generic and universal properties (rank-size law, center-periphery structure) but also a variety of more specific patterns (in terms of spatial repartition of populations, densities, prices, activities, etc.). The spatial economy and the urban evolutionnary theory both focus on the explanation of the emergence of such patterns, but the simulation models they support classicaly consider one level of spatial organisation only, respectively intra- and inter-urban. Understanding and reconstructing those levels' interdependancies is a crucial issue for long-term sustainable urban planning. This thesis presents a set of models and tools that are dedicated to the study of this question through agent-based simulation. They have been developed in the context of the Simpop project, and particulary on the comparison of the morphogenesis of urban systems in Europe and in the United States over the period 1800-2000. These tools include the simpopNano agent-based model, and some experimentation modules gathered in an extensible and generic GIS-based platform, which is dedicated to a systematic, collective and intelligent exploration of spatial simulation models. Together they reinforce the idea that the sole difference of topology of the streets networks could be sufficient to generate some more diluted spatial repartitions, as observed in US cities when compared to european ones. This intra-urban model is then articulated with an inter-urban one, Simpop2, in a multilevel model. The latter serves to engage a comparison among a variety of approaches in agent-based simulation litterature for integrating models of multiple levels of abstraction.
Les theories urbaines, en particulier economiques, reposent pour la plupart sur une conception statique de la ville (par exemple, la theorie des lieux centraux, ou la theorie des economies d'agglomeration), ce qui conduit a des explications du fait urbain qui ne prennent pas en compte la genese ni le changement, et donc reduit la pertinence des previsions. Pour traiter des villes, qui sont des objets complexes, la theorie urbaine doit integrer des aspects temporels, non seulement au sens de la dynamique, mais aussi du devenir, de l'histoire. Nous proposons ici les premisses d'une theorie evolutive des systemes de villes. Quelques elements de cette theorie peuvent etre empruntes aux modeles dynamiques d'auto-organisation inspires des theories des systemes physiques ou vivants, mais il faut y ajouter des proprietes, spatiales et sociales, specifiques des systemes urbains.
Why are some cities specialised and others diversified? What are the advantages and disadvantages of urban specialisation and diversity? To what extent do the structure of cities and the activities of firms and people in them change over time? How does the sectoral composition of cities influence their evolution? To answer these and related questions, we first distil some key stylised facts from the empirical literature on cities and the composition of their activities. We then turn to a review of different theories looking at such issues, and study the extent to which these theories contribute to the understanding of the empirical regularities.
In this article we use the SLEUTH model and publicly available datasets to develop a stylized planning application for Mumbai, India. We use two sets of model specifications that reflect the two regional extents at which Mumbai region is planned: (1) the jurisdiction of the Municipal Corporation of Greater Mumbai (MCGM), which is responsible for the central city and surrounding suburbs, and (2) the planning area of Mumbai Metropolitan Regional Development Authority (MMRDA), which is a loose collection of a much larger set of municipalities and districts. Using these, we illustrate how urban models can be limiting as a predictive tool but useful as an assessment mechanism, especially when additional considerations of scale and institutional roles are applied. We compare the outcomes for the overlapping geographic area between the two planning agencies and find considerable variations in the location and amount of growth and discuss why and how the differences in the spatial extents affect the model results. Through a discussion on the implications for modelers and planners, especially in light of the ongoing initiatives in India, we highlight the value of consideration of multiple model outcomes and highlight the importance of coordination of planning efforts taking place in spatially overlapping or nested jurisdictions.