Environment and Planning B: Urban Analytics and City Science

RELU-TRAN (Regional Economy Land Use and Transportation) is a spatial computable general equilibrium (CGE) model treating endogenous road congestion, housing and labor markets, and real-estate development consistent with microeconomic theory. The model has been calibrated and used for the Chicago Metropolitan Statistical Area and the Greater Paris Region, and is currently being implemented for the Greater Los Angeles metropolitan area. In the Chicago application the model has been used: (i) to examine the impact of an increase in the price of gasoline on travel and location patterns; (ii) to study how travel time, gasoline consumption and automobile emissions would evolve over time as the area grows in population and land area and as employment continues to disperse into the suburbs; (iii) to evaluate the effects of cordon tolling; and (iv) of road congestion versus a tax on gasoline. The main findings from these applications of RELU-TRAN are reviewed in this paper. The recent application to the Greater Paris region was aimed to study the effects of projected growth and of planned rail investments on concentrating jobs in growth poles around the City of Paris.

The paper examines the impact of communication-cost reductions on growth and welfare by means of an endogenous spatial growth model. Policy makers favouring certain infrastructure investments often claim that such projects have growth-stimulating effects that generate ‘wider impacts’ in terms of welfare, over and above producer and consumer surplus effects, which are typically measured by traditional cost–benefit analysis. It is well understood that such wider impacts cannot arise in a first-best environment with perfect competition and zero externality. But, if the market allocation is not first-best, wider effects do occur and can in principle be positive or negative. Recent literature has shown that freight-cost or commuting-cost reductions can have wider impacts. While large infrastructure projects, such as high-speed trains, have no direct effect on freight costs and little effect on commuting costs, they are nevertheless conjectured to generate wider impacts. The typical argument is that high-speed trains reduce the costs of face-to-face contacts. This promotes innovation, which in turn exerts a positive externality on growth. To verify these claims rigorously, I have set up a Romer-type endogenous growth model for a multiregional economy. In this model innovators need to learn from the existing stock of knowledge by communicating with others across space, which is a costly activity. I show that, at the margin, reducing these costs generates a welfare gain that consumers value more than the cost reduction itself.

We are building a series of fast, visually accessible, cross-sectional, hence static urban models for large metropolitan areas that will enable us to rapidly test many different scenarios pertaining to both short-term and long-term urban futures. We call this framework SIMULACRA which is a forum for developing many different model variants which can be finely tuned to different problem contexts and future scenarios. The models are multisector, dealing with residential, retail/service, and employment location, are highly disaggregate, and subject to constraints on land availability and transport capacities. They have an explicit urban economic focus around transport costs, incomes, and house prices and thus encapsulate simple market-clearing mechanisms. Here we will briefly outline this class of models, paying particular attention to their structure and the way physical flows and locations are mirrored by economic flows in terms of costs and prices. Several versions of the model now exist, but we will focus, first, on the simplest ‘one-window’ desktop pilot version with the most obvious graphical interface; and, second, on a much more elaborated framework developed for web access, extensible to web service architectures and other related services. To demonstrate its flexibility and intelligibility, we define the various interfaces and demonstrate how the aggregate model can be calibrated to the wider London region to which it is applied. We will demonstrate the model, albeit briefly with respect to the rapid assessment of different urban futures—“what-if” scenarios, based on the impact of new London airports in the Thames Estuary. The key feature of this entire project is that the model and its variants can be run in a matter of seconds, thus entirely changing the traditional dialogue associated with their use and experimentation.

Random utility modelling has been established as one of the main paradigms for the implementation of land-use spatial interaction (LUSI) models. We present a detailed formal description of a LUSI model that adheres to the random utility paradigm through the explicit distinction between utility and cost across all processes that represent the behaviour of agents. The model is rooted in a social accounting matrix, with the workforce and households accounts being disaggregated by socioeconomic type. Similarly, the land account is broken down by domestic and nondomestic land-use types. The model is developed around two processes. Firstly, the generation of demand for inputs required by established production; when appropriate the implicit production functions are assumed to depend on costs of inputs, which give rise to price-elastic demands. And, secondly, the spatial assignment of demanded inputs to locations of their production; here sequences of decisions are used to distribute demand both spatially and aspatially, and to propagate costs and utilities of production and consumption that emerge from imbalances between supply and demand. The implementation of this generic model is discussed in relation to the case of the UK. The model has been developed for testing the sustainability of integrated economic, spatial development policies, and output information for estimating urban form and the potential for decentralised technologies. The inputs include area-wide socioeconomic forecasts and the allocation policy of urban land. The outputs include the spatial allocation of activities and prices of labour, goods and services, land, and floorspace. They are combined with the land inputs to estimate the changes in the density of urban form and activities. These outputs can then be used to estimate the demands for infrastructure services and the potential for decentralised infrastructure supply. We focus primarily on the calibration process and its methodological implications, including a method of refining the calibration and demonstrate how this improves the spatial representation of the utility of land.

This paper presents a recursive spatial equilibrium model for urban activity location and travel choices in large city regions that anticipate major development or restructuring. In the model, producer and consumer choices that adjust quickly to stimuli reach temporary equilibria subject to recursively updated activity churn, background trends, estate development, and transport supply. The city region's performance at each time horizon affects the recursive variables for the next. The model builds on field leaders of urban general equilibrium, spatial interaction, and nonequilibrium dynamic models, and offers theoretical and practical improvements in order to fill an important gap in long-range urban forecasting. Linking the equilibrium and nonequilibrium models enables the simulation of path dependence in urban evolution trajectories that neither could produce in isolation. At the same time the model provides quantification of impacts of different policy interventions on a consistent basis for a given time horizon. The model is tested on the main archetypal urban development strategies for large-scale development and restructuring.

The use of equilibrium formulations in urban modelling is increasingly challenged by models that explicitly address the dynamics of urban change. Equilibrium models assume that urban land use and transport converge to equilibrium between supply and demand and focus on comparative static analysis of these equilibria. Dynamic models consider the different speeds of processes of urban change and concentrate on their outcomes over time and the path dependence this implies. It is becoming increasingly apparent that without understanding the inherent inertia of different subsystems of cities it is impossible to assess their likely responses to land-use or transport policies. With new challenges from energy scarcity and climate change, the time horizon of urban planning is extending beyond the present generation; this makes a long-term perspective of urban models even more important. In this paper our aim is to revive the debate on whether modelling intended to inform decision making can reasonably represent cities as if they were in or near equilibrium or whether it needs to recognise explicitly that they are continuously changing and far from equilibrium. We start with a classification of urban change processes by speed of adjustment and show how equilibrium models fail to deal with them. We discuss options of modelling dynamics and argue for recursive dynamics or quasi-dynamics as a rational trade-off between theory and operationality in spatially disaggregate urban models. We illustrate this by comparing how three existing recursive or quasi-dynamic urban models address temporal dynamics and close by suggesting research needs.

We compare the structural properties of the street networks of ten different European cities using their primal representation. We investigate the properties of the geometry of the networks and a set of centrality measures highlighting differences and similarities between cases. In particular, we found that cities share structural similarities due to their quasiplanarity but that there are also several distinctive geometrical properties. A principal component analysis is performed on the distributions of centralities and their respective moments, which is used to find distinctive characteristics by which we can classify cities into families. We believe that, beyond the improvement of the empirical knowledge on streets' network properties, our findings can open new perspectives into the scientific relationship between city planning and complex networks, stimulating the debate on the effectiveness of the set of knowledge that statistical physics can contribute for city planning and urban-morphology studies.

In this paper we analyse the role of walking accessibility to transit facilities. Microdata and GIS tools have been used to calculate distances walked by different population groups in accessing Metro stations. Distances walked by the population were used to determine the threshold distances of the station service areas and calculate the population covered by the Metro network. With respect to Metro ridership, different distance-decay functions were adjusted and the sensitivity of the population groups to the distance was measured. Two indicators were proposed, based on the distance-decay functions, to measure access quality and potential demand. The Madrid Metro network was used as the study area. Results show that young people and adults, men, immigrants, and public transit captives are willing to walk longer distances and are less sensitive to the effect of distance. When walking distances have been used in order to fix the limit of catchment areas, the amount of the population covered is lower than when a standard threshold (0.5 miles) is used, but overestimations affect each age group in a different way. The access quality indicator shows that the population group in the worst situation is children and that stations in the centre of the network have higher access quality values. However, the synthetic accessibility indicator shows that potential demand is lower for the most central and most peripheral stations than for the stations located in the intermediate areas. It has been proved that both indicators are sensitive to changes in the spatial distribution of population groups within the catchment areas. These results demonstrate some of the advantages of the proposed methodology and argue in favour of its use in public transport planning.

Existing online virtual worlds, or electronic environments, are of great significance to social science research, but are somewhat lacking in rigour. One reason is that users might not participate in those virtual worlds in the way they act in real daily life, communicating with each other in familiar environments and interacting with natural phenomena under the constraints of the human–land relationship. To help solve this problem we propose the real-geographic-scenario-based virtual social environment (RGSBVSE). The aim is to enhance the ability of current virtual worlds in social issues studies by promoting virtual geographic environments that are built with real scenarios in the physical world. In this paper we first discuss the potential shortage of current virtual worlds for serious social research. We then explain how real geographic scenarios can contribute to building a virtual social environment by providing (1) real geographic data, including the time dimension, in terms of data acquisition and organisation; (2) dynamic or real-time natural phenomena and processes for scenario simulation and expression; (3) shared spaces that enhance participants' interaction through a mix of virtuality and reality; and (4) shared hot spots of social phenomena for researchers from multidisciplinary (eg, sociology, psychology) performing collaborative research. Furthermore, two of our projects, the virtual Chinese University of Hong Kong and the Virtual Globe of the Chinese Family Tree, are introduced as case studies, to illustrate how the RGSBVSE can play a significant role in a number of critical social research issues from the local to regional scale.