Environment and Planning B: Urban Analytics and City Science

In this paper, we propose a multilevel network perspective for the conceptualization of the dynamics underlying supply chains in the presence of competition. The multilevel network consists of: the logistical network, the informational network, and the financial network. We describe the behavior of the network decisionmakers, which are spatially separated and which consist of the manufacturers and producing firms, the retailers, and the consumers located at the demand markets. We propose a projected dynamical system, along with stability analysis results, that captures the adjustments of the commodity shipments and the prices over space and time. A discrete-time adjustment process is described and implemented in order to illustrate in several numerical examples the evolution of the commodity shipments and prices to the equilibrium solution.

This paper discusses the recent and future operational performance of the Russian electricity-generation industry, which is central to government plans for industrial regeneration and environmental improvement. The paper commences with information on outputs from the industry, namely current and future production levels, and atmospheric emissions, followed by data on fuel and capacity inputs. Information is then presented to show that the meeting of future output targets may run counter to objectives for reduction of greenhouse gas emissions, in view of the slow replacement of ageing plant and the plans to switch fuel from natural gas back to coal. The paper then discusses the economic and commercial factors influencing the strategic choices available to the Russian power-generation industry and the barriers to the implementation of these alternatives. The conclusion is reached that substantial investment is required in Russian power stations, which may require significant increases in electricity prices with associated economic and social problems for industrial and domestic consumers. The pricing system could also be used to reduce demand to match the capacities of available equipment, but that decision may also give rise to associated economic and social problems.

Urban growth dynamics attracts the efforts of scientists from many different disciplines with objectives ranging from theoretical understanding to the development of carefully tuned realistic models that can serve as planning and policy tools. Theoretical models are often abstract and of limited applied value while most applied models yield little theoretical understanding. Here we present a mathematically well-defined model based on a modified Markov random field with lattice-wide interactions that produces realistic growth patterns as well as behavior observed in a range of other models based on diffusion-limited aggregation, cellular automata, and similar models. We investigate the framework's ability to generate plausible patterns using minimal assumptions about the interaction parameters since the tuning and specific definition of these are outside of the scope of this paper. Typical universality classes of the simulated dynamics and the phase transitions between them are discussed in the context of real urban dynamics. Using suitability data derived from topography, we produce configurations quantitatively similar to real cities. Also, an intuitive class of interaction rules is found to produce fractal configurations, not unlike vascular systems, that resemble urban sprawl. The dynamics are driven by interactions, depicting human decisions, between all lattice points. This is realized in a computationally efficient way using a mean-field renormalization (area average) approach. The model provides a mathematically transparent framework to which any level of detail necessary for actual urban planning application can be added.

Much of the theoretical and empirical debate about transport and land-use planning has focused upon the strength and vitality of the connection between the two. Studies increasingly find that this connection is weakening and thus attempts to address urban transport problems with land-use policies are ineffective. The author introduces proximate commuting, a novel employer-based program that decreases urban commuting by providing marginal accessibility improvements to its participants. With the aid of a case study involving a commercial bank in the Western Detroit Metropolitan Area, the author examines individuals' motivations for participating in a proximate-commuting program. Results show that 25% of bank tellers surveyed state that they are willing to take advantage of this accessibility-improvement program. Estimation of a discrete-choice model reveals that gender; expected improvements in accessibility; and better job prospects are three key factors explaining individuals' stated willingness to proximate commute. To the extent that these results hold more generally, they underscore the usefulness of interventions that rely on marginal improvements of home-to-work accessibility. Furthermore, the case illustrates an innovative approach for addressing transport challenges that is both politically palatable and enhances individual choices.

The authors report on web-based communications between professional planners and graduate students in Washington State, focusing on growth management issues. The website gmforum included: solicitations of advice from the planners; a moderated listserv; traditional course materials; statements on growth-management topics with links to other websites; and facilities for personal e-mail communications. A survey of the planners and the students showed a high degree of satisfaction with the outcomes.

This paper examines the distinctions between empirical and simulation models using the metaphors of argument and narrative. It argues that all argumentation is contextualized within a narrative that is either inferred or communicated. It provides another semantic structure for urban models that applies elements of systems-dynamic method to construct ‘stories' of the past and possible futures of communities in a watershed in southern Arizona. By constructing such narratives this paper demonstrates how computer-based urban models can ‘tell a story’.

The need to implement shape grammars efficiently, rather than hardcode them, in a way that supports creativity through shape emergence is an ongoing research challenge. This paper introduces a shape grammar interpreter that supports parametric subshape recognition, and thereby shape emergence. The approach divides shapes into hierarchies of subshapes based on specified geometric relationships within the shape. A default hierarchy based on geometric relations often found in engineering and architectural designs is presented as an efficient example of one appropriate hierarchy. The interpreter's shape recognition and generation abilities are demonstrated with two examples: a new engineering shape grammar for the design of vehicle inner panels and a modified version of the classical ice-ray shape grammar.