Elisabete A. Silva

Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal

The SLEUTH model (slope, landuse, exclusion, urban extent, transportation and hillshade), formerly called the Clarke Cellular Automaton Urban Growth Model, was developed for and tested on various cities in North America, including Washington, DC, and San Francisco. In contrast, this research calibrated the SLEUTH model for two European cities, the Portuguese metropolitan areas of Lisbon and Porto. The SLEUTH model is a cellular automaton model, developed with predefined growth rules applied spatially to gridded maps of the cities in a set of nested loops, and was designed to be both scaleable and universally applicable. Urban expansion is modeled in a modified two-dimensional regular grid. Maps of topographic slope, land use, exclusions, urban extents, road transportation, and a graphic hillshade layer form the model input. This paper examines differences in the model’s behavior when the obviously different environment of a European city is captured in the data and modeled. Calibration results are included and interpreted in the context of the two cities, and an evaluation of the model’s portability and universality of application is made. Questions such as scalability, sequential multistage optimization by automated exploration of model parameter space, the problem of equifinality, and parameter sensitivity to local conditions are explored. The metropolitan areas present very different spatial and developmental characteristics. The Lisbon Metropolitan Area (the capital of Portugal) has a mix of north Atlantic and south Mediterranean influences. Property is organized in large patches of extensive farmland comprised of olive and corkorchards. The urban pattern of Lisbon and its environs is characterized by rapid urban sprawl, focused in the urban centers of Lisbon, Oeiras, Cascais

Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese metropolitan areas

We explore the simulation of urban growth using complex systems theory and cellular automata (CA). The SLEUTH urban CA model was applied to two different metropolitan areas in Portugal, with the purposes of allowing a comparative analysis, of using the past to understand the dynamics of the regions under study, and of learning how to adapt the model to local characteristics in the simulation of future scenarios. Analysis of the two case studies show the importance of SLEUTHs self-modification rules in creating emergent urban forms. This behavior can help build an understanding of urban social systems through this class of CA.

Artificial Intelligence Solutions for Urban Land Dynamics: A Review

Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and dynamic problems in urban studies. The goal of this article is a review of current literature in the field of planning and AI. The aim of this review is to increase the understanding of how AI approaches urban and land dynamics modeling processes and how, as a result, researchers can structure that knowledge and choose the correct approaches to embed in their models. For this purpose, the authors review the applications of AI techniques in urban land dynamics domain as well as the emerging challenges they face. The authors discuss hybrid AI systems as a need resulting from the trend in planning policy to develop more holistic approaches. The authors conclude that, although challenges exist, AI-based approaches offer promising solutions for urban and land dynamics.

Surveying Models in Urban Land Studies

Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety of models dealing with the manifold processes of urban change. This article reviews the models discussed in the literature in order to understand the most important aspects in modeling for urban studies currently. It starts by making a comprehensive review of the sixty-four models mentioned in the literature, and then “zooms in,” detailing only the dynamic models (considered at the forefront of the fifth generation of systems theory). The issues explored include each model’s subject areas and main goals, scales of analysis, and technologies used. Building on the previous criteria, the article aims to present a guide to existent models and point out future directions toward new technology developments in urban studies.

Effects of the Modifiable Areal Unit Problem on the Delineation of Traffic Analysis Zones

Transportation analysis is typically thought of as one kind of spatial analysis. A major point of departure in understanding problems in transportation analysis is the recognition that spatial analysis has some limitations associated with the discretization of space. Among them, modifiable areal units and boundary problems are directly or indirectly related to transportation planning and analysis through the design of traffic analysis zones (TAZs). The modifiable boundary and the scale issues should all be given specific attention during the specification of a TAZ because of the effects these factors exert on statistical and mathematical properties of spatial patterns (ie the modifiable areal unit problem—MAUP). The results obtained from the study of spatial data are not independent of the scale, and the aggregation effects are implicit in the choice of zonal boundaries. The delineation of zonal boundaries of TAZs has a direct impact on the reality and accuracy of the results obtained from transportation forecasting models. In this paper the MAUP effects on the TAZ definition and the transportation demand models are measured and analyzed using different grids (in size and in origin location). This analysis was developed by building an application integrated in commercial GIS software and by using a case study (Lisbon Metropolitan Area) to test its implementabiity and performance. The results reveal the conflict between statistical and geographic precision, and their relationship with the loss of information in the traffic assignment step of the transportation planning models.

Simulating the Dynamics Between the Development of Creative Industries and Urban Spatial Structure: An Agent-Based Model

Creative industries have been widely adopted to promote economy growth, urban regeneration and innovation. It is expected that this strategy can produce a sustainable development model. However, in reality it is not effective enough because the implemented policy based on linear analysis is misleading. This chapter aims to fill this gap by examining the dynamics among creative industries, urban land space and urban government from a complex systems’ view. It presents a general simulation framework and an agent-based model (named “CID-USST”) developed in NetLogo. This is a spatially explicit model where a simplified urban space is used to represent the real urban land space. The agents involved include the creative firms, the creative workers, and the urban government. The resulting urban spatial structure is examined from two aspects: the spatial density distribution and the spatial clustering pattern of both the creative firms and the creative workers.

Zoning Decisions in Transport Planning and Their Impact on the Precision of Results

In most transport planning studies, one of the first steps is the definition of a zoning scheme into which the study area is divided and the corresponding space is discretized. There are no clear rules on how to carry out this operation in an optimal way, and the dominating practice is to proceed on the basis of experience, trying to mix a certain degree of within-zone homogeneity and the convenience of using administrative borders as zone limits. The potential errors generated with this operation were examined, both at the statistical level when trip matrices are based on sampling and at the geographical level when all trips starting or ending in a zone are assumed to do so at its centroid. A set of quality criteria for a general zoning scheme and an algorithm that constructs a basic zoning on the basis of a sample of geo-referenced trip extreme points and improves it in successive steps according to those criteria are presented. A case study based on the mobility survey for the Lisbon, Portugal, metropolitan area illustrates those steps and the improvements achieved in each step. The magnitude of those improvements is significant and shows that more attention should definitely be given to this initial process in the transport planning studies.

Expert Knowledge in Land Use Planning: The Role of Information in Workshops, Scenario Building, Simulation Modelling and Decision Making

What role does expert information play in the planning process, and how can the role be enhanced by integrating information management and simulation modelling tools into a participatory decision-making exercise? We examine these questions through a survey of pertinent planning literature, and a case study. This paper reports a workshop with expert participants, which developed a set of future scenarios for Portugals two largest metropolitan areas (Lisbon and Porto, Portugal). A methodology was set up targeting the inclusion of expert knowledge into the modelling process. The research and case study led to three principal conclusions: (1) that it is possible and profitable to include knowledge in decision-making that results from participation from a diversity of experts; (2) that what the experts considered to be the spatial output of their suggestions might be different from simulations that result from modelling; (3) that simulations based on policies that control and constrain the behaviour of urban systems can result in simulation outcomes that were unanticipated by the experts especially in their resultant spatial distributions.

Block-level changes in the socio-spatial landscape in Beijing: Trends and processes:

Socio-spatial differentiation has been identified not only in capitalist market economies but also in transitional countries, including post-reform China. However, most prior studies on this topic in Chinese cities are limited to a spatial resolution of the sub-district level, and finer scale analyses remain scarce. Drawing on this gap, this article provides a block-level examination of the socio-spatial changes in the central city of Beijing by employing the Beijing Travel Survey data, which contain rich socio-economic information. Latent class analysis and GIS visualisation are used to stratify the residents into different levels of socio-economic well-being based on multiple attributes and analyse their spatial distribution. The findings reveal a dramatic transformation of the socio-spatial landscape in Beijing in only five years between 2005 and 2010; 90% blocks show a greater than 10% increase or decrease in the average social stratification index of their residents. The socio-spatial changes can to a large extent be related to the market reform of China’s economy and housing distribution, which exerts its influence through not only commercial developments but also profound interactions with the public sector.

Examining the dynamics of the interaction between the development of creative industries and urban spatial structure by agent-based modelling: A case study of Nanjing, China:

Much of the focus of research on creative industries’ influence upon urban land use has been around the investment in specific regeneration projects or flagship developments rather than addressing the nature and location of the infrastructure, networks and agents engaged. In other words, the complexity of the institutional/temporal and spatial interaction among the involved elements is overlooked or not well understood. This paper presents an agent-based model named CID-USST (Creative Industries Development-Urban Spatial Structure Transformation) that examines the dynamics of the interaction between the development of creative industries and urban spatial structure by outputting a set of adaptive scenarios through time and space. It reveals that the spatial distribution of both the creative firms and the creative workers evolves in a repeating up-and-down pattern even when the exogenous urban economic condition is set to be steady. Moreover, the analysis also points to the policy implication that more open job/rent market information will lead to more rapid geographical clustering of the creative firms and the creative workers, which possibly may reduce the time cost in their spatial evolvement, and perhaps accelerate innovation if we accept that geographical proximity can enhance knowledge and information spill-over.