Strategies for landscape ecology: An application using cellular automata models

Progress in Planning 70 (2008) 133–177 www.elsevier.com/locate/pplann

Strategies for landscape ecology: An application using cellular automata models Elisabete A. Silva a,*, Jack Ahern b, Jack Wileden c a

b

Department of Land Economy and Robinson College, University of Cambridge, 19 Silver Street, Cambridge CB3 9EP, UK Department of Landscape Architecture and Regional Planning (LARP), University of Massachusetts, Amherst, MA 01003, USA c Computer Science Department (CSSID), University of Massachusetts, Amherst, MA 01003, USA

Abstract The countervailing cellular automata (CVCA) is a loose coupled program designed to work in conjunction with SLEUTH (an urban cellular automata simulation model). CVCA operationalises a set of landscape ecological strategies for urban planning. CVCA first assesses a landscape against a set of landscape metrics. It then evaluates the proposed urban cells from SLEUTH against the metrics and allocates future land uses according to a suite of planning strategies (offensive, defensive, protective, opportunistic). This paper describes the development of CVCA, the theory behind the landscape strategies, and the actions taken to bring CVCA into a computable environment. An application of CVCA in two metropolitan areas in Portugal (Porto and Lisbon) is made and discussed. The results of implementing it are then discussed and evaluated. The paper concludes that the implementation of the cellular automata model CVCA, loose coupled with SLEUTH (a cellular automaton urban model), provides a robust and useful application of landscape ecological strategies in metropolitan planning. The applied strategies vary locally as a function of the specifics concerning particular patterns and processes to be promoted. Besides the quantitative analysis they provide, these patterns and processes can also be assessed and compared in terms of the resulting images of urban growth, the location and the shape of corridors, buffers, and the relative importance of the different landscape ecological planning strategies. The paper ends with a comprehensive discussion of the four main subject areas, in the context of cellular automaton and dynamic models: the importance of integrated planning strategies for the territory; the lack of planning tools; the importance of cellular automaton approaches and applications; and the results of applying SLEUTH and CVCA to two metropolitan areas. # 2008 Elsevier LtdElsevier B.V. All rights reserved. Keywords: Cellular automata; SLEUTH; CVCA; Metropolitan planning; Integrated regional planning; Landscape ecological strategies; Planning strategies; Lisbon; Porto; Portugal

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Landscape ecology and its application to metropolitan planning . . . . . . . . . . . 2.1. Patches, corridors and pattern-process dynamics. . . . . . . . . . . . . . . . . . 2.2. Including patches and corridor dynamics in a holistic planning approach

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

* Corresponding author. Tel.: +44 1223337141; fax: +44 1223337130. E-mail addresses: [email protected] (E.A. Silva), [email protected] (J. Ahern), [email protected] (J. Wileden). URL: http://www.landecon.cam.ac.uk/staff/profiles/esilva.htm 0305-9006/$ – see front matter # 2008 Elsevier LtdElsevier B.V. All rights reserved. doi:10.1016/j.progress.2008.05.002

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

134 135 136 136

134

3.

4.

5.

6.

7.

8.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Range of model approaches available . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Application of cellular automata models to land use planning . . . . . . . . . . . . . . . . 3.2. Trends in integrating CA simulations into landscape ecology. . . . . . . . . . . . . . . . . 3.3. State of the art in urban growth simulation of landscape ecology . . . . . . . . . . . . . . 3.4. Coupling ecological principles to urban growth models. . . . . . . . . . . . . . . . . . . . . Development and interoperability of the SLEUTH and CVCA cellular automata models . . 4.1. Model structure and requirements for SLEUTH and CVCA. . . . . . . . . . . . . . . . . . 4.2. Selection of the landscape metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. The metrics of CVCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Landscape shape index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Mean patch size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4. Mean nearest neighbour distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Structure and functioning of the countervailing cellular automata (CVCA) model . . 4.3.1. Grid space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2. Neighbourhood effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3. Transition rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4. Time steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lisbon and Porto Metropolitan Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Lisbon Metropolitan Area (AML). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Porto Metropolitan Area (AMP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Application of CVCA future simulations to Lisbon and Porto . . . . . . . . . . . . . . . . . . . . . 6.1. Assessment of the metrics computed by CVCA . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Simulating future urban environments for the Lisbon and Porto Metropolitan Areas. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Results of applying SLEUTH and CVCA to two metropolitan areas. . . . . . . . . . . . 7.2. Importance of cellular automaton approaches and applications. . . . . . . . . . . . . . . . 7.3. Lack of planning tools and the benefits/difficulties of coupling models . . . . . . . . . . 7.4. Importance of integrated ecological and urban planning strategies . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Traditionally, urban and environmental analysis have been seen as different fields, with different data required, and different metrics and models proposed. The increasing perception that sustainable management of landscapes needs to include both dimensions is, however, creating the need for integrated urban models, which present holistic approaches to both fields. The potential for integrated planning and the development of dynamic models is therefore a welcome development in metropolitan planning. Dynamic models allow for the inclusion of the complexity required to engage rigorously in integrated planning. The group of dynamic models detailed here also allows the inclusion of local environmental variability and its interaction with urban processes. Sensitivity to local conditions is a major concern in modelling. The main challenge is to develop models that include robust theoretical considerations and that are sensitive to local characteristics. Cellular automaton

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 138 139 140 140 142 142 145 145 145 146 146 146 147 147 147 148 152 153 154 155 159 160 166 166 168 170 171 172 173 173 176

is an approach capable of answering both needs when creating urban and environmental models. The capacity of these models to simulate local behaviour according to a set of rules of neighbourhood, several cell states, and time constraints, is being recognised as valuable when planning complex systems, while simultaneously assuring the portability and adjustability of the model to local characteristics. Two models will be explored in detail: the SLEUTH model (an urban cellular automaton model) and the CVCA model (an environmental model). While SLEUTH was developed some years ago and is widely known, the CVCA was recently developed and acts as a countervailing cellular automaton to SLEUTH urban dynamics. The CVCA model was conceived to provide answers to questions such as: What is the state of the landscape? Which landscape strategies are predominant? How can one create a different image of the metropolitan area? What is the dominant pattern (corridor, patch)? Are the strategies promoting connectivity? Which landscape

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

metrics increase the dominant strategy? In order to answer these questions, several components of the CVCA model had to be defined, including landscape metrics, decision rules, and interaction with the urban model. This paper and the research it presents, emphasises the importance of patterns and processes for the sustainability of urbanising metropolitan regions. These highly populated landscapes have been subjected to intense pressures. The existing land cover is typically highly fragmented and few of the ecological systems have been sustained (Silva, 2000; Silva, 1999). This paper is organised according to eight points that can be grouped in two main parts. The first part addresses methodological issues: the importance of landscape ecology and its application to metropolitan planning; the range of model approaches available; the cellular automata models; and the interoperability of SLEUTH and CVCA. The second part of the paper addresses the implementation of CVCA and its interaction with SLEUTH, starting with a description of the two case studies (the Lisbon and the Porto Metropolitan Areas); the application of the models to these two metropolitan areas; followed by an extended discussion; and ending with some conclusions. 2. Landscape ecology and its application to metropolitan planning The need to include the environmental component in regional planning and landscape ecology concepts is widely accepted as the appropriate basis for environmental planning in urbanising regions (Dramstad, Olson & Forman, 1996; Forman, 1995; Geneletti, 2008; Hersperger, 1994; Nassauer & Corry, 2004; Ndubisi, 1997; Swaffield & Primdahl, 2004; Tippett, Handley & Ravetz, 2007; White & Ellis, 2007). The need for integrated metropolitan planning has also been raised by many (Ahern, 1998; Benson & Roe, 2000; Dunos & Watson, 2002; Masri & Moore, 1993; Naveh, 2001; Soncini-Sessa, Castelletti & Webster, 2003; Sorensen, 2000; Steiner, 1999; ThompsonFawcett & Bond, 2003; Toth & Hizsnyik, 1998; Van Mansvelt & Van der Lubbe, 1999; Webster and Wu, 1999; Williams, 1999), but there is still a gap between the theoretical conceptions of landscape ecology, the development of modelling approaches and the actual implementation of integrated metropolitan planning. Such a holistic approach requires a complex systems approach, in order to better understand the processes involved and to ensure that future urban forms are sustainable and environmentally benign. Meanwhile, and probably as a result of the lack of integrated analysis

135

and planning, there is a lack of tools available to conduct dynamic simulations of metropolitan land use change that integrate landscape ecological concepts and principles. During recent years, with the generalisation of GIS applications, there has been a multiplication of studies portraying static maps and projections into a specific year in the future. At the watershed scale, usually linked to a relatively vast area served by a main river and its secondary river system, much has been done in terms of the integration of the environmental and the human component (Alexandridis, Takavakoglou, Crisman & Zalidis, 2007; Born & Sonzogni, 1995; Fabos & Gross, 1997; Garcia, 2000; Khanna, Ram-Babu & George, 1999; King, Annandale & Bailey, 2003; Kraft & Penberthy, 2000; Pettit, 2005; Quon, Martin & Murphy, 2001; Raj, 1995; Rohde, Hostmann, Peter & Ewald, 2006; Roseland, 2000; Steiner, 1999). The regional planning of metropolitan areas frequently includes the study of land cover, land use and land use change. Rarely, however, does it include landscape ecological concerns which address the needs of urban growth, or the needs of interaction of spatial pattern and ecological processes over time. Land use is frequently seen as an important element for human evolution (i.e. market value), but seldom does analysis include functions and processes as contributing to balanced regional ecosystems and to improving the quality of life for society in general. In order to perform landscape ecological studies, several elements need to be considered: the study of components (e.g. number and type of spatial elements and species), the study of patterns (e.g. ecological relationships that help establish and sustain species) and the study of processes (e.g. ecological functions over time). We argue that the three components (components of the landscape, patterns and processes) are interconnected and interrelated and should constitute the main elements of an integrated modelling process to support metropolitan planning. Landscape ecology is based on the premise that there are strong links between ecological patterns and ecological functions and processes (Blaschke, 2006; Bu¨rgi, Hersperger & Schneeberger, 2004; Dramstad, Olson & Forman, 1996; Geneletti, 2008; Gustafson, 1998; Leitao, Miller, Ahern & McGarigal, 2006; Ligmann-Zielinska, Church & Jankowski, 2008; Marull, Pino, Mallarach & Crodobilla, 2007; Risser, 1987; Turner, 1987; Turner & Gardner, 1991). Ecological systems are spatially heterogeneous, exhibiting considerable complexity and variability in time and space. Landscape ecology also represents the study of individuals, from microscopic elements to entire

136

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

landscapes with multiple relations and constraints. Planners are increasingly looking towards landscape ecology for concepts and principles that support sustainability, broadly defined (Forman, 1995; Geneletti, 2008; Hersperger, 1994; Leitao, Miller, Ahern & McGarigal, 2006; Ndubisi, 1997; Swaffield & Primdahl, 2004). Landscape planning therefore becomes a common term to designate activities that integrate both naturalecological needs and human-socioeconomic needs. Landscape planning argues for the sustainable use of physical, biological and cultural resources. It seeks the protection of unique and scarce resources, the avoidance of hazards, the protection of limited resources for controlled use, and aims to accommodate development in appropriate locations (Fabos, 1985). The landscape plan offers specific recommendations regarding land use allocation and designation of levels of protection and management. It often includes setting a strategy to ‘undo’ negative changes in the landscape from the past (Ahern, 1998). In order to understand how the sustainable planning of landscapes should be performed, it is important first to understand what its main elements are and how they interact. Land is a continuous surface, composed of many different elements. The understanding of its main parts is very important to understand how it works. We now explore the main elements of a landscape, its associated dynamics, and how planners incorporate landscape ecological planning into their plans. 2.1. Patches, corridors and pattern-process dynamics Pattern-process dynamics are fundamental to the study of landscape ecology and landscape planning. Landscape patterns become the spatial arrangement of the components and processes, and landscape processes can change components and patterns. Consequently, the study of spatial structure is a major subset of spatial heterogeneity, usually referring to the spatial configuration of the landscape system’s components. Gustafson (1998, p. 145) lists three methods to represent the heterogeneity of a system: 1. non-spatial (composition, proportions, diversity); 2. spatial (configuration—patch-based indices: size, shape, patch density, connectivity, etc.; or cell-based indexes: contagion and lacunarity); and 3. point data (continuous variables, samples). Patch, corridor and mosaic are the elements of landscape composition and they characterise a land-

scape’s spatial heterogeneity (Forman, 1995; Forman & Godron, 1986). Pattern and process in landscapes are reciprocally interrelated. Pattern influences functions and processes. Processes and functions influence pattern. The landscape model of patch, corridor and mosaic provides a useful and widely accepted spatial language for landscape ecology. Landscape ecology is concerned with the configuration (spatial placement and shapes of spatial elements) and connectivity that emerges from the interaction of composition and configuration (Forman & Godron, 1986; Leitao, Miller, Ahern & McGarigal, 2006; Sorrell, 1997). The relative shape and location of a patch, with respect to its landscape context, together describes landscape configuration, for example of a fragmented landscape. Landscape configuration has been identified as a critical factor in the maintenance of species diversity and abundance where the natural vegetation cover has been disturbed (O’Neill, Hunsaker, Jones, Riitters, Wickham & Schwartz, 1997). In response to some of the questions of landscape ecologists and planners when confronted with patch size, patch number, and patch location, Dramstad, Olson & Forman (1996) listed three fundamental questions and subjects to take into account: (1) patch size (large or small?); (2) patch number (how many?); and (3) patch location (where?). Transferring landscape ecology research into planning is a fundamental step in order to start managing land as a continuous surface, with processes impacting multiple species and changing the land patterns that are the habitat of many species, including mankind. Two of the most well known approaches in landscape ecology are the ‘patch-corridor-matrix model’, described above, and the ‘greenway’ approach. Both incorporate the main principles and methodologies of land ecology in order to achieve sustainable landscape planning. 2.2. Including patches and corridor dynamics in a holistic planning approach Several approaches for integrating landscape ecology principles into landscape planning have been proposed (Dramstad, Olson & Forman, 1996; Hersperger, 1994; Leitao, Miller, Ahern & McGarigal, 2006; Steinitz, 1990). This research examines two of the most commonly accepted and used approaches: the patchcorridor-matrix model and the greenway approach to landscape planning (Forman, 1995; Ahern, 1998). The patch-corridor-matrix model provides a system for describing landscape structure. Landscape ecological processes are integrally and reciprocally related to

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

spatial patterns. Forman has proposed a suite of indispensable patterns that are essential for ecological functions in any landscape: large patches of natural vegetation, riparian corridors, connectivity for movement of key species among large patches, and small patches that provide heterogeneous ‘bits’ of nature throughout developed areas (Forman, 1997, 1995). The greenways concept is another approach to sustainable landscape study. Proponents argue that it is very successful for integrating ecological and human components in highly urbanised areas (Ahern, 1997, 1996; Fabos & Ahern, 1995; Flink & Schwarz, 1993; Machado, Saraiva, Silva, Rocha, Ferreira & Morgado, 1997). Ahern (1996) describes the greenway approach as ‘networks of land containing linear elements that are planned, designed and managed for multiple purposes including ecological, recreational, cultural, aesthetic, or other purposes compatible with the concept of sustainable land use.’ Five key ideas contained in the above definition warrant further discussion: 1. The spatial configuration of greenways is primarily linear. 2. Linkage is a key greenway characteristic that defines the greenway and relates it to the larger landscape context, often at multiple scale levels. 3. Greenways are multifunctional, based on an assumed or negotiated spatial and functional compatibility of certain uses. 4. Greenways strategy is consistent with the concept of sustainable development, in that it is based on assumed complementarities between nature protection and economic development. 5. Greenways represent a distinct spatial strategy, based on the particular characteristics and advantages of integrated linear systems. The simplicity of this concept, the originality of including the human factor (e.g. recreation), and the inclusion of grassroots initiatives makes it a known/ applied concept (Ahern, 1996; Fabos & Ahern, 1995; Flink & Schwarz, 1993; Little, 1990; Smith & Hellmund, 1993). There are a number of arguments that support greenways. It supports the flow of nutrients, species and energy, as well as the protection of interior habitat from disturbances. It represents a contra-tendency opposed to fragmentation, as a landscape synergy that links the constituent parts of the system (e.g. forest, recreation). It also has the potential to provide a visible structure and legibility to the landscape (Ahern, 1996). Consequently, greenway planning and patternprocess theories and concepts were adopted as the

137

basis for ‘decision rules’ articulated during the code development of the model discussed in this paper. The goal was to protect, defend and restore a metropolitan spatial pattern for each metropolitan area being studied (as well as for other case studies each time the developed models are used), that would provide the ecological, economic and social functions associated with the patch-corridor-matrix and greenways planning models. The main goal of this paper is therefore to accept one planning approach, which clearly includes the main ecological elements that allow for a sustainable landscape planning (assuming that by including those elements in a clear structure we will be able to manage in a more environmentally sustainable manner). Having clarified the main land ecology elements (pattern-patchcorridors), it is possible to access a set of metrics that allows us to describe those same elements. We can thus prepare a set of planning analysis and strategies emphasising the establishment of connectivity among patterns, thereby allowing us to organise land uses according to a more sustainable pattern. By doing so we are also assuming that those sustainable patterns will generate more sustainable processes (and vice versa) in a perpetual cycle, that will be mutually beneficial for processes and patterns and for the species living there and their ecosystem. 3. Range of model approaches available The issues of ecological patterns and processes in the planning of sustainable regions are infrequently seen in the modelling process. Urban and ecological models tend to be developed separately, with their own structures, language and problems. Several studies have been carried out using geographical information systems (GIS). Nevertheless, few have attempted to include landscape strategies focussed on landscape connectivity, or having environmental components interacting dynamically with urban pressure(s), or time evolution. Several reasons can be pinpointed for this absence of integrated studies. GIS approaches already allow the inclusion of some urban and environmental planning strategies. If, however, the objective is to integrate these strategies in an interactive and dynamic way, that not only includes concerns about the pattern-process dynamic, but also includes the possibility of locally varying the different strategies, without using a ‘one size fits all’ philosophy, GIS approaches can be problematic. Such integrated analyses require dynamic models, which address several possibilities and include the complexity of the models under study.

138

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

The inclusion of these complex elements in the modelling process requires a shift from simple linear causal analysis to more complex, discrete event analysis and simulations, including stochastic analysis, to deal with the inherent uncertainty and variability in the integrated model process (i.e. a progressive change from deterministic models to stochastic models) (Batty, 1996; Batty, 2005). More robust and complex computer-based modelling is a rapidly growing field of landscape ecology and planning (Ball, 2002; Brail & Klosterman, 2001; Clarke, Parks & Crane, 2002; Gardner & Engelhardt, 2008; Klosterman, 1997; Silva, 2002, in press; Wileden, Silva & Ahern, 2003; Wyatt, 1999; Zeiler, 1999). The study of populations and communities has been one of the most explored themes in ecological studies (Giles & Trani, 1999; Gustafson, 1998; Hargis, Bissonette & David, 1998; Hargrove, Hoffman & Efroymson, 2004; Lenz, Malkina-Phkh & Pykh, 2000; MalkinaPykh, 2000; Romero-Calcerrada & Luque, 2006; Silva and Ahern, 1999; Silva & Clarke, 2002). Many of the existing biodiversity indices have been integrated into computer-based ecological models. Nevertheless, once again these are mainly linear formulations, which either do not include a spatial component, or do so in a static environment, using global models, few of which reflect local characteristics (Fre´on, Drapeau, David, Moreno, Leslie & Oosthuizen, 2005; Rohde, 2005; Rohde, Hostmann, Peter & Ewald, 2006; Romero-Calcerradaa & Luqueb, 2006; Wintle, Elith & Potts, 2005). To analyse pattern and process throughout a metropolitan landscape requires models that are sensitive and responsive to heterogeneous local conditions and variability. Cellular automata (CA) are helpful in doing this because they are cell-based in their analysis and results, sensitive and adaptable to local conditions. These characteristics enable CA to address pattern variations, and processes can be included in the rules and relationships among spatial elements. 3.1. Application of cellular automata models to land use planning The background of cellular automata (CA) includes von Neumann and Morgenstern’s ‘theory of games and economic behaviour’ (1944), which shifts the emphasis of scientific analysis from determinant to structure. Also noteworthy are Standislav Ulam’s (Ulam, 1961; Burks, 1971) propositions that CA could be given sets of local rules that generated mathematical patterns in twoand three-dimensional space, allowing the extraction of mathematical formulations from such behavioural patterns. Conways (Gardner, 1970) places von Neumann,

Morgenstern and Ulam’s development in the mainstream research by presenting the ‘game of life’, linking these game theories and the organisation of patterns in a spatial context, by having a matrix and a set of ‘agents’ behaving according to a set of rules that produced emergent behaviour. Finally, Waldo Tobler (1979) definitively links it to land-related scientific subjects by publishing Cellular geography. During the 1980s and particularly during the 1990s, several prominent authors (Batty, 1999; Clarke, 1998; Openshaw & Openshaw, 1997; Wu & Webster, 2000) built a strong body of knowledge on the subject and set the stage for multiple developments in the sciences researching in land-related fields. Cellular automata represent the phenomenon under study using a grid space of cells, cell-states, neighbourhood effects and transition rules. Cells have a very important function in CA: they are the smallest units, and their states change in line with transition rules functioning in accordance with neighbourhood characteristics (its closest four or eight cells), self-modification rules, and other defined elements of the model. While the neighbourhood effect is a basic concept to the CA, the transition rules need to be clearly stated and they are pivotal for a realistic simulation of the phenomenon under study. The rules must apply to every cell, state and neighbourhood, and every change in each state must be local. One of the biggest advantages claimed for CA models is the fact that they have the capacity to include time, generating a dynamic analysis of the phenomenon being studied. In order to perform such dynamic actions, CA requires an initial set of states over the cells, a set of transition rules, and a sequence of discrete time steps. The data-set required for the CA to work must include a starting grid space (i.e. land uses in a specific area), and once the rules are applied to that grid a synchronous update of all the cells represents the first iteration that corresponds to the first time step. For each interaction the entire grid is examined, the transition rules are applied and a new time step/iteration is activated. The final result is a simulation through time of the phenomenon under study, and a better understanding of the phenomenon itself and its evolution. In applying CA to the study of landscapes, the basic cells usually correspond to land uses, and the transition rules correspond to the processes that are associated with land change. When running CA it is possible to understand the spatial organisation of these different cells, whose attributes are, for instance, the type of land use. When the model is run through time, the interactions will also help to highlight the implications of landscape processes in the development and/or change of existing patterns.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

3.2. Trends in integrating CA simulations into landscape ecology The bibliography of cellular automata applied to landscape ecological studies has followed an interesting evolution. The first applications began in the 1980s, closely following new discoveries in physics (Prigogine, George, Henin & Rosenfeld, 1973; Prigogine, Lefever, Goldbeter & Herschkowitz-Kaufman, 1969). This close proximity is important today, as recent studies and applications of CA introduce are still closely followed by researchers of other fields (Holland, 1995; Holland, 1999; Toffoli, 1998). The research carried out with ecological models and cellular automata (CA) includes several studies, such as those developed by Arii and Parrott (2006); Green and Sadedin (2005); Rohde (2005); Bolliger (2005); Molofsky and Bever (2004); Luck and Wu (2002); Kok, Engelen, White and Wind (2001); van der Veen and Rotmans (2001); Ligtenberg, Bregt and van Lammeren (2001); Alonso and Sole´ (2000); Sirakoulis, Karafyllidis and Thanailakis (2000); Kay, Regier, Boyle and Francis (1999); Lorek and Sonnenschein (1999); Jai, Fournier, Cabrera and Maurissen (1999); Grimm, Wyszomirsk, Aikman and Uchmanski (1999); Balzter, Braun and Kohler (1998); Congleton, Pearce and Beal (1997); Adamatzky (1994); Silvertown, Holtier, Johnson and Dale (1992). Some of these studies are very detailed (almost subject specific), but, nevertheless, a holistic analysis of the environment and of the environment–urban interaction is still absent. In the above-referenced papers, we find several approaches to implementing CA applications. Some papers focus on the traditional C and C++ approaches. Writing the code for software/computer models with C and C++ languages has been the most commonly used approach, particularly if the goal is to develop operational models. This computer language gives high flexibility to the programmer, and allows the inclusion of robust coding that will run programs in a more efficient way when there are vast amounts of data and/or a high requirement of computational analysis. With time, modellers started to develop loosely coupled applications, together with GIS, or developed specific simulators for ecological CA models using stand-alone applications in JAVA or VB (more user-friendly computer languages). While the subjects vary, advancements in CA development are interesting and somewhat equivalent to what is happening in urban regional applications. Among others, it is important to mention the studies developed by Balzter, Braun and Kohler (1998), that model population dynamics of three different plant

139

species on a lawn; the studies developed by Alonso and Sole´ (2000), that simulate rainforest dynamics (forest growth, species diversity and canopy gap formation and expansion); and the studies by Sirakoulis, Karafyllidis and Thanailakis (2000), that develop a CA for the effects of population movement and vaccination on epidemic propagation. This latter model not only includes the traditional epidemic focus, but also the effects of population movements and multiple foci of infection, as well as the effects of vaccination in a particular geographic area. It is also important to mention the studies of Lorek and Sonnenschein (1999), that explore the potential of simulators in general and then develop an original CA simulator, suggesting an application to metapopulation dynamics. More recently, we find a new set of models, that start to include spatial components and biodiversity indicators interacting in dynamic analysis: Arii and Parrott (2006) present a CA model which simulated the dynamics of invasive species and the general colonisation (through invasion—virus-like spread processes) process of exotic species that have various ‘competitive abilities’ against the native species. They used a twospecies CA model in order to demonstrate that: (1) a threshold level of competitive ability is required for the exotic species to successfully establish in a new landscape; and (2) an exotic species with superior competitive ability does not necessarily become dominant in a landscape (alternatively, a species that has inferior competitive ability may successfully colonise a new system) (Arii & Parrott, 2006, 219). The studies of Laird and Schamp (2006) used a spatially explicit CA model to investigate the potential for varied levels of competitive intransitivity (called by the authors ‘non-hierarchical competition’) to promote species coexistence through local competition. It is important to point out also the study of Wood, Ackland and Lenton (2006), that speculates (and presents some data) on oscillations on zero-dimensional systems (Hopf bifurcations). The authors developed a two-dimensional CA, Daisyworld, to include mutation of optimal growth temperature as well as mutation of albedo. Thus, the organisms (daisies) can adapt to prevailing environmental conditions or evolve to alter their environment. They found out that these systems tend to oscillate according to a specific pattern, which seems to reduce its intensity with time, and therefore tends to increase the probability of a ‘transient’ bifurcations phase (which becomes adaptable to the new conditions in space and time). Finally, it is also important to mention the work developed by Messina and Walsh (2005), that uses

140

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

multi-thematic and spatially explicit data. This data is combined from a longitudinal socioeconomic and demographic survey, using a CA model representing land use and land cover change, linked to spatially referenced biophysical and socioeconomic coverage used as input data, and then combined with ‘rules’ derived from empirical analyses. 3.3. State of the art in urban growth simulation of landscape ecology The evolution of computer models in urban and environmental dynamic models is fairly recent, as mentioned above. While there are many applications with great potential (Batty, 1999; Batty, Xie & Zhanli, 1999; Clarke, Hoppen & Gaydos, 1997; Kocabas & Dragicevic, 2007; Lau & Kam, 2005; Stevens & Dragic´evic´, 2007; White, Engelen & Uijee, 1997; Yang & Lo, 2003), the models discussed in the literature either tend to run few data sets; or are only experimental in approach, making use of simplified data in order to test the performance of models; or are very dependent on the landscape for which they were developed (modes that are not transferable to other case studies). In terms of full operational models, only two approaches have been widespread in use, due to their flexible implementation. SLEUTH (developed during the 1990s in the USA by Professor Keith Clarke and already in its fifth version), and the EU Murbandy model (developed by Carlo Lavalle, Luca Demicheli, Maddalena Turchini, Pilar Casals-Carrasco and Monika Niederhuber). The Murbandy/Moland model (Monitoring Urban Dynamics) was launched as a research project at the Space Applications Institute (SAI) of the Joint Research Centre (JRC) of the European Commission in Ispra (Italy). This project’s main goal is to document changes in land usage in over 20 European and neighbouring metropolitan areas via remote transmitted data (i.e. satellite images) and to derive indicators from these. Murbandy’s computer model simulates the future development of land usage in urban areas, according to three different modules: 1. CHANGE (to monitor cover changes in urban and peri-urban areas); 2. UNDERSTAND (to compute static and dynamic urban and environmental indicators, in order to help understand urban and peri-urban landscapes and estimate their level of sustainability); and 3. FORECAST (to develop scenarios for sustainable urban and regional development, using a combination of earth observation and non-space data)

(Barredo & Demicheli, 2003; Fricke & Wolff, 2002; Hagen-Zanker, 2006). While this approach has multiple applications in Europe, it is not widely used in the rest of the world, and tends to be a land use change model (ecological and biodiversity dynamics are absent). The second approach, as mentioned above, relates to the work developed by Keith Clarke at the University of Santa Barbara, together with the US Geological Services (Clarke, Gazulis, Dietzel & Goldstein, 2007). SLEUTH was the first operational model in use (Clarke, Hoppen & Gaydos, 1997) and, probably because of this, there are many more case studies applied throughout the world (Gazulis & Clarke, 2006). This is a robust model, which addresses urban land change though time. It also has a land use change model that can be coupled if required (Deltatron). Nevertheless, neither of these two models (SLEUTH and Murbandy/Moland) addresses biodiversity issues directly. Both SLEUTH and Murbandy/Moland models include land use metrics and transportation metrics. They also include some landscape change dynamics, but there are no landscape ecology metrics that access the state of the landscape and its functioning, nor are any landscape ecology planning strategies incorporated. It is in this context that the work developed by Silva and Clarke (2002 and 2005), Silva, Ahern and Wileden (2003) and Wileden, Silva and Ahern (2003) is the first holistic attempt to link multiple disciplinary fields in an operational computer application. The adaptability of CA, the calibration phase to case study (a phase required to adjust the model to the landscape under study), and the possibility of including planning policy in the simulation of future scenarios all make it a very appealing approach, which answers a real need. The non-existence of operational dynamic models that include land ecology metrics and landscape ecology planning policies is at the basis of the development of the environmental model detailed in this paper. It aims to respond to this gap in model development (providing more holistic planning approaches, that include both urban and environmental considerations in a dynamics modelling approach). 3.4. Coupling ecological principles to urban growth models If landscape planning would be the platform structure on which human and the environment, interact, there is a clear need for a common spatial language and common concepts. Land ecology should

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

play a major role, providing major indicators, most appropriate metrics, and goals to be reached. The literature review in this paper presents a prolific multitude of authors developing and using landscape ecology concepts. In some instances, all that is needed is to integrate all of this research into a coherent work, which obviously was not the aim of this paper. This paper has only attempted to include landscape ecological concepts and principles that could help the authors to perform integrated planning for two metropolitan areas. If a coherent body of literature is to be in place, in order to structure holistic planning approaches that include ecological planning approaches, a clear understanding and classification of the spatial configuration of the landscape system’s components is fundamental (the study of spatial heterogeneity). Understanding spatial structure is one of the key actions in gaining an understanding of patterns and processes and in correctly planning for their functioning. Because both human and natural actions are the result and are resulting from spatial structure, this could easily be the supporting ‘canvas’ of this integrated planning. One key problem at this moment is to develop metrics that can answer to both needs (equivalent metrics for urban and ecological landscape planning). There is a multitude of metrics for urban and for environmental planning, but they tend to be used separately, and there are few indicators that handle both subjects together. SLEUTH, coupled with an environmental ecology model, could be used in order to answer these needs of integrated planning, in particular the two approaches taken by the greenways approach (Ahern, 1997, 1998; Fabos & Ahern, 1995; Fabos & Gross, 1997; 1985) and the patch-corridor-matrix model (Forman, 1995, 1997). Nevertheless, while there is plenty of literature on the subject, these two approaches tend to be separated. The patch-corridor-matrix model is used as a subset of studies of habitat fragmentation and metapopulation studies. And the greenways approach tends to emphasise the design element and not the processes and patterns it might promote. This separation is particularly unfortunate, as several authors stress, as the greenways concept should be an important approach to sustainable landscape study, since it is very successful for integrating ecological and human components in highly urbanised areas. While it does a lot in terms of creating interesting landscape areas for social interaction in a natural environment, the greenways approach is far from being explored to its maximum potential. If the issues of ecological patterns and processes are infrequently seen in the modelling process of local or very

141

specific areas/problems, they also tend to be rare when planning for sustainable regions. Once again, both urban and ecological models tend to be developed separately, with their own structures, language and problems. Finally, if urban and environmental integrated approaches tend to be rare in the planning literature, they become even unusual when geographical information systems (GIS) are used as supporting tools. The fact that data collection tends to be different for environmental and ecological subjects makes it even harder to integrate these elements into a GIS environment. The development of a common methodology faces other barriers, since the technicians developing these two analyses tend to have different backgrounds and develop their own separate studies. Another element to add to the gap between planners and ecologists has to do with the fact that they tend to discuss the reasons for developing particular kinds of applications and for which purpose. It is also clear that many of the projects are developed by private or public sector institutions that have a clear sector analysis purpose, which is sometimes incompatible with a more holistic view (and time, money, contract agreements, etc., tend to hinder researchers’ or practitioners’ own views of what could be done to expand or introduce some innovation into these studies). Therefore, we conclude that this framework method for landscape ecological planning can be one of the elements to bring together both urban and environmental planning in a holistic kind of approach. We focus on a set of possible, non-mutually exclusive planning strategies: 1. Protective strategy (when the existing landscape supports the abiotic, biotic and cultural (‘abc’) resource goals); 2. Defensive strategy (when the existing landscape is already in a spatial configuration that is negatively impacting ‘abc’ resources); 3. Offensive strategy (adopting a proactive action when the landscape is already deficient with respect to supporting ‘abc’ resources); 4. Opportunistic; and 5. ‘Let it grow’ strategy. The strategies require the application of the patchmatrix-corridor concept, in order to clearly assess the state of the landscape and propose a methodology that is clearly perceived by international researchers and practitioners, and that can be easily adopted when ecologically planning for landscapes. Also, the patchmatrix-corridor concept allows for the application of

142

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

appropriate metrics in order to be implemented in a computational program. This allows the inclusion of policy and urban dynamic processes in an integrated process that enables us to improve the quality of life of the populations living in these landscapes, by improving the ecological patterns and processes. 4. Development and interoperability of the SLEUTH and CVCA cellular automata models SLEUTH is an urban CA simulator, and is the acronym for Slope, Land use, Excluded areas of Urbanisation, Transportation and Hillshade/topographic-rendering (the input layers that will make the model run). Developed by Clarke (Clarke, Hoppen & Gaydos, 1997; Clarke, 1998), SLEUTH is composed of the same components that characterise CA models: 1. 2. 3. 4. 5.

Grid or raster space, Cell states, Neighbourhoods, Transition rules, Sequence of discrete time steps.

SLEUTH requires five input images: urbanisation, transportation, excluded areas from urbanisation, slopes, and hillshade. The images are prepared using a commercial GIS and then converted to eight-bit.gif format for each layer. In SLEUTH each cell in a layer receives a value: 0 is a null value, while all the values represent the range between one and 255. Five indicators that synthesise the behaviour of the system were considered: roads, slope diffusion, breed, spread, slope resistance and road gravity. These allow the control of urban growth through, respectively, the effect of roads, slopes, generation of new urban nuclei in areas favourable to growth, spread of urban growth from existent nuclei, and the effect of dispersion and impact of different road hierarchies. Through analysis of the files SLEUTH mimics several types/forms of urban growth, including: organic growth, road-influenced growth, and diffusive growth. Cell evolution of urbanisation is recorded and supports a set of self-modification rules in order to control the parameters, allowing the model to modify itself and therefore making the resulting simulations closer to what is observed in the real world (Silva & Clarke, 2002, 2005). For example, the detection of intense growth periods (boom) or periods of little or no growth (bust), changes the control parameters. Therefore, when the absolute amount of growth in any year exceeds a critical value, the diffusion, spread

and breed factors are increased by a multiplier greater than one. This encourages diffusive, organic, and roadinfluenced growth, reproducing the tendency of an expanding system to grow even faster. In opposition to the previous case, when the system growth rates fall below another critical value, the diffusion, spread and breed factors are decreased by a multiplier less than one, similar to what would happen in depressed or saturated areas. Two other self-modification rules can play an important role in normal (linear) growth. As the road network increases in terms of density of streets and enlarges in terms of the number of lanes, the road gravity factor is increased. As the percentage of land available for development decreases, the slope resistance factor is decreased, allowing urban expansion to steeper slopes. With time, the spread factor is also increased, which accelerates urban expansion on flat land. The results from running SLEUTH include yearby-year images of urban growth simulated for a study area (i.e. metropolitan region). These images contain: the existing urban cells, and cells with different probabilities of growth (from high to low probability of growth). These cells are placed in the background (hillshade file). SLEUTH will work, therefore, as the backdrop of urban dynamics, where a set of environmental dynamics needs to countervail. Through time, this game of interaction of urban–environment will produce a different image of the metropolitan area, where the needs of urban growth are satisfied, but where ecological needs are also maintained, increased or optimised. CVCA (countervailing cellular automaton) is the second model that used a set of countervailing strategies to direct SLEUTH urban growth cells to ‘good-to-grow’ areas. The CVCA algorithm is based on the theoretical research developed by Ahern (1998), and Forman’s pattern-patch-corridor model. CVCA proposes an alternative future scenario analysis, based on a set of landscape planning strategies defined by Ahern (1998): offensive, opportunistic, defensive, protective (and includes a further strategy called ‘let it grow’). These strategies are proposed according to the behaviour of ecology metrics on the pattern-patch-corridor performance of the landscape. The next section explains the landscape ecological strategies and their interaction with the urban CA model. 4.1. Model structure and requirements for SLEUTH and CVCA Fig. 1 presents a block diagram of the countervailing cellular automaton model (CVCA) and its loose

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

143

Fig. 1. CVCA block-diagram.

coupling with the SLEUTH model. This means that CVCA builds on SLEUTH and develops a set of countervailing strategies to the urban growth allocated by SLEUTH. This dynamic interaction is useful, since it allows urban growth generated by SLEUTH to be driven to other areas where the strategies are not applied. CVCA begins by assessing the initial state of the landscape, generating several landscape metrics based on the initial state, and using these metrics to select and implement the appropriate landscape strategies. CVCA then interacts yearly with SLEUTH in order to have the landscape strategies enforced, thus guiding SLEUTH forward to ‘good-to-grow’ areas, and buffering important ecological areas identified by CVCA from projected urban development. This is the main reason why the CVCA model is named ‘countervailing cellular automaton’. It uses a set of landscape ecological strategies to counteract (in a sustainable way) urban growth to good-to-grow areas. At the same time, it allows several planning strategies to be indicated at specific geographic locations as a function of the degree of urban pressure. These offensive, defensive, opportunistic or protective planning strategies can afterwards be the basis for specific

planning actions, such as: purchasing land in order to connect different patches of natural importance, or increasing surveillance on specific areas that are under intense urban growth pressures. The CVCA model requires the same input layers as SLEUTH (Slope, Hillshade, Transportation, Urban, Excluded). The excluded layer had to be changed significantly, however, in order to distinguish a different class that represents all the areas outside the boundary of the metropolitan areas. These areas are excluded from urbanisation, but for CVCA purposes they cannot be considered the same type of exclusion, since they do not figure in the application of the ecological strategies. The output files are the same, although the year-byyear output images are obviously different. SLEUTH outputs different classes against a hillshade background: existent urban, simulated urban with high probability of change, or simulated urban with lower probabilities of change (this class has several subclasses of probability, from very low probability to averagehigh probability). The CVCA outputs four more classes that match five different landscape planning strategies (protective, defensive, offensive, opportunistic, let it

144

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

grow). As discussed in the introduction, it is important to develop this kind of model, where urban and environmental dynamics can be integrated, allowing for simulations where both urban and environmental needs are considered and allocated in a sustainable future. The CVCA algorithm is based on the theoretical research developed by Ahern (1998), that proposes an alternative future scenario analysis, based in a set of essential attributes of a landscape ecological planning model. Ahern defines the landscape ecological planning model as an interdisciplinary approach that addresses landscape pattern-process at multiple scales, and

includes a ‘human ecological component’ (Ahern, 1998, 4). Ahern’s framework method (1998) for landscape ecological planning focuses on a set of possible, nonmutually exclusive planning strategies (Fig. 2): 1. Protective strategy (when the existing landscape supports the abiotic, biotic and cultural (‘abc’) resource goals); 2. Defensive strategy (when the existing landscape is already in a spatial configuration that is negatively impacting ‘abc’ resources);

Fig. 2. Landscape strategies (Ahern, 1996, 140).

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

3. Offensive strategy (adopting a proactive action when the landscape is already deficient with respect to supporting ‘abc’ resources); and 4. Opportunistic strategy (since landscapes frequently contain unique elements or configurations of elements that represent positive opportunities). Some adjustments needed to be made, in order to translate Ahern’s landscape strategies into landscape metrics and afterwards to develop an appropriate code that the CVCA model could run. The selection of strategies was based on previous work developed by McGarigal and Marks (1995). The next section explores the selection and inclusion of the landscape metrics into the model. 4.2. Selection of the landscape metrics Besides SLEUTH, Fragstats (McGarigal & Marks, 1995) was used as a reference system, mainly in terms of the data structure used in order to identify the proximity of different patches and the patch itself. While Fragstats was not used as the main software, there is Fragstats code in the program (the code calls and runs the landscape metrics against it). This code integration is very important, not only to optimise the reading of this landscape metric, but also for further development in case other metrics are to be included in further developments of CVCA. Fragstats was developed by Professors McGarigal and Marks, at University of Massachusetts, Amherst (UMASS), Department of Natural Resources and Conservation. This software has several characteristics that make it very important, both as a learning experience to understand landscape classification of patterns, and also in understanding how the results might be robust enough to proceed using some of the metrics. This software was developed during the 1990s, but since its development several of the applications have been improved. It has been used by other researchers, and the popularity it has achieved among the ecological and landscape ecology community has granted some reliability to this application (Grilli & Bruno, 2007; Kong & Nakagoshi, 2006; Kumar, Stohlgren & Chong, 2006; Maclean, Hassall, Boar & Lake, 2006; Matsushita, Xuf & Fukushima, 2006; Palmer, 2004; Uuemaa, Roosaare & Mander, 2005; Wei & Hoganson, 2005). Therefore, we included some of the code of Fragstats, in particular the use of the Fragstat’s metrics, adapting these strategies to a CA environment, in order to describe the initial conditions of the

145

landscape. This was included in the CVCA, as part of the code. These actions are not sequential, and since the initial development of the code several adaptations have been done to CVCA; nevertheless, it was important for the team to learn with the experience of Fragstats, in order to build CVCA. As stated, the use of Fragstats was marginal (more as a learning experience and as a criterion to contribute to the selection of the best landscape metrics to be used in CVCA). 4.2.1. The metrics of CVCA The metrics used were chosen by the spatial indicators that have been mentioned in different landscape ecological studies (Gustafson, 1998; Kong & Nakagoshi, 2006; Kumar, Stohlgren & Chong, 2006; Leitao & Ahern, 2002; McGarigal & Marks, 1995; Turner & Gardner, 1991; Uuemaa et al., 2005) and explored in multiple practical applications (i.e. Fragstats). Seven metrics were selected: edges (number of edges per patch), area (area per patch), num clusters (number of clusters), MCS (mean cluster size), MPS (mean patch size), LSI (landscape shape index), MNND (mean nearest neighbour distance), while the edges, area and number of clusters represent a statistical calculation (still presenting some problems at the moment of defining the ‘object’ patches in raster environment). Three metrics required advanced computation: landscape shape index (LSI), the mean patch size (MPS), and the mean nearest neighbour distance (MNND). Their description is as follows. 4.2.2. Landscape shape index

0:25E0 LSI ¼ pffiffiffi A Units: none. Range: LSI  1, without limit. LSI = 1 when the landscape consists of a single circular (vector) or square (raster) patch; LSI increases without limit, as landscape shape becomes more irregular, or as the length of edge within the landscape increases, or both. Description: LSI equals the sum of the landscape boundary (regardless of whether it represents true edge) and all the edge segments (m) within the landscape boundary (including those bordering background), divided by the square root of the total landscape area (m2), adjusted by a constant for a square standard (raster).

146

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

LSI = landscape shape index; E = sum of landscape boundary and edge segments; A = total landscape area in a cell. 4.2.3. Mean patch size MPS ¼

  A 1 N 10; 000

Range: MPS > 0, without limit. The range in MPS is limited by the grain and extent of the image and the minimum patch size in the same manner as patch area (AREA). MPS equals the total landscape area (m2), divided by the total number of patches. MPS = mean patch size; A = total landscape area (in cells); N = total number of patches. 4.2.4. Mean nearest neighbour distance Pn0 MNND ¼

j¼1 hi j

n0 j

Range: MNND > 0, without limit. MNND equals the sum of the distance (m) to the nearest neighbourhood patch of the same type, based on nearest edge-to-edge distance, for each patch of the landscape with a neighbour, divided by the number of patches with a neighbour. MNND = mean nearest neighbour; h = distance; i = patch; n = number of patches; j = neighbour. As already stated, choosing the specific metrics was an important process, and resulted from the review of the literature and of several software applications. For the working purpose of the model, the most important metric is the mean nearest neighbour distance (MNND), since a key planning objective is to establish greenways and connectivity between excluded areas of urbanisation. Because this research emphasises the connectivity of different elements of the landscape, it is important to have a metric that would indicate the state of the landscape (LSI) and another metric that would indicate the characteristics of the patches (MPS). Extensive testing and debugging was done before the model could run. While this is not the place to discuss the impacts of the different metrics in the landscape, the scale of analysis, combined with the different strategies, produced interesting results. For example, several of the strategies were initially defined based on a comparison of nearest neighbour (NN) to one-half of the MNND value. Because the landscape is very fragmented (has a

very high LSI), and the cell size is 100 m by 100 m, the actual MNND values were less than two. As a result, NN could never be less than one-half of MNND, and so the definition was changed to use MNND instead of one-half of MNND, indicating that the selection of a metric and the scale of analysis can indeed greatly impact the results. These subjects of the selection of metrics and the scale of analysis will be explored further in the discussion part of this paper, pointing to the fact that not only is calibration of the model important, but also that ‘calibration’ of the metrics is a pre-condition to the success of any analysis (in the sense that, in order to establish the best parameter values, it is important to understand the diversity of values and understand maximum and minimum values occurring in space). The selection of the minimum unit of analysis can constrain the maximum scale of detail to be analysed; in addition, the representativity of objects tends to be twice the minimum size of the unit of analysis. These ‘laws’, defined in cartography and remote sensing, tend to be neglected when developing these applications and the consequences impact the model/ algorithm’s behaviour and the outputting results and analysis. Therefore, while it was possible to develop equivalent actions for all strategies, some adjustments had to be made. For instance, the definition of corridor and buffer width was equal to the dimension of one cell (100 m); the definition of minimum distance to establish a corridor was defined as being equal to the distance of the mean nearest neighbour distance metric (MNND). Finally, besides the definition of the four strategies defined by Ahern, it was important to include a fifth strategy, ‘let it grow’, in order to have SLEUTH urban cells allowing the dynamics of urban growth. 4.3. Structure and functioning of the countervailing cellular automata (CVCA) model The CVCA model is an augmented cellular automaton, whose main objective is to apply a set of spatial strategies (offensive, defensive, opportunistic, protective, let it grow). These strategies will vary as a function of the environments’ local characteristics. Following CA concepts, CVCA is defined by a grid space, by a neighbourhood effect, and by a set of transition rules, and a time step iteration. On top of that we impose a set of constraints on the overall behaviour of the cells, thereby modifying the model to allow some action-at-a-distance and not only the cell-by-cell local

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

147

Table 1 Transition rules at CVCA. Transition rules:

Number of cells (cells with a probability of change to urban)

1. Protective:

0 but NN > MNND

2. 3. 4. 5.

Action step

then add protective cell around all outer patch and add protective cells until arriving at closest neighbour then add defensive cell to all outer Defensive: 50%*,** patch cells where transition cell exists Offensive: >50% add offensive cell to all outer patch cells and add offensive cells until nearest neighbour Opportunistic: 0 but NN  MNND (and no transition cell nearby) then link to nearest neighbour Grow: CVCA leaves the cells that are not subject to strategies. These cells feed SLEUTH, again are the base for the next simulation.

*

If in this 50% of cells more than half are high probability of change then add defensive cells to all other patches (Fig. 5). If in this 50% of cells more than half are high probability of change, and NN is half of MNND then add defensive cell to all other patches and connect to NN (Fig. 6). **

behaviours. The next sections describe the characteristics of each of the component elements of CVCA.

4.3.3. Transition rules There are five possible transition rules (Table 1):

4.3.1. Grid space Two base maps are needed: an urban layer resulting from SLEUTH, and the excluded layer that also feeds SLEUTH (includes the agricultural reserve (RAN) and ecological reserve (REN)). The urban layer is one output year chosen by the user that results from the run of SLEUTH (the first year of the simulation). This urban layer includes existing urban areas, and the different probabilities of urbanisation for that specific year. The excluded layer includes the agricultural and ecological reserves. From the excluded layer a set of landscape metrics is computed: the landscape shape index (LSI), the mean patch size (MPS), and mean nearest neighbour distance (MNND) (McGarigal & Marks, 1995). These metrics are a first assessment of the landscape’s spatial characteristics; they also play an important function in locally defining each strategy. They also might be changed to different numeric values that describe the user landscape preferences.

1. 2. 3. 4. 5.

4.3.2. Neighbourhood effect CVCA works with an eight-cell neighbourhood effect. It uses the excluded areas defined in the excluded layer as the active cells (this is due to the fact that these areas include, among other things, the parks and river buffers contained in the metropolitan ecological and agricultural reserves REN and RAN). From the SLEUTH urban layer CVCA uses the cells that present some probability of change. Each strategy will compete for an urban cell that has some probability of change.

Protective; Defensive; Offensive; Opportunistic; and Let it grow.

The transition rules take into account the neighbourhood effect (how many ‘urban probability’ cells) and proximity to a ‘green cell’ (value given by the MNND). Table 1 synthesises the possible relationship between the neighbourhood effect and the MNND, in order to define which of the five strategies will be applied and which transition rules will be predominant, with the purpose of defining the required planning strategy. Table 2 systematises the colour tables associated with each strategy. In order to have the offensive strategy activated, and having the model suggesting high intensity planning strategies to that cell if the landscape status is to be enhanced in its patch size and connectivity, the model

Table 2 Colour table of the strategies. Category Category Category Category Category Category Category Category

Colour/value 1 2 3 4 5 6 7

– – – – – – –

opportunistic (brown) protective (brown+) defensive (purple) more defensive (purple+) most defensive (purple+) offensive (purple –) grow

R255; R166; R251; R200; R150; R128;

G113; B0 G107; B60 G0; B255 G21; B208 G42; B163 G64; B118

148

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

needs to record at least 50% of urban cells ‘attacking’ an existing patch of nature. In that case the model will add an offensive ring of cells to the outer patch and will link, through a corridor of ‘offensive cells’, to the closest nearest neighbour patch (these offensive cells are identified by a different colour at the output image of the model). In order to have the model applying an opportunistic strategy, a new strategy needs to be enabled. If SLEUTH does not proposes urban cells around a specific patch, but the nearest neighbour distance to the next patch is smaller or equal to the mean nearest neighbour distance, in that case CVCA establishes a corridor to the nearest neighbour patch, painting the cells between the Euclidean distance with a different colour that reflects the planning strategy. If the defensive strategy is to be applied, less than 50% of the surrounding cells of a patch need to have urban grown cells being simulated in a specific time step. In this case, if more than half of these new urban cells are high probability of change to urban, in that case CVCA will add an outer protective buffer to all cells. This is a goal that promotes a conservation strategy in order to protect patches of natural interest (ecological and agricultural reserve patches), allowing the proposal of policies that will promote the maintenance of these patches (i.e. through the promotion of specific land uses to the surrounding land parcels). With the protective strategy the goal is to take a step further, with an emphasis on conservation and environmental management of processes and patterns. In this case it is considered that (even if there are no urban growth cells being simulated for the cells nearby a patch) if two patches are so close-by to each other that their nearest neighbour distance is half of the mean nearest neighbour distance, in that case the model builds corridors in between these patches, and an outer buffer around them. Finally, the strategy of growth (strategy 5 of CVCA) diverts growth to other areas that do not impact the existing (or future) patches or corridors, but that still allow growth to happen. 4.3.4. Time steps It is important to stress once more the need to have SLEUTH and CVCA working together in this interoperable environment. CVCA, by countervailing urban growth to areas good-to-grow it, will allow a response to the urban pressures of more urban areas, but will tend to do so in an incremental process. Each time (year) SLEUTH proposes an urban cell, CVCA will evaluate it against the characteristics of the landscape and the need

to maintain sustainable patterns and processes. It will apply four strategies if the cell has some potential to increase landscape ecological potential, or is at risk of reducing that ecological potential. Time plays ain important role in this year-by-year interaction. The CVCA code is closely linked to SLEUTH and runs in coordination with SLEUTH. While the structure of the code is already built, the simulations that were run did not include the different probabilities of urban growth that SLEUTH generates. This means that each strategy was generated just in accordance with the existence of cells that were subject to urban pressure (independently of the intensity of the pressure) and CVCA read it as having a probability of change, but discarded which probability it was. It is important to keep both CVCA and SLEUTH in a modular structure that allows us to enable/disable each application if required, thereby allowing for SLEUTH to run without CVCA and vice versa. The same can be stated for specific actions at the code of each module, if required specific actions of CVCA can be enabled/disabled in the code, for instance, due to lack of data. Table 2 presents the colour table of the different strategies. The browns predominate in the more passive strategies (that indicate a less dynamic landscape, with fewer urban pressures, and with less fragmented landscapes). The purples characterise landscapes with high urban pressures, with fragmented landscapes and very high landscape shape indices. The environmental countervailing code works as specified in Fig. 2, and the pseudocode as specified in Table 3. This pseudocode is very important for those researchers who cannot read coding, and who want to have an idea of the main actions CVCA will perform. One of the key actions of the CVCA model is the computation of the landscape metrics, and the development of the strategies. During the first time step of running the model it receives an excluded layer (it might receive more excluded layers proposed for different moments in time) and will compute the landscape metrics, and demarcate the future evolution of the system, by permanently defining where the excluded areas are. CVCA works in a sequenced time step (in this case a year-by-year basis). It begins with a base-year urban layer (e.g. 2002), looks at the cells with some probability of change to urban, and applies the five landscape strategies, according to the neighbourhood effect, the MNND, and the different probabilities of urban change (i.e. high probability of changing to urban, or only an average probability of change to an urban land use).

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

149

Table 3 Pseudocode of CVCA.

A new map, based on the application of the strategies, will result. This map has information that can be used in two different ways: (1) feed SLEUTH an urban version that only indicates where urban growth has been permitted, but does not explicitly show where the landscape strategies were applied; or (2) display the results of applying the landscape strategies. While we consider that there is much added value if these two action steps are implemented together, the value of having an input map to SLEUTH that can guide new urban growth to areas that do not impact these nature patches of agricultural and ecological reserve—and that at the same time diverts growth from these new outer-patch buffers and corridors suggested by CVCA—will certainly constrain growth from happening in other growth areas. But, if the CVCA strategies are to be used, this will certainly increase the

potentiality of using CVCA close-coupled with SLEUTH. Using the CVCA strategies through time will allow us to dynamically allocate new landscape strategies as a function of new urban pressures, allowing for a constant interaction of CVCA and SLEUTH and a resulting image that will be the interaction of both algorithms through time. In the code of CVCA the mean nearest neighbour distance (MNND) has an important function in both the opportunistic and the protective strategy (Figs. 3 and 4). The precondition for applying the opportunistic strategy does not require an urban cell to be adjacent to any excluded areas of urbanisation. The only requirement is that the distance to the nearest neighbour (NN) needs to be less than, or equal to, the mean nearest neighbour distance (MNND); in this case, the strategy will be to establish a corridor to the

150

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 3. Protective strategy. Establish corridor and create outer buffer. The green colour (A) is a patch of excluded area of urbanisation and the dashed line is the strategy. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

Fig. 4. Opportunistic strategy. Establish corridor between the patch and its neighbour. The green colour (A) is a patch of excluded area of urbanisation and the dashed line is the strategy. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

nearest neighbour (Fig. 4). As previously stated, this is a good strategy to enable some conservation strategies in specific areas that might increase the connectivity of specific areas or increase the capacity of having the core of a patch being protected from ‘exterior’ intrusive land uses. (Usually these strategies tend to be very localised and tend to maximise the selection of areas that are capable of meeting all the criteria for limited funding opportunities.) Applying the protective strategy also does not require that an urban cell be adjacent to any excluded areas of urbanisation, but the distance to the nearest neighbour (NN) does need to be greater than the mean

Fig. 5. Offensive strategy. Establishes an outer buffer to the patch and a corridor to its neighbour. The green colour (A) is patches of excluded areas of urbanisation and the dashed line is the strategy. The purple colour (B) corresponds to the original SLEUTH urban cells that will be replaced by a strategy. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

nearest neighbour distance (MNND); in this case the strategy will be to establish a corridor to the nearest neighbour, and a buffer around both patches (Fig. 3). Once again, the main goal is to enable conservation strategies and to increase the connectivity of the entire landscape (by doing so, we assume that we will promote the landscape ecological processes that answer species requirements). The existence (and number) of urban cells with some probability of change that are adjacent to an excluded area has an important function in the offensive and defensive strategies (Figs. 5 and 6). In the offensive and defensive strategies, the probability of a value changing will also play an important function: to apply an offensive strategy, at least 50% of the urban cells proposed by SLEUTH adjacent to a given excluded area (patch) need to be of high probability of changing to urban. It is important to note that we are calculating and measuring the landscape metrics on a patch-by-patch basis. The objective of the offensive strategy is to link a given patch to its immediate nearest neighbour and to create a buffer around the patch (Fig. 5). To apply the defensive strategy, fewer of the proposed urban cells need to be high probability of change to an urban land use; in this case, the defensive strategy will be applied to each SLEUTH high probability urban cell adjacent to the given patch, replacing that cell by a defensive cell (Fig. 6). When applying CVCA there is an issue regarding the existence of barriers to the establishment of corridors:

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

151

Fig. 7. Barriers. CVCA allows the establishment of corridors across barriers. The green colour (A) is patches of excluded areas of urbanisation and the black is the barrier. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

Fig. 6. Defensive strategy. 50% (all cells are low probability of change). Replaces the urban cell by a defensive cell. The green colour (A) is patches of excluded areas of urbanisation and the dashed line is the strategy. In the lower diagram, all cells marked with average probability of change are marked as defensive strategy. The green colour (A) is a patch of excluded area of urbanisation and the dashed line is the strategy. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

the protective, opportunistic, offensive landscape strategies imply the definition of corridors. The excluded maps of ecological and agricultural reserves do not include existing urban areas (or transportation) and when simulating urban growth and imposing the strategies at the same time, frequently a strategy ‘cuts across’ an existing urban area. This poses a potential problem, since it can be questionable whether we can ‘cut across’ these existing human structures each time the MNND in a Euclidean distance cuts an existent urban area (Fig. 7). In this research it was accepted that CVCA-generated corridors might cut existent urban areas. In reality this would imply an ecological restoration, indicated as an offensive strategy.

The question of retrofitting systems/landscapes to original conditions is examined in the discussion section of this paper, but it is important to emphasise that this is one of the challenges of CA at this moment (and much of the literature of environmental planning). Questions arise such as: ‘Does retrofit really exist in the landscape?’ ‘If so, how do we accommodate these, and other related, processes?’ These questions need to be taken into account in the research and development of applications of dynamic models of space and time. More detail is given in the discussion part of this paper; nevertheless, the assumption that ‘retrofit’ exists is an entire research subject in itself. Finally, there is a hierarchy of predominance among the different strategies: if a strategy is already placed in a cell and a new strategy is proposed afterwards, then the more ‘important’ strategy will win. The degrees of importance are assigned in the following priority order of importance: offensive, defensive, opportunistic, and protective (Fig. 8). As stated, the defensive strategy can assume two other spatial configurations: (1) if in this 50% of cells more than half are high probability of change, then add defensive cell to all other patches (Fig. 9); (2) if in this patch at least half of the cells are high probability of change, and the nearest neighbour (NN) patch is at half distance (according to the statistic presented by the mean nearest neighbour—MNN), then add defensive cell to all other patches and connect to the nearest neighbour (NN) patch (Fig. 10).

152

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 8. Overlap of strategies. Resolved by a hierarchy of most important strategies. The green colour (A) is patches of excluded areas of urbanisation and the grey cells are a protective strategy and the dotted cells are defensive strategy. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

These dynamics need to be understood in a multidimensional universe: each time step (i.e. each year) is a dynamic process, not only in terms of evolving through time, and with that evolution allowing different landscape situations/characteristics each year, but also throughout space (the dynamics across the entire matrix of cells) in every year, through the competition of strategies for each specific cell. Therefore each time step is more than a one-dimension time-step, but a multi-dimension, resulting from dynamic games locally binding for each cell, and globally reorganising an entire metropolitan landscape. From these interactions it will be possible to have a set of quantitative and visual outputs that will be able to answer the CVCA main research questions (Fig. 11): What is the state of the landscape? Which landscape strategies are predominant? Do these strategies create a different image of the metropolitan area? What is the dominant pattern? Are these strategies promoting connectivity? Which landscape metrics increase dominant strategies? 5. Lisbon and Porto Metropolitan Areas The Lisbon Metropolitan Area (AML) and the Porto Metropolitan Area (AMP) are the two areas of analysis of the research (Fig. 12). In order to better understand the resulting metrics within the calibration results and subsequently in the scenarios, the two metropolitan areas first need to be described. In recent times, several events have impacted on both metropolitan areas and on the country as a whole, as a result of different political, socioeconomic and cultural

Fig. 9. Defensive: probabilities 2. If in these 50% cells, more than half are high probability of change and NNI is <50% of MNND, add defensive strategy to all outer patch and link to close patch. The green colour (A) is a patch of excluded area of urbanisation, the dashed line is the SLEUTH probability of urbanisation. Because, these cells are 50% or more, the model assigns a defensive strategy (dotted cells). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

changes. The first period, before the revolution of 1974, was characterised by a centralised planning system. The second period was between 1974 and the end of 1986. The third period starts with the Portuguese membership of the European Community, from 1986 until the present. After the end of the dictatorship in 1974, a period of political instability and international economic crisis followed, including a massive return of populations from Portugal’s former overseas colonies. In the years after 1974, Portugal had to house 650,000 citizens from the colonies, around half of whom settled in the Lisbon Metropolitan Area.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

153

Portugal implemented urban rent control nationwide. The major consequence was a decline in the rental markets, degradation of the older urban areas, and, significantly, the acceleration of construction in the metropolitan peripheries. In the majority of cases in the north of Portugal, new houses were built by the land owners on their small parcels for their own use, promoting a more dispersed urban pattern, and compromising the viability of planning new developments, because of their irregular spatial growth. In the south of Portugal, small developers tended to market and sell to a local clientele, creating new urbanisation in the immediate periphery of Lisbon. This initial phase tended to develop organic growth, mainly around old nuclei and following the existing roadway systems. The second important factor was the Law of Municipal Economic Autonomy, bestowing on each municipality the right to income from licences it gave to build new homes. These factors, and low mortgage rates, led to very rapid urbanisation all over the country. The development of Municipal Master Plans (PDMs) with the goal of clearly defining buildable land, was severely constrained by the previous dynamics, ending up by proposing more urbanised areas than were actually required by possible demographic increases. 5.1. Lisbon Metropolitan Area (AML)

Fig. 10. Defensive: probabilities 1. If in these 50% cells, more than half are high probability of change, add defensive strategy to all outer patches. The green colour (A) is a patch of excluded area of urbanisation, the dashed line is the SLEUTH probability of urbanisation. Because in these 50% of cells half of them are high probability of urban change, the model assigns a defensive strategy (dotted cells). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

The decade of the 1980s—especially after the European Community’s massive investments in urban infrastructure, and with a growing European and world economy—stimulated a Portuguese ‘urban renaissance’. During this period, the importance of planning was also significantly reinforced, once several of the European Union (EU) directives to protect the environment had been imported and enforced in the Portuguese system (e.g. Environmental Impact Assessment Law). Two other factors are important in describing the two metropolitan areas. The first factor is the housing market and the rental laws which protected tenants. After the 1974 revolution that deposed the dictatorship,

The Lisbon Metropolitan Area contains approximately 2,641,000 inhabitants in an area of 2957 km2, producing a population density of approximately 800 inhabitants/km2. Population is concentrated mainly around the city of Lisbon—the capital of the country— and its central urban nucleus, and extends outwards along main roadways and railways (including the municipalities of Cascais, Oeiras, Amadora and Vila Franca de Xira). It was clear early on that the capital of the country and its environs needed integrated planning. Therefore, a metropolitan plan was developed and proposed to guide future activities and urbanisation. In 1964, the regional plan, ‘Plano Director da Regia˜o de Lisboa‘, was established to organise housing, industry, harbours, airports and tourism in the metropolitan area. Not approved by law, this regional plan provided a definition of a clear structure for transportation in the entire area, and this transportation system was largely adopted. A second phase in the planning of the metropolitan area was the regional plan, ‘Plano Regional de Ordenamento do Territo´rio’ in 1992 (only recently approved by law). Once again the different activities were organised throughout the metropolitan areas, with

154

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 11. Environmental growth model research questions.

a special emphasis given to the transportation infrastructure and its hierarchy. Both of these plans were important elements in structuring the transportation infrastructure and therefore constraining the intensity, direction and shape of urban growth throughout the metropolitan region (Silva, 1999). Two main bridges were built during these four decades, and have had a major impact on the organisation of the metropolitan space. The ‘Ponte 25 de Abril’ was built in 1966, and the ‘Ponte Vasco da Gama’ completed in 1998. The first is considered to be one of the main factors contributing to the intense urban pressures on the west side of the south margin of the Tagus River (municipalities of Almada, Seixal, Barreiro) (Silva, 1999). It seems likely that a similar process is happening on the east side of the south bank because of the 1998 Vasco da Gama bridge (mainly in the municipalities of Alcochete and Palmela). As opposed to the traditional compact city, with its organic growth, the contemporary metropolitan image is sprawling in suburbs further and further away and disconnected from the traditional urban centres. Three main landscape characteristics can be seen at the regional scale: urban growth along the transportation systems; large patches of forest or agriculture; and new urban nuclei being developed in the suburban fields though ‘commercial’ developers (Fig. 13). As opposed to the traditional compact city with its organic growth, the contemporary metropolitan image of Lisbon has lower density, sprawling in suburbs further and further away. In some of the metropolitan

area some of these urban areas are disconnected from the traditional urban centre, in a kind of ‘leap-frog’ effect (Silva & Clarke, 2002, 2005). Three main landscape characteristics can be identified at the regional scale: urban growth along the transportation systems; large patches of forest or agriculture; and new urban nuclei being developed in the suburban fields, through private sector-driven development. 5.2. Porto Metropolitan Area (AMP) The Porto Metropolitan Area houses a population of 1,581,694 in an area of 1573 km2, with a population density of approximately 1000 inhabitants/km2. The AMP is characterised by dispersed urban settlements, with the highest densities in the municipalities of Porto, Vila Nova de Gaia, and Matosinhos. The city of Porto is the main nucleus. Just as in the Lisbon Metropolitan Area, the last 25 years have also seen intense urbanisation in the Metropolitan Area of Porto (AMP). Two main time periods define the evolution of the Porto Metropolitan Area: the 1950s and the 1980s. During the 1950s, new roadways linked the North with the Lisbon Area (e.g. Vias Norte and Via Ra´pida). This period also saw the construction of the ‘Ponte da Arra´bida’, finished in 1963, linking the city of Porto with the region to the South (municipality of Vila Nova de Gaia). This bridge was one of the major elements to shape the future of urbanisation on both banks of the Douro River. The 1950s also saw a major political action that moved urbanisation outward to Porto’s urban fringe: the

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 12. Location of the Lisbon and Porto Metropolitan Areas.

Porto plan ‘Plano de Melhoramentos’ (1956). This plan, though confined to the city of Porto, was a very important force in the renewal of the industrial population from the city centre, who lived in slums called ‘ilhas’. This kind of ‘urban renewal’ period had a major influence on the relocation of population to the urban periphery. Development of north–south transportation infrastructure along the coast, as well as the development of the new port, ‘Porto de Leixo˜es’, also took place. The consequence was a west urbanisation axis, which included the municipalities of Matosinhos and Vila Nova de Gaia. Industries, facilities for tourism, and housing were built densely in the seashore areas. The 1980s included the construction of the ‘Ponte do Freixo’, which began a second phase of urban/ transportation change. This time another axis developed, first intensifying the east–west connection, reinforcing the municipalities of the metropolitan area,

155

and in a second phase, regionally within the east and northeast of northern Portugal. At the same time the roadway system of the western municipalities of the Porto Metropolitan Area was consolidated. This reinforced the connectivity with the centre of Portugal (mainly the littoral), and with the capital of the country (Lisbon). Two other main characteristics should be highlighted in order to understand the urban pattern of the Porto Metropolitan Area. The first is a polycentric model of dispersion, promoted by state authorities. This model is used both to extract advantages from historical patterns (populations and activities scattered throughout the area) and, at the same time, to reinforce that tendency. The second characteristic was a total absence of regional planning in the area that now comprises the Porto Metropolitan Area, once again reinforcing the scattered populations and activities of the region. The aerial photo of 1995 reflects this scattered character of activities and populations and a very fragmented land use pattern. Therefore, in contrast with Lisbon, the Porto Metropolitan area is characterised by dispersed urban settlements, with the highest densities in the municipalities of Vila Nova de Gaia, and Matosinhos Porto. This traditional tendency for dispersed populations and their associated support activities has resulted in small patches of forest, agriculture, industrial areas, and several small urban nuclei throughout the metropolitan area (Fig. 14). Recently, the Porto Metropolitan Area has been subject to a different planning process. Regional transportation planning has been completed, industry has been planned and built in concentrated industrial parks, and large-scale private-sector development is reshaping the ‘regional’ character of the Porto Metropolitan Area. As already detailed in other papers (Silva & Clarke, 2002 and 2005; Silva, 2002; Silva, 2004) the Porto Metropolitan Area is changing the spatial organisation of land uses through a set of master plans and other regional policy actions. Consequently, new opportunities and threats to Porto’s more historical landscape character are unfolding. 6. Application of CVCA future simulations to Lisbon and Porto In order to test the algorithm of CVCA and the interaction of CVCA with SLEUTH, two metropolitan areas were selected: the Lisbon and Porto Metropolitan Areas in Portugal (Fig. 12). One of the reasons for this selection was the fact that they are fundamentally different in terms of landscape

156

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 13. Aerial photo representing the regional character of the AML (Aerial Photo, 1995, n8 3874, line 60, Lat. 38.61167, Long. 9.123611, Municipality: Seixal, Freguesia: Amora.). The Tagus river is visible at the top of the photo. #CNIG.

characteristics (type and size of patch) and socioeconomic characteristics (different densities of population, different transportation structure), but also in terms of the planning processes they were subject to (more incremental versus more rational in scope and application). Also, these landscape and socioeconomic differences make these two metropolitan areas very interesting for studying the sensitivity of the local conditions of the SLEUTH and CVCA models. As mentioned, the landscapes of the Lisbon and Porto Metropolitan Areas vary significantly in many respects. Both present different geomorphologic features, different climate and vegetation, and different traditions and patterns of human occupation and settlement. This research is particularly concerned with how these different characteristics are manifested in the

different sizes and spatial arrangements of environmental patches and the consequent variation in metrics. In order to successfully run SLEUTH, a database had to be built for each Metropolitan Area (a detailed description of the data requirements for the model can be read in Silva & Clarke, 2002). The Lisbon Metropolitan Area dataset was easily developed, particularly when compared with the Porto Metropolitan Area. Several reasons can be pinpointed for this ease of data collection and treatment: the database for Lisbon has been built for a longer time period and was well documented (by the National Centre of Geographical Analyses, by the Metropolitan Authority of Lisbon, and by myself when digitising or classifying the remaining datasets that were not yet available). The Porto database was being compiled at the time of the

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

157

Fig. 14. Aerial photo presenting the regional character of the AMP (Aerial photo, 1995, n8 2981, line 19, Lat. 41.07639, Long. 8.626667, Municipality:Vila Nova de Gaia, Freguesia: Gulpilhares). The Atlantic Ocean is the dark shade that covers the left side of the image. #CNIG.

analysis and both projects, developed by the Porto Metropolitan Area and by the School of Engineering of Porto, were still ongoing (the authors of this paper also had to produce a substantial number of the data sets required). Because of data availability, the classification of the Landsat images was also easier in the case of Lisbon. Tables 4 and 5 present a synthesis of the different layers required and their respective grid values. The Landsat images resulted from two main sources: Eurimage and United States Geological Survey (USGS). The last years from the 1980s and 1990s were generously offered by Eurimage, and the images of the 1970s were both given by USGS. The Lisbon Metropolitan Area land uses resulting from classified images were used both as a data source, and as a way to

compare classification results (Silva, 1999; Silva & Clarke, 2002 and 2005). The development of the urban data layers was the result of a supervised classification of the Landsat images. (Using a supervised classification increased the reliability of the resulting urban maps. Several pilot areas were previously collected from other maps and ground surveys, then juxtaposed with the images in order to be sure that the algorithm was accurately selecting the urban areas in the remote sensing images.) From the classified images there resulted a binary file of urban–non-urban, with the urban pixels including all the spectral signatures that recorded impervious surfaces resulting from human activities. The Landsat data restriction of a 30 m  30 m pixel limits the minimum unit possible to extract (approx. 60 m  60 m). Several

158

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Table 4 Description of the input layers for the Lisbon Metropolitan Area database. AML

Description

Layer

Number of layers

Number of years

Number of classes

Cell value

Source of data

Slope

1

–

–

0–768 (0–373%)

CNIG-RCV*

Excluded

1

–

1. Ecological reserve (REN) 2. Agricultural reserve (RAN)

1. REN = 100 2. RAN = 50

Municipal master plans

Urban

4

1975, 1984, 1995, 1997

1. Urban

Transportation

2

1987, 2000

1. Base roads (BR) 2. Highways (HW) 3. Proposed (PR)

Hillshade

1

–

*

Landsat images (MSS, TM: Eurimage, USGS) 1. 50 2. 100 3. 25

1987 roads provided by ACP 2000 roads provided by PROT

0–3658 Azimuth-315 Altitude 45

DTM with 100 m  100 m cell size

Centro de Informac¸a˜o Geogra´fica (#CNIG, Portugal), Project ‘Rede de corredores verdes’

elements below that minimum threshold are therefore automatically clumped by predominance. The transportation layer was derived from several sources. In the case of the Lisbon Metropolitan Area, the most recent year was extracted from the Regional Plan for the Lisbon Metro Area (PROT). The earliest year was obtained through the digitalisation of a 1987 road map from ACP (Automo´vel Club de Portugal—a national transportation association). In the case of the Porto Metropolitan Area, the 2000 map was a result of the compilation of the Municipal Master Plan roadway maps, and several other forms of auxiliary data information. The same process was applied to obtain the 1987 road map. Because the model allows us to weight the different roads with values that reflect their importance in the

transportation systems, the roads were ordered by degree of importance in both metropolitan areas. Three classes were defined: base roads, highways and proposed roads. The values were equally distributed between 0 and 100. (We are aware that further studies need to be done, in order to be sure of the degree of importance in the network and to then assign a value that can more accurately reflect that road.) The assigned values were: highway 100, base roads 50 and proposed roads 25. The excluded-areas layer contains the areas of water and land where urbanisation cannot occur. In both study areas the information was taken from the Municipal Master Plans, predominately from two classes defined by law: the ‘ecological reserve’ (REN) and the

Table 5 Description of the input layers for the Porto Metropolitan Area database. AMP

Description

Layer

n8 layers

n8 years

Slope

1

–

Excluded

1

–

Urban

4

1975, 1987, 1997, 2000

Transportation

2

1987, 1999

Hillshade

1

–

n8 classes

Cell value

Source of data

0–768 (0–373%)

CNIG

1. Ecological reserve (REN) 2. Agricultural reserve (RAN)

1. REN = 100 2. RAN = 50

Municipal master plans 1975, 1987, 1997 Landsat images (MASS, TM: Eurimage and USGS); 2000 Municipal master plans

1. Base roads (BR) 2. Highways (HW) 3. Proposed roads (PR)

1. 50 2. 100 3. 25

1987 extracted from AICP, 1999 Municipal master plans

0–3658 Azimuth-315 Altitude 45

DTM with 100 m  100 m cell size

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

‘agricultural reserve’ (RAN). The objective of the RAN was to spatially define areas of high quality soils for agriculture. The objective of REN was to protect biophysical areas of high importance. This objective requires each municipality to spatially define areas such as coastal areas, stream networks, steep slopes, aquifers. For both Lisbon and Porto Metropolitan Areas, this layer also contains weight values indicating probabilities of exclusion. These values tended to vary according to the required simulation (from 100—meaning no development allowed; to 50, allowing some development in the areas prone to higher pressures). Because both RAN and REN are non-buildable areas according to Portuguese law, these layers were classified as having 100% level of exclusion. Nevertheless, after comparing the degrees of implementation of RAN and REN throughout the metropolitan areas, it was clear that some RAN and REN areas were being urbanised (through illegal development or through a legal process called detachment). Consequently, we decided to generate another simulation that would allow some development in RAN (while maintaining 100% protection in REN). This option, of allowing urbanisation (up to 50%) in some areas, was the result of field observation which led us to conclude that classified agricultural areas were the most affected by urban development. The slope layer results from the average per cent slope computed from a 100 m Digital Terrain Modelling (DTM). The hillshade layer is used as a visual display of model results in the graphic version of the model. As stated, the previous files were used as input files to the SLEUTH model. Because CVCA was built in order to work together with SLEUTH, while it requires only two input files (an adapted map of excluded areas of urbanisation and the urban output files produced by SLEUTH), it does require all the base files in order to allow the strategies to vary according to the probabilities outputted by SLEUTH. In other words, if the only goal is to generate an initial base of landscape ecological strategies, with no other dynamic allocation of urban cells through time, in that case it will be enough to have a map/layer of urbanised areas and a map/layer of excluded areas of urbanisation. 6.1. Assessment of the metrics computed by CVCA The selection of landscape metrics and their inclusion in the CVCA model are important to assess the current state of the landscape, and also to implement the landscape ecological strategies in order to develop alternative future scenarios. As previously stated, seven

159

metrics were selected for the CVCA model: edges (number of edges), area (total area of natural interest), number of clusters, mean cluster size (MCS), mean patch size (MPS), landscape shape index (LSI), and mean nearest neighbour distance (MNND). This set of metrics was chosen as the internationally accepted indicators of key ecological processes and spatial patterns (i.e. fragmentation, connectivity, patch size distribution) (Gustafson, 1998; Kumar, Stohlgren & Chong, 2006; Leitao & Ahern, 2002; Matsushita, Xu & Fukushima, 2006; McGarigal & Marks, 1995; Wei & Hoganson, 2005). Of the previous seven metrics, the following three play an important role in the assessment of the state of the landscape, or in the application of the landscape strategies.: the landscape shape index (LSI); the mean patch size (MPS); mean nearest neighbour distance (MNND). Because a key objective of the modelling was the definition of corridors and buffers around the natural areas, it was important to evaluate the size, number and distance between patches. Also important was the assessment of the overall state of the landscape, through the landscape shape index (LSI). As mentioned, while this metric was not used to implement the landscape strategies, it was useful for comparing different metropolitan areas and was therefore included in the model. These metrics were computed for the layer in CVCA that contains the ecological and agricultural reserves (RAN and REN), and some parks of the Lisbon and Porto Metropolitan Areas. These layers include parcels of high quality soils for agricultural production, as well as parcels of protected forest (i.e. for cork production), and land areas under ecological protection, for instance important biodiversity sites. The sections of this paper reporting on the code development of CVCA pointed to the fact that this model requires an initial base layer, which contains the nature areas that will be computed using the previous metrics. This base layer is not the same as the excluded areas of urbanisation layer used by SLEUTH. Some adjustment had to be done, in order for the metrics to calculate the effective size and location of the different patches of nature. As stated, besides the layer of excluded areas of urbanisation (that acts as the ‘seed layer’ in CVCA), CVCA uses the resulting urban simulation maps of SLEUTH. SLEUTH simulates an urban year, CVCA imports it, assesses the location of the simulated urban cells, and juxtaposes a set of strategies or simply lets those cells grow. Tables 6 and 7 present the compiled metrics for each metropolitan area. As anticipated, both metropolitan

160

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Table 6 Landscape metrics for AML (possible environmental DNA of the AML). Metric

Value

Edges Area Num clusters MCS MPS LSI MNND

35,171 106,460 1,134 93 577 9.9 1.6

Table 7 Landscape metrics for AMP (possible environmental DNA of the AMP). Metric

Value

Edges Area Num clusters MCS MPS LSI MNND

14,964 24,207 708 34 275 7.7 1.5

areas present very different metrics as a result of their fundamentally different landscapes. Similarly to what happened with SLEUTH, these metrics correspond to the DNA of the landscape ecological characteristics of each metropolitan area (Gazulis & Clarke, 2006; Silva, 2004). The Lisbon Metropolitan Area has more edges than the AMP (AML has a total number of 35,171, and AMP has 14,964 edges), but a lower mean nearest neighbour distance (the AMP has a MNND of 1.5 while the AML has 1.6). This is due mainly to the fact that the landscape pattern of the Porto Metropolitan Area looks like a fine-grained checkerboard. The overall image is of a landscape with small areas of aggregated urban cells and small patches of excluded cells of urbanisation. The consequence of this spatial organisation is a highly fragmented landscape (with high LSI), and small MNND (since these small patches are all relatively close to one another). The mean patch size (MPS) presents expected results for both metropolitan areas. The Lisbon Metropolitan Area has larger patches of forest, agriculture and natural areas, and therefore the MPS value of 577 seems to be in accordance with the landscape. This is reinforced when comparing it with the Porto Metropolitan Area, which has an MPS value of 275, reflecting a landscape characterised by small properties and an absence of large patches.

The landscape shape index (LSI) is an important metric, since it reflects the current state of the landscape. The LSI values therefore provide an idea of the degree of ‘irregularity’ of the landscape being studied, and also make it possible to compare ‘degrees of irregularity’ between different landscapes. A value of one LSI indicates a landscape with high connectivity; consequently the respective LSI values of 9.9 for the AML and 7.7 for the AMP, disclose landscapes with different characteristics. A first analysis indicates that the landscape of the AML might be less connected, since the LSI is larger and therefore the landscape should be more ‘irregular’. Nevertheless, the comparative analysis of a slightly lower value for the Porto Metropolitan Area (LSI = 7.7) does not imply a more connected landscape, because of the ‘checkerboard pattern’ of the Porto Metropolitan Area. The relative close proximity of the natural and urban patches has an effect on the computation of this metric (the very fine-grained checkerboard pattern). In fact, the close proximity of the patches increases the fragmentation in the case of the Porto Metropolitan Area. The analysis and conclusion above are significant, since they disclose the relative importance of the metrics, and the importance of having ground-base knowledge about the landscape. It is important to check the analysis of the metrics against a field assessment. These selected landscape metrics, for both the Porto and Lisbon Metropolitan Areas, provide a useful assessment of the landscape state (i.e. average size of patches, distances between them), and work as the base values for implementing and comparing the landscape ecological strategies for simulations of future conditions. Assessment of the landscape metrics also supports comparative analysis of the metropolitan areas. 6.2. Simulating future urban environments for the Lisbon and Porto Metropolitan Areas Once the landscape metrics had been compiled for each metropolitan area, it was possible to simulate future scenarios for both metropolitan areas simultaneously with SLEUTH and CVCA. As already pointed out, SLEUTH is the acronym for Slope, Land use, Excluded areas of urbanisation, Urbanisation, Transportation, and Hillshade. These are the five layers required in order to run: urbanisation, transportation, excluded areas from urbanisation, slopes, and hillshade. The output of SLEUTH is a set of simulations that places urban growth against a

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

hillshade background, where it is possible to ‘see’ and quantify urban growth evolving over time. Urban growth is classified according to its probability of urbanisation (i.e. high probability, average probability, and low probability). CVCA is a novel software program written by Silva and Wileden at the University of Massachusetts, Amherst (Silva, 2003; Silva, Ahern & Wileden, 2003; Silva, 2002,) to implement landscape strategies. CVCA works closely coupled with SLEUTH. The ‘excluded areas from urbanisation’ seed layer used by SLEUTH is replaced in CVCA. This is due to requirements to compute the landscape metrics and to identify unbuilt areas. The images resulting from running CVCA and SLEUTH together are primarily simulations that place urban growth against the hillshade background. Along with the red pallet of urban probabilities of change, three colours predominate (Figs. 15 and 16): brown (opportunistic strategy), light purple (protective strategy and offensive), dark purple (defensive strategy). Besides the visual assessment of the different colours which each cell allows us to perceive, it is also possible to quantify the number of cells for each landscape strategy, and for each probability of urbanisation. This result of the model tends to be as important as the visual images it outputs. With this analysis it is possible to pinpoint clearly which areas are subject to intense urban pressures, and might therefore deserve some kind of planning strategy, in order to protect or promote the existence of ecological corridors, patches of forest/ agriculture or other natural amenities Figs. 15 and 16 present the simulations resulting from running CVCA coupled with SLEUTH. The images present the strategies that CVCA executed immediately after the seed year (1998, in the case of the AML; 2000, in the case of AMP), once no other layer of ‘natural areas/excluded layers of urbanisation’ were included. This excluded-areas file used in the first time step needs to be used as a base-file through time for the remaining years of the simulation. We believe the functioning of the model could improve if we could have more than one layer/year of natural areas excluded from urbanisation, in order to calibrate the model according to the evolution of those excluded areas of urbanisation through time—i.e. are those excluded areas of urbanisation being built on independently of the regulations that protect them? If so, the model should reflect that. A visual analysis of the images reveals the impact of the planning strategies on the simulated urban form. The most visible strategy is the defensive strategy, with the vast majority of the area being represented in this

161

colour (purple colour in the image—letter A), the predominant pattern is the development of buffers along the riparian corridors, parks, forest and agriculture patches. The other two strategies are not visible at this scale, but by zooming in on the images (Figs. 17 and 18) it is possible to see some cells of the protective strategy (brown cell indicated in the image by an arrow and letter B) and also some cells of offensive strategy (dark purple —letter C). The shape and size of the patches of this strategy tend to be very small (one, two or groups of approximately four cells). For both metropolitan areas it is also possible to see how the different cells are organised spatially, and to what extent that organisation avoids breaking up big patches of nature, or increases connectivity by defining links (corridors) between patches. It appears that the defensive strategy buffers surround the natural areas (as explained in the previous paragraph), or links protected patches (for instance the south bank of the Tagus river); it is possible to see that the north and the south of the Peninsula de Setu´bal now have open corridors, that link its borders and crisscross its interior, allowing therefore the establishment of greenways throughout the peninsula of Setubal. (The Peninsula of Setubal is the land area at the south of the Tagus River; it is characterised by rich agricultural soil, and its subsoil has important water reserves.) By performing a year-by-year visual analysis of the images, the simulations reveal that some images seem to have slightly increased agglomerations/concentration of urban cells when the strategies are applied (Table 8). This reveals the fact that the intensity of urban growth is not the same through time, but that there are specific years in which it tends to happen faster. It is also interesting to see the time-limit that the strategies impose on urban growth. For example, the strategies prevent urban growth until 2006, and the urban growth is contained close to existent nuclei (this seems to be happening, both in the Lisbon and the Porto Metropolitan Areas). Furthermore, urban growth is not intense from 2006 to 2015, but the periphery of the metropolitan areas records several new growth areas. From 2015 onward, urban pressures become very intense and urbanisation cells are proposed very rapidly (as if the system reached maximum capacity for restraining urban pressure during the previous years and suddenly released a high number of urban cells into the system). The number of protective strategies is minor and unrepresentative at this scale. The most obvious reason for this might be related to the urban pressures being so intense that, rather than protecting existing natural areas, the level of ‘protection’ needs are so high a

162

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 15. Simulation with CVCA, the AML 1998–2025. The yellow cells represent urban growth. The grey hillshade background was clipped to the administrative boundary of the AML. Black represents the outer area of AML (mainly the Tagus estuary to the southwest, and land at the northeast). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

defensive strategy is required. Being so scarce and dispersed, the protective strategies are almost invisible in printouts that present the entire metropolitan area. Nevertheless, these protective strategies were quantified

(Table 8) and can also be visually assessed in Figs. 16 and 17. In order to do so, it is necessary to zoom into the image and amplify the selected area to allow those cells to be viewed.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

163

Fig. 16. Simulation with CVCA, the AMP 2001–2025. The yellow cells represent urban growth. The grey hillshade background was clipped to the administrative boundary of the AMP. Blue represents the area outside the AMP (the Atlantic Ocean to the west, and land to the east). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the paper.)

The opportunistic strategy does not exist because of the rules established. In most areas of the AML and AMP the urban pressures are so intense that the opportunistic strategy tends to be overpowered by other,

more ‘important’ strategies in the hierarchy of strategies defined earlier during code development. These metropolitan areas suffer from very intense pressures. Each time an opportunistic, or a protective strategy is

164

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fig. 17. Spatial organisation of the strategies for AML, 2025.

proposed by the model to a specific cell, if another strategy is subsequently proposed to that same cell, that subsequent strategy has a higher degree of importance. In such a competitive environment, therefore, lower importance strategies tend to be overtaken by more proactive strategies (Fig. 17). Complementarily to the previous analysis, the quantitative evaluation of urban cells proposed by the SLEUTH model when running together with CVCA (Table 8) reveals that the high pressures of urban growth have a predominance of cells in both metropolitan areas. For the year 2025 (a year that has a larger number of high probability of urban cells), in the AML the high pressure urban cells are 27,220, against 3548 cells of average pressure; the Porto Metropolitan Area in the same year has 3332 cells of high probability, against 412 of cells of average probability. This high number of urban cells with a high probability of urbanisation is considerable in the case of the Lisbon Metropolitan Area: for the year 2010 the number of high probability urban cells is 14,639, and 27,220 for the year 2025. Comparatively, the number of ‘average’ urban cells (sum of green-sum of all the

SLEUTH probabilities of growth, with exception of the high probability) is 5114 for the year 2010, and 3548 for the year 2025. These values are illustrative of the intense urban pressure; for 2025 there is an increase of 53% of high probability urban cells/pressures, when compared with the actual urban area. The Porto Metropolitan Area (AML) also presents different classes of urban growth, with the high probabilities of change being larger than the average and average-high probabilities. Nevertheless, the difference between the high probability and the ‘average probabilities’ is not as high as in the case of the AML. For the year 2010 in the Porto Metropolitan Area, the high probability of urban pressure is found in 1549 cells and the ‘average probabilities’ apply to 1087 cells. The year 2025 yields values of 3332 for high probabilities of urban growth, and 412 for the average probabilities. These values are illustrative of the intense urban pressures; for 2025 there is an increase of 5.9% of high probability urban pressures relative to the actual urban area. The overall image of the Lisbon Metropolitan Area shows large areas excluded from urbanisation. The north boundary of the river Tagus presents several such patches, mainly included in the major protected areas (in the Sintra and Mafra municipalities, and in the Tagus estuary) and also in Azambuja, in the corridor in the north boundary of this municipality. The south margin of the river Tagus has several excluded areas of urban growth: the heartland of the municipality of Palmela is one such area (among other attributes, this area is very important for groundwater recharge). Locally, other areas can be identified; Fig. 7 presents some examples of such areas. Three strategies can be identified: protective cells in a very urbanised area indicate a last chance to preserve a natural area. The defensive strategies reveal the importance of the landscape strategies in order to contain urban growth (or to direct it to more suitable areas), through the development of corridors or the establishment of buffer zones that impose a kind of urban growth boundary, and to redirect urban expansion. Therefore, each new year, SLEUTH proposes urban cells that might be juxtaposing a specific landscape strategy. This action is constrained by CVCA, and in order for SLEUTH to fulfil the required urban pressures, SLEUTH needs to place those cells in other areas/cells available for urbanisation, but not committed to a landscape ecological strategy. Fig. 18 discloses once more the importance of the strategies, using the case study of the Porto Metropolitan Area (AMP). While the overall image of the AMP is not as organised as that of the AML, it is possible to

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

165

Fig. 18. Spatial organisation of the strategies for AMP, 2025.

evaluate the impact of the strategies on the Porto landscape. Locally, by zooming into specific areas of the image, it is possible to see that small patches of protective strategies tend to be represented in the images, but the defensive and offensive strategies have a greater weight and therefore predominate. The arrows of Figs. 16 and 17 present some examples of the different strategies. The dashed line of Fig. 17 indicates the importance of the landscape strategies in avoiding the closing of the mouth of a river stream, or in keeping

the seashore free of urban pressures. It is important to point out that these are landscape planning strategies that use biodiversity measures (the size of patches, the proximity between patches, the establishment of corridors between patches, the understanding of the state of the landscape) in order to prevent uncontrolled urban growth, allowing us to channel required urbanisation to areas deemed good-to-grow. Overall, we can state that CVCA is answering a pressing need to have most of the biodiversity metrics

Table 8 Landscape strategy cells and urban cells for AML and AMP.

Yellow (existent urban cells) Green1 (simulation of low probability urban cells) Green2 (simulation of low to mid-probability urban cells) Green3 (simulation of mid-probability urban cells) Green4 (simulation of mid- to high probability urban cells) Red (simulation of high probability urban cells) Sum green (sum of the low to mid-probability) Protective Offensive-6 Defensive

2001 AML

2010 AML

2025 AML

2001 AMP

2010 AMP

2025 AMP

51,417 12 1,331 1 2 1 1,346 115 447 40,636

51,417 676 931 1,296 2,211 14,639 5,114 115 447 40,636

51,417 482 626 893 1,547 27,220 3,548 115 447 40,636

56,249 1 1 1 1 2 4 50 26 8,052

56,249 138 155 278 516 1549 1,087 50 26 8,052

56,249 37 64 103 208 3,332 412 50 26 8,052

166

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

included in dynamic models that assess the state of the landscape and plan landscapes according to biodiversity needs. CVCA goes further, by building on existing, well-documented metrics, and explores a set of landscape ecological planning approaches as a way of enhancing landscape patterns and processes. Thirdly, by interacting with an existent urban model, CVCA manages to divert urban growth to ‘good-to-grow’ areas, preserving/enhancing other areas of landscape and ecological interest. Finally, by allowing the interaction of the simulated urban cells with the five landscape ecology strategies, CVCA can point out to decisionmakers the location of the cells which need to be protected/preserved, and the degree of urgency required in implementing the protection efforts. Because the Porto and the Lisbon Metropolitan Areas are very dynamic landscapes, subject to intense urban pressures, CVCA proposed offensive and protective strategies as being the ones that can best cope with the intense urban growth. Consequently, the opportunistic and the defensive strategies tend to be under-represented. The visual assessment tends to be very realistic, the model tends to propose the strategies in places that we know are being subjected to urban pressures or that will be in the future. It is also worth noting that some of the proposed corridors will certainly enhance connectivity, such as the corridor that will link north–south the ‘Peninsula de Setubal’, connecting both estuaries of the river Tagus and the river Sado. The next section looks at the different models being developed using cellular automaton, and the importance of CA in bringing together different subject areas, and in starting to plan landscapes in a holistic way. It should be stressed that we still lack planning tools in order to perform integrated analysis, and that CA applications are important and are able to contribute, both quantitative and qualitatively, to the study and planning of landscapes. 7. Discussion This paper reports on the development and application of CVCA. During the development of this application, multiple questions were raised: the importance of integrated analysis of environmental and urban issues; the multiplication of ecological and biological applications that tend to avoid the spatial component; the importance of sensitivity to local conditions; and the importance of applying urban and environmental metrics to CA and other artificial intelligence applications.

In the following four sections we explore the questions raised by this research, according to four different subjects:  The results of applying SLEUTH and CVCA to two metropolitan areas;  The importance of CA approaches and applications;  The lack of planning tools; and  The importance of integrated planning strategies for the sustainable management of territory. 7.1. Results of applying SLEUTH and CVCA to two metropolitan areas One of the most interesting outcomes of applying CA to these two case studies was the verification that both models are sensitive to local conditions, and that CA is a very good modelling approach if one requires models adapted to local conditions. Cellular automata have the particular feature of adapting themselves to the characteristics of a place. The fact that land is represented in a cell-by-cell kind of surface, where each cell stores characteristics of itself and the surrounding four or eight neighbours, makes it particularly suitable, not only to represent the territory, but also to simulate its evolution. In terms of understanding the land being studied, one of the most useful results comes from the identification of a list of numbers that describes the behaviour of the system. These numbers can be classified as the DNA of the metropolitan region (Gazulis & Clarke, 2006; Silva, 2004); through these sets of numbers (metrics) we can describe the region without having to see a map or read its history. It is certainly a simplification of reality, but a very useful simplification—not only in terms of describing a region, but also as a powerful tool to test changes in some (or all) of the elements that control the behaviour of that specific system (i.e. testing policies). It is easy to compare the Lisbon and Porto Metropolitan Areas, when CVCA presents a landscape shape index (LSI) of 9.9, a mean patch size (MPS) of 577 and a mean nearest neighbour distance (MNND) of 1.6 for the Lisbon Metropolitan Area; and an LSI of 7.7, an MPS of 275 and an MNND for the Porto Metropolitan Area. It is easy to understand that the patch size in Porto tends to be smaller than in Lisbon, that distance between patches tends to be bigger in the case of Lisbon, and that the Lisbon LSI tends to perform differently from that of the Porto Metropolitan Area. These numbers are important, because they contribute to the correct understanding of both landscapes. They also raise other questions, such as the meaning of

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

the LSI being different in both metropolitan areas, with a slightly better performance in the case of Porto. Reflecting on the reasons for this diversity led to the understanding that the checkerboard effect from the Porto Metropolitan Area could be causing this difference, presenting a value that could be indicative of a different reality. Fieldwork is therefore important, to clearly describe and include the main variables that describe the behaviour of the system. As stated, the value of 1 indicates a landscape with high connectivity. Consequently, the respective LSI values, of 9.9 for the AML and 7.7 for the AMP, could indicate that the landscape of the AML might be less connected, since the LSI is larger and therefore the landscape should be more ‘irregular’; on the contrary, however, it indicated that the AMP is highly fragmented. The same kind of ground verification can be seen in other phases of developing the model and selecting the best scale of analysis. For instance, when selecting the metrics for CVCA we realised that a cell-by-cell matrix of 100 m  100 m would raise questions in terms of the representativeness of the values of the metrics: we had to choose between changing the minimum size cell, changing the metrics, or changing the calculation values per metric. The subjects of the selection of metrics and the scale of analysis are fundamental, and need to be carefully considered when the models are being developed. Operational CA should require a calibration process that is both the result of refining the metrics, but simultaneously the result of refining the scale of analysis (starting from a more coarse scale and then progressing to finer scales). This points to the fact that not only is calibration of the model important, but also that a good understanding of the metrics is a precondition to the success of any analysis. The same considerations should apply when selecting the minimum unit of analysis that can constrain the maximum scale of detail to be analysed (the representativity of objects tends to be twice the minimum size of the unit of analysis). These ‘laws’, defined in cartography and remote sensing, tend to be neglected when developing these applications, which impacts on the model/algorithm behaviour and the output results and analysis. The importance of calibration underlines another need that was fulfilled by these two models: the importance of having an open code, and the importance of having it flexible, so that it can be changed if necessary. It is also important that the codes/software are set in a modular kind of structure, that would allow

167

us to input other metrics, that could better represent the behaviour of the system. This would then enable/disable the CVCA and SLEUTH from compiling metrics and simulating future planning strategies for the regions. A visual analysis of the output images resulting from CVCA and SLEUTH is also very illustrative of the sensitivity to local conditions shown by both models. It also presents the importance of these images for planning purposes. These images are illustrative of the characteristics of the region; they present a good portrait of the regions under study. While they are important for analysis (quantitatively and visually), they can also be used in terms of proposing scenarios; and in selecting the scenario that best fits the metropolitan regional image (vision), and the scenario that has the best output quantitative values in terms of system behaviour. Again, in terms of the CVCA, it is possible to detect where the areas with the most urban pressures are, where the different strategies are located, and what kind of patterns and processes they promote. These can be seen in terms of quantitative analysis (how many cells per strategy, how many urban cells, and what kind of probability associated with these urban cells), but also in terms of a visual assessment. A visual analysis of the images reveals the impact of the planning strategies on the simulated urban form. As stated, the most visible strategies are the offensive and defensive strategies, with the vast majority of the area (purple colour in the image—identified as image A); the predominant pattern is the development of buffers along the riparian corridors, parks, forest and agriculture patches. When visual analysis of the images on a year-by-year basis is performed, there is another notable element that can be seen. The simulations reveal that some images seem to have slightly increased agglomerations/concentration of urban cells when the strategies are applied. For example, the strategies prevent urban growth until 2006, and the urban growth is contained close to existent nuclei (this seems to be happening both in the Lisbon and the Porto Metropolitan Areas). Furthermore, urban growth is not intense from 2006 to 2015, but the periphery of the metropolitan areas records several new growth areas. From 2015 onward, urban pressures are very intense and urbanisation cells are proposed very rapidly. These kind of visual analyses through time (and not only at one unique image/year) raise relevant questions about urban pressures: can we assume that urban pressures were building up during a period of time and afterwards they ‘exploded’ in a kind of uncontrolled spill-over? And if so, can we assume that constraining urban growth too much might have a different effect in

168

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

the long term, opposite to what was planned, and very negative when compared to the results after the shortterm period of too restrictive urban growth control? The results also raise other questions, in terms of the relationship between demographic statistics, that tend to point to reduction of population, and urban consumption, that keeps growing. (In order to study and assess socioeconomic issues other models need to be coupled with SLEUTH and CVCA.) 7.2. Importance of cellular automaton approaches and applications One of the advantages of using CA is the fact that it clearly represents the territory. CA represent the phenomenon under study using a grid space of cells, cell-states, neighbourhood effects and transition rules. Cells are the backbone of CAs—they are its smallest units, and each cell state changes according to transition rules functioning in line with neighbourhood characteristics (its closest four or eight cells), self-modification rules, and other defined elements of the model. While the neighbourhood effect is a basic concept of the CA, the transition rules need to be clearly stated. They are pivotal for a realistic simulation of the phenomenon under study, and therefore much time needs to be spent observing and understanding the behaviour of the studied elements. This is the main reason why so much attention was given to this issue. The success of a realistic simulation has as much to do with calibration as, in the first instance, it concerns the understanding of the system being represented and its behavioural mechanics. The rules are applied to every cell, state and neighbourhood, and every change in each state must be local. This represents the most pure kind of CA, where no action at a distance is allowed in order to have self-organisation happening. It is therefore totally dependent on the local historical evolution of the elements that represent the behaviour of a system. By action at a distance, we mean actions that are indiscriminately applied to all land, independently of spatial configuration by a central or regional authority. Regarding the application of CA to the study of landscapes and to the behaviour of CVCA, the basic cells usually correspond to land uses (excluded areas of urbanisation that are the ecological and agricultural reserves). The transition rules correspond to the processes that are associated with land change (in this case, avoiding the transition of land from non-urban uses to urban uses in cells with ecological interest), promoting in this case greater connectivity, when new

corridors are established between patches, but also increasing the size and/or protecting the core of existing patches (promoting/preserving ecologic processes). SLEUTH applies behaviour rules, when change from a non-urban to an urban cell implies more proximity to urban centres, proximity to transportation infrastructures, slope values and historic evolution in terms of growth rates and boom and boost phases. It mimics several types/forms of urban growth, including: organic growth, road-influenced growth, and diffusive growth. Because there is a loose coupling between SLEUTH and CVCA, and because there is a common backbone that supports the analyses, the same grid of 100 m  100 m cell size matrix, it becomes easy to integrate the dynamics of these two systems (once the required code adjustments are done). Urban growth analyses and land ecology planning analysis can thus become fully integrated in this CA modelling approach. It is also important to mention the use of Fragstats and several metrics presented in the bibliography, as a good starting point in order to describe the landscape patterns. While Fragstats was not coupled with SLEUTH or CVCA, it is important to build on existing research, and avoid re-inventing similar methodologies, so we can progress on other people’s research (more so when we can validate a set of metrics that has been successfully built elsewhere). As presented in Fig. 1 and detailed in section 4 of this paper, CVCA builds on SLEUTH and develops a set of countervailing strategies to the urban growth allocated by SLEUTH. This dynamic interaction is very useful. It allows urban growth generated by SLEUTH to be driven to other areas where it is possible to grow. CVCA begins by assessing the initial state of the landscape, generating several landscape metrics based on the initial state, and using these metrics to select and implement the appropriate landscape strategies. The flexibility of both SLEUTH and CVCA was further extended by allowing the model to enable or disable specific parts of it, functioning according to needs—in other words, it is possible to turn specific parts of the code on or off. In the case of SLEUTH, there is the capacity to turn off the land use change model (model name: Deltatron), or the possibility of running SLEUTH in a simulation mode from the past to the present; or as a scenario running to the future. In the case of CVCA, we compute the landscape ecological strategies, keeping the dynamic simulation with SLEUTH turned off; or turning it on in order to allow the interaction of the code of both CVCA and SLEUTH software.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Both CVCA and SLEUTH were built on a set of metrics that best describe the landscape—in the case of CVCA the landscape shape index (LSI), the mean patch size (MPS), and mean nearest neighbour distance (MNND), and in the case of SLEUTH the Roads, Slope, Bread and Spread. These metrics have multiple uses: they are a first assessment of the spatial characteristics of the matrix/landscape being studied; they also play an important function in locally defining each strategy in terms of urban growth or landscape ecology planning, and they are quantitative values that clearly represent a DNA of that specific reality. This DNA is the fingerprint of a system, that makes it unique, and that demonstrates the sensitivity of CA to the local condition; but it can also be ‘adapted’ if different policies are to be used in the future, and if different urban and environmental patterns and processes are to be promoted. The way these cells and behavioural roles interact in space is another relevant element, and a valuable attribute of CA that is explored extensively in this paper and in CVCA functioning. Supported by game theory, and with the help of powerful computation capacities, it is possible to see the dynamic interaction of cells competing for a specific use or for a specific strategy. As an example, we point to the behaviour of the offensive strategy in CVCA. When this strategy is activated, it allows the model to suggest special highintensity planning strategies to that cell (if the landscape status is to be enhanced in its patch size and connectivity). In order for that to happen, the model needs to record at least 50% of urban cells ‘attacking’ an existing patch of nature. In that case, the model will add an offensive ring of cells to the outer patch and will link through a corridor of ‘offensive’ cells to the closest nearest neighbour patch (these offensive cells are identified by a different colour and a letter A in the output image of the model). This could be seen in a more simplistic way as a kind of game, where probabilities play an important role in these ‘fights’ for a specific cell change. But we should see it also as a policy approach, as something that is being tested for possible application in the future, as a tool to promote specific strategies if one wants sustainable landscapes to be produced. As an example, we point to the fact that we can promote conservation strategies in order to protect patches of natural interest (ecological and agricultural reserve patches), enabling the creation of policies that will promote the maintenance of these patches (i.e. through the promotion of specific land uses in the surrounding land parcels) if we select a specific strategy to be promoted.

169

With the protective strategy, the goal is to take a step further, with an emphasis on conservation/environmental management of processes and patterns. In this case, it is considered that (even if there are no urban growth cells being simulated for the cells near a patch) if two patches are so close that the nearest neighbour distance is half of the mean nearest neighbour distance, the model builds corridors between these patches and an outer buffer around them. While we consider that there is much added value if these two action steps are implemented together, the value of having an input map to the SLEUTH—that can direct new urban growth to areas that do not impact on these nature patches of agricultural and ecological reserve, while at the same time diverting growth from these new outer buffers and corridors suggested by CVCA—is that it will direct growth to other good-togrow areas. If the CVCA strategies are to be used, this will increase the potential of using CVCA closecoupled with SLEUTH. Using the CVCA strategies through time will allow us to dynamically allocate new landscape strategies as a function of new urban pressures, allowing for a constant interaction of CVCA and SLEUTH, and resulting in an image that will be the interaction of both algorithms through time. Regarding the strategy in place, in terms of the hierarchies of predominance at CVCA among the different strategies, if a strategy were already placed in a cell and a new strategy were subsequently proposed, then the more ‘important’ cell would win. These degrees or hierarchies of importance are assigned in the following priority order: offensive, defensive, opportunistic, and protective. At a given moment, however, we can change this hierarchy or locally constrain the behaviour of the cells in order to emphasise specific strategies—these are opportunities opened up by modelling these complex systems using CA models. Nevertheless, there are many things that the authors would like to change. One of these, that is also being discussed in the international literature, is the question of retrofitting systems to original conditions. This was an issue discussed both with SLEUTH and with CVCA. With CVCA it implies at the moment that if a strategy establishes a corridor between two patches and there is an urban area functioning as a barrier to that corridor, the algorithm was created to ‘cut across’ and establish the corridor (in a kind of retrofit of that cell to an initial condition). The decision options are: contouring an object; not establishing the corridors; or partially establishing corridors. Because these options would all imply a set of other considerations and actions too demanding for this stage of development of the model,

170

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

they had to be set aside for future development. In the case of SLEUTH, these questions of retrofitting had to do, for instance, with old urban areas that tended to be abandoned (i.e. brownfield areas) and that can be converted to other kinds of urban areas, or to natural areas. The questions of retrofitting systems/landscapes to original conditions are one of the challenges of CA at this moment. Questions such as: does retrofit really exist in the landscape? If so, how do we accommodate these processes? If not, how to convert these cells to a kind of state that is para-similar to the initial condition? These questions need to be taken into account in the research and development of applications of dynamic models of space and time. These dynamics should be understood as taking place in a multidimensional universe. Each time step (i.e. each year) is a dynamic process, not only in terms of evolving through time, and with that evolution allowing different landscape situations/characteristics each year, but also in every year, throughout the space, in a multidimensional competition process, where the competition of strategies for each specific cell happens. Therefore each time step is more than a onedimensional time-step; it is a multidimensional step, resulting from dynamic games locally binding for each cell, and globally reorganising an entire metropolitan landscape. 7.3. Lack of planning tools and the benefits/ difficulties of coupling models Meanwhile, and probably as a result of the lack of integrated analysis and planning, there is a lack of tools available to conduct dynamic simulations of metropolitan land use change that integrate landscape. During the last five years, the literature has presented a multitude of maps that are static in scope (one year, one scale of analysis). These maps are like ‘snapshots’ of analysis that intend to reflect an analysed reality (and these maps tend to be descriptive—a visual tool that will increase the understanding of the reader), or a static scenario for a specific year in the future. The statistical packages of the GIS software are another factor contributing to the reduced development of integrated and dynamic applications of urban and environmental planning. GIS applications barely include more than the traditional mean/average/standard deviation statistics, as well as few statistical geoprocessing tools (clip, overlap, intersect, etc.). As a result, many planners explore the GIS capacities for producing maps, and these have tended to be maps that

describe/classify territory (planners tend to have more background on policy and socioeconomic analysis, and more need to support decision-makers in explaining the case studies under analysis). In contrast, many ecologists have tended to follow a different path, developing metrics and their own computer applications (these experts tend to have more mathematical and programming capacities than planners and this also enables them to follow this quantitative approaches path). Consequently, the goal of the land ecologists has been to develop biodiversity indices and to integrate them into computer-based ecological models. Nevertheless, once again these are mainly linear formulations that either do not include a spatial component, or when they do include it, do so in a static environment, with global models and rarely reflecting local characteristics. This paper has explored the difficulties and the action steps required in order to include the landscape ecological approach in a computational application. Some adjustments needed to be made, in order to translate Ahern’s landscape strategies into landscape metrics and afterwards to develop an appropriate code that the CVCA model could run. While it was possible to develop equivalent actions for all strategies, some adjustments had to be made—for instance, the definition of corridor and buffer width had to be equal to the dimension of one cell (100 m); the definition of minimum distance to establish a corridor was classified as being equal to the distance of the mean nearest neighbour distance metric (MNND). Finally, besides the definition of the four strategies defined by Ahern, it was important to include a fifth strategy, ‘let it grow’, in order to have SLEUTH urban cells allowing the dynamics of urban growth. In order to include the CA concepts, this integrated modelling approach also had to be adjusted to reflect CA requirements. CVCA has to be structured as a grid space, a neighbourhood effect, a set of transition rules, and a time step iteration. The landscape strategies therefore needed to be imported into a tridimensional space, that would allow us to perform incremental time steps and updates, on a year-by-year basis, of the landscape where the strategies were to be applied. On top of that, we imposed a set of constraints on the overall behaviour of the cells, thereby modifying the model to allow some action at a distance, rather than just the cell-by-cell local behaviours. This kind of approach tends to be different from that of ecologists, who rely on species representation and tend to use absolute values (and landscape ecologists also tend to prefer vector analysis for their studies). More recently, integrated or

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

hybrid approaches of CA and genetic algorithms have tended to answer to these particular requirements. New CA applications tend to be less rigid in their representation of space (i.e. containing irregular cell sizes and shapes), and individual modelling of objects in space by generic algorithms makes these very powerful applications when used together. The fact that CVCA and SLEUTH were built to allow maximum flexibility is also important, as this ensures the portability of the application to other case studies, and also a high flexibility in parameter change. An example of this flexibility is the development in CVCA of modules that can be turned on or off, allowing for the representation and assessment of the studied landscape, or that can turn their simulation mode on/off. The first outputted map of CVCA has information that can be used in two different ways:  feed SLEUTH an urban version that just indicates where urban growth has been permitted, but does not explicitly show where the landscape strategies were applied; or  display the results of applying the landscape strategies. Finally, the fact that these new applications of CVCA and SLEUTH are an open-ended code allows other users to adapt and change the code to reflect their needs, avoiding the ‘black box’ kind of model. Nevertheless, more interactive modelling model languages (MMLs) need to be offered for those who lack the programming capacities, but who might still do good and useful work in modelling these integrated dynamic systems. 7.4. Importance of integrated ecological and urban planning strategies The first part of this paper emphasised the need to include the environmental component in regional planning, and highlighted landscape ecology concepts as the appropriate basis for environmental planning in urbanising regions. Several reasons were pointed out, but one that in our view supports this argument strongly is the fact that nowadays the majority of the world’s landscapes have some kind of human intervention. Environmental planning that does not include human action will therefore tend to fail, and urban planning that does not accommodate or include environmental needs and processes will certainly put at risk human beings as living organisms and their societies. One of the major problems is the fact that, despite all the international discussions on the importance of

171

environmental questions and on the acknowledgement by each individual that nature plays a major role in their wellbeing, research results are still disappointing in terms of the integration of urban and environmental areas. The gap between the theoretical conceptions and practice of landscape ecology is evident; the development of modelling approaches and the actual implementation of integrated metropolitan planning tends to be neglected. While we can point to several reasons for this gap, it is important to highlight one of its probable root causes: an holistic approach requires a complex systems approach, in order to better understand the processes involved and to ensure that future urban forms are sustainable and environmentally benign; and the fact is that we are used to a kind of sectoral approach to the problems and solutions. We were taught this way, day-to-day life in the administrative sector tends to emphasise a sectoral approach, and the public and private sectors tend to create compartments of subject areas, instead of identifying problems or actions to be addressed and then trying to include different sectors in a coherent response. Regarding ecological analyses, these also tend to be too specific and too detailed, with ecology metrics that do a good job describing ecological processes, but rarely include land in their spatial component (i.e. data layers, containing land uses or other geographic information). The same can be said for planning, in particular urban and regional planning. These subject areas frequently include the study of land cover, land use and land use change, but rarely include landscape ecological concerns and metrics. Planning considerations therefore tend to be absent from ecological analysis and proposals, and planning proposals tend to lack any spatially explicit ecological metrics. In order to perform landscape ecological studies, several elements need to be considered: the study of components (e.g. number and type of spatial elements and species), the study of patterns (e.g. ecological relationships that help establish and sustain species), and the study of processes (e.g. ecological functions over time). All the above reasons underlie the importance of having landscape planning as a common term, to designate activities that integrate both natural-ecological needs and human-socioeconomic needs. Landscape planning would promote the sustainable use of physical, biological and cultural resources, by seeking the protection of unique and scarce resources, the avoidance of hazards, and the accommodation of development in appropriate locations. By proposing a landscape ecology model (CVCA) that can interact with an urban model (SLEUTH), in

172

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

order to propose a set of planning strategies that can direct urban growth to good-to-grow areas, this paper is contributing to the progress of research in and practice of landscape planning. Furthermore, by including in CVCA a set of landscape strategies (offensive, defensive, opportunistic and protective) that can support decision makers when establishing strategies that enhance the sustainability of landscapes, this model links research performed in CA and urban and environmental dynamics with its practical implementation. 8. Conclusion The importance of guiding urban growth, in order to accommodate future urban expansion in coordination with environmental concerns, makes it vital to develop these kinds of CA models. Landscape strategies supported by biodiversity metrics allow growth in some areas, while establishing more protective, defensive, opportunistic or offensive strategies in other areas. At the same time, they allow for an increase in the number of large patches of natural vegetation. The simulations also integrate greenway corridors and designate several areas to accommodate future urban growth. This approach attempts to be interdisciplinary and conducive to the integrated planning of the metropolitan areas under study. The first part of the paper described the theoretical considerations that support landscape ecology planning, and the way they were used in order to build the CVCA model. This was followed by the description of the basic units behind CVCA: the basic patterns the model tries to simulate, the processes associated to it, and how these are incorporated when planning the landscapes. The approach proposed by Ahern (1998) was used as a basis for the landscape ecological planning model. His framework method addresses landscape pattern-process at multiple scales, and includes a ‘human ecological component’. The proposed landscape strategies provide a model for integrated planning, and at the same time state the tone and context for evaluating the resulting integrated simulations from CVCA and SLEUTH Once theoretical considerations and the ‘mechanic’ of the model were detailed, it was possible to start describing the capabilities and opportunities of running CVCA and SLEUTH, in order to implement the landscape ecological strategies. First, both Metropolitan Areas’ landscape characteristics were described, to evaluate the accuracy of the compiled metrics, and to analyse the images resulting from the simulation. Then, an analysis of the metrics calculated by the model

was performed, looking at the model’s capacity to reproduce each Metropolitan Area’s local characteristics (regional DNA), and the model’s contribution to the different landscape strategies, particularly the spatial location of corridors and buffers. The evaluation of the resulting images obtained from running SLEUTH with CVCA was equally important, to understand the future urban and environmental patterns at the regional scale and to assess the resulting image of the Metropolitan Areas when these landscape ecology strategies are applied. The conclusion of this paper points to the fact that few models tend to include landscape components as a spatial attribute. This underlines the importance of the modelling approach described in this paper. It provides a more proactive modelling process that proposes different planning strategies and promotes the implementation of a set of landscape planning strategies and the development of CVCA. The fact that these strategies could vary with local conditions if developed in a cellular automaton-based modelling scheme affirms the value of cellular automata models. The fact that SLEUTH was fully operational and tested in several areas made it clear that there was no need to develop an urban model, and therefore a close coupling between SLEUTH and CVCA would complement the application of the landscape strategies. The extensive discussion presented at the last part of the paper allowed us to put in perspective further technical and methodological issues concerning SLEUTH and CVCA. We looked at the importance of coupling different models, the interpretation of the data resulting from applying the models to two Metropolitan Areas, and the importance of the research reported in this paper for the progress of the landscape planning techniques. We can therefore conclude by stating that the integrated approach used in the CVCA modelling process was judged to be successful. This integration can be seen at several levels, for instance at the level of incorporating principles and strategies of landscape ecology in urban methodologies. The results of running CVCA and SLEUTH, and the subsequent analysis, illustrate the applicability and validity of both models. The development of models that can build on existing models and establish close coupling to another model approaches the seamless integration that modelling professionals seek. Moreover, integrated studies that go beyond the false dichotomy of urban versus environment, while difficult to achieve because of different terminology, models and data requirements, are clearly important to pursue.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Acknowledgements This research was supported by two grants: from the Luso-American Foundation (FLAD) and from the Portuguese National Science Foundation (FCTPRAXIS XXI). We are also grateful to the United States Geographic Systems (USGIS), to the European Space Agency (ESA), to the Centre for Geographical Information and to the National Centre for Geographical Information and Analysis (NCGIA, Santa Barbara) for their support with data, funding and technical advice. References Adamatzky, A. (1994). Identification of cellular automata. London: Taylor & Francis. Ahern, J. (1996). Greenways as planning strategy. In J. Fabos & J. Ahern (Eds.), Greenways. The beginning of an international movement (pp. 131–155). New York: Elsevier. Ahern, J. (1997). At the crossroads: Sustainable future or urban sprawl concepts and scenarios for the Lisbon Metropolitan Area. In J. Machado & J. Ahern (Eds.), Environmental challenges in an expanding urban world and the role of emerging information technologies (pp. 13–26). Lisbon, Portugal: National Centre for Geographic Information Systems. Ahern, J. (1998). Spatial concepts, planning strategies and future scenarios: A framework method for integrating landscape ecology and landscape planning. In J. Kopatek & R. Gardner (Eds.), Landscape ecological analysis: Issues and applications (pp. 175–201). New York: Springer-Verlag Inc. Alexandridis, T., Takavakoglou, V., Crisman, T., & Zalidis, G. (2007). Remote sensing and GIS techniques for selecting a sustainable scenario for Lake Koronia, Greece. Environmental Assessment, 39(2), 278–290. Alonso, D., & Sole´, R. (2000). The DivGame simulator: A stochastic cellular automata model of rainforest dynamics. Ecological Modelling, 133, 131–141. Arii, K., & Parrott, L. (2006). Examining the colonization process of exotic species varying in competitive abilities using a cellular automaton model. Ecological Modelling, 199, 219–228. Ball, J. (2002). Towards a methodology for mapping ‘regions for sustainability’ using PPGIS. Progress in Planning, 58(2), 81–140. Balzter, H., Braun, P., & Kohler, W. (1998). Cellular automata models for vegetation dynamics. Ecological Modelling, 107, 113–125. Barredo, J., & Demicheli, L. (2003). Urban sustainability in developing countries’ megacities: Modelling and predicting future urban growth in Lagos. Cities, 20(5), 297–310. Batty, M. (1999). Modelling urban dynamics through GIS-based cellular automata. Computer Environment and Urban Systems, 23, 205–233. Batty, M., Xie, Y., & Zhanli, S. (1999). Modellng urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23, 205–233. Batty, M. (1996). Fractal cities. London: Academic Press. Batty, M. (2005). Cities and complexity. Cambridge, MA: The MIT Press. Benson, J., & Roe, M. (2000). Landscape and sustainability. London: Spon Press/Taylor and Francis.

173

Blaschke, T. (2006). The role of the spatial dimension within the framework of sustainable landscapes and natural capital. Landscape and Urban Planning, 75(3–4), 198–226. Bolliger, J. (2005). Simulating complex landscapes with a generic model: Sensitivity to qualitative and quantitative classifications. Ecological Complexity, 2(2), 131–149. Born, S., & Sonzogni, W. (1995). Integrated environmental-management—strengthening the conceptualization. Environmental Management, 19(2), 167–181. Brail, R., & Klosterman, R. (2001). Planning support systems: Integrating geographic information systems, models and visualization tools. Redlands, CA: Esri Press. Bu¨rgi, M., Hersperger, A., & Schneeberger, N. (2004). Driving forces of landscape change—current and new directions. Landscape Ecology, 19, 857–868. Burks, A. (1971). Essays on cellular automata. Champaign, IL: University of Illinois Press. Clarke, K. (1998). Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographic Information Science, 12(7), 699–714. Clarke, K. C., Gazulis, N., Dietzel, C. K. & Goldstein, N. C. (2007). A decade of SLEUTHing: Lessons learned from applications of a cellular automaton land use change model. Chapter 16 in Fisher, P. (Ed.) Classics from IJGIS. Twenty years of the International Journal of Geographical Information Systems and Science (pp.413–425). Boca Raton, FL: Taylor and Francis. Clarke, K., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–261. Clarke, K., Parks, B., & Crane, M. (2002). Geographic information systems and environmental modelling. Saddle River, NJ: PrenticeHall. Congleton, W., Pearce, B., & Beal, B. (1997). A C++ implementation of an individual/landscape model. Ecological Modelling, 103, 1–17. Dramstad, W., Olson, J., & Forman, R. (1996). Landscape ecology principles in landscape architecture and land-use planning. Washington, DC: Island Press. Dunos, C., & Watson, N. (2002). Integrated land and water management in the US: Narrowing the implementation GAP. Journal of Environmental Planning & Management, 45(3), 403–423. Fabos, J. (1985). Land use planning: From global to local challenge. New York: Chapman and Hall. Fabos, J., & Ahern, J. (1995). Greenways: the beginning of an international movement. New York: Elsevier Science. Fabos, J., & Gross, M. (1997). From watershed management to greenway planning. In J. Machado & J. Ahern (Eds.), Environmental challenges: An expanding urban world and the role of emerging information technologies. Lisbon, Portugal: National Centre for Geographic Information Systems. Flink, C., & Schwarz, L. (1993). Greenways: A guide to planning, design and development. Washington, DC: Island Press. Forman, R. (1995). Some general principles of landscape ecology and regional ecology. Landscape Ecology, 10(3), 133–142. Forman, R. (1997). Land mosaics. The ecology of landscapes and regions. Cambridge, UK: Cambridge University Press. Forman, R. T., & Godron, M. (1986). Landscape ecology. New York: John Wiley & Sons. Fre´on, P., Drapeau, L., David, J., Moreno, A., Leslie, R., Oosthuizen, R., et al. (2005). Spatialized ecosystem indicators in the southern Benguela. ICES Journal of Marine Science, 62(3), 459–468.

174

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Fricke, R., & Wolff, E. (2002). The MURBANDY Project: Development of land use and network databases for the Brussels area (Belgium) using remote sensing and aerial photography. International Journal of Applied Earth Observation and Geoinformation, 4(1), 33–50. Garcia, M. (2000). Rapid watershed planning handbook: A comprehensive guide for managing urbanizing watersheds by CtrWatershed-Protection. Journal of the American Planning Association, 66(3), 335–336. Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game ‘‘Life’’. Scientific American, 223, 120–123. Gardner, R., & Engelhardt, K. (2008). Spatial processes that maintain biodiversity in plant communities: Perspectives in plant ecology. Evolution and Systematics, 9, 211–228. Gazulis, N., & Clarke, K. C. (2006). Exploring the DNA of our regions: Classification of outputs from the SLEUTH model. In S. El Yacoubi, B. Chapard, & S. Bandini (Eds.), Cellular automata: Lecture notes in computer science. New York: Springer. Geneletti, D. (2008). Incorporating biodiversity assets in spatial planning: Methodological proposal and development of a planning support system. Landscape and Urban Planning, 84(3–4), 252– 265. Giles, R., & Trani, M. (1999). Key elements of landscape pattern measures. Environmental Management, 23(4), 477–481. Green, D., & Sadedin, S. (2005). Interactions matter—Complexity in landscapes and ecosystems. Ecological Complexity, 2(2), 117– 130. Grilli, P., & Bruno, M. (2007). Regional abundance of a planthopper pest: The effect of host patch area and configuration. Entomologia Experimentalis et Applicata, 122(2), 133. Grimm, V., Wyszomirsk, T., Aikman, D., & Uchmanski, J. (1999). Individual based modelling and ecological theory: Synthesis of a workshop. Ecological Modelling, 115, 275–282. Gustafson, E. (1998). Quantifying landscape spatial pattern: What is the state of the art? Ecosystems, 1, 143–156. Hagen-Zanker, A. (2006). Map comparison methods that simultaneously address overlap and structure. Journal of Geographic Systems, 8(2), 165–185. Hargis, C., Bissonette, J., & David, J. (1998). The behaviour of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecology, 13, 167–189. Hargrove, W., Hoffman, F., & Efroymson, R. (2004). A practical mapanalysis tool for detecting potential dispersal corridors. Landscape Ecology, 20, 361–373. Hersperger, A. M. (1994). Landscape ecology and its potential application to planning. Journal of the Planning Literature, 9(1), 14–29. Holland, J. (1995). Hidden order: How adaptation builds complexity. Reading, MA: Helix Books. Holland, J. (1999). Emergence: From chaos to order. Reading, MA: Helix Books. Jai, A., Fournier, M., Cabrera, O., & Maurissen, Y. (1999). A prediction correction alternative for modelling spatiotemporal systems. Application to landscape modelling. Ecological Modelling, 116, 237–251. Kay, J., Regier, H., Boyle, M., & Francis, G. (1999). An ecosystem approach for sustainability: Addressing the challenge of complexity. Futures, 31, 721–742. Khanna, P., Ram-Babu, P., & George, M. (1999). Carrying-capacity as a basis for sustainable development: A case study of National Capital Region in India. Progress in Planning, 52(2), 101–166.

King, P., Annandale, D., & Bailey, J. (2003). Integrated economic and environmental planning in Asia: A review of progress and proposals for policy reform. Progress in Planning, 59(4), 233–315. Klosterman, R. (1997). Planning support systems: A new perspective on computer-aided planning. Journal of Planning Education and Research, 17, 45–54. Kocabas, V., & Dragicevic, S. (2007). Enhancing a GIS cellular automata model of land use change: Bayesian networks, influence diagrams and causality. Transactions in GIS, 11, 681–702 (22). Kok, J., Engelen, G., White, R., & Wind, H. (2001). Modelling landuse change in decision-support system for coastal-zone management. Environmental Modelling and Assessment, 6, 123–132. Kong, F., & Nakagoshi, N. (2006). Spatial-temporal gradient analysis of urban green spaces in Jinan, China. Landscape Urban Planning, 78(3), 147–164. Kraft, S., & Penberthy, J. (2000). Conservation policy for the future: What lessons have we learned from watershed planning and research. Journal of Soil and Water Conservation, 55(3), 327–333. Kumar, S., Stohlgren, T., & Chong, G. (2006). Spatial heterogeneity influences native and non-native plant species richness. Ecology, 87(12), 3186–3199. Laird, A., & Schamp, B. (2006). Competitive intransitivity promotes species coexistence. The American Naturalist, 168(2), 182–193. Lau, K. H., & Kam, B. H. (2005). A cellular automata model for urban land-use simulation. Environment and Planning B: Planning and Design, 32(2), 247–263. Leitao, A., & Ahern, J. (2002). Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning, 59(2), 65–93. Leitao, A. B., Miller, J., Ahern, J., & McGarigal, K. (2006). Measuring landscapes: A planner’s handbook. Washington, DC: Island Press. Lenz, R., Malkina-Phkh, I., & Pykh, Y. (2000). Introduction and overview. Ecological Modelling, 130, 1–11. Ligmann-Zielinska, A., Church, R. L., & Jankowski, P. (2008). Spatial optimization as a generative technique for sustainable multiobjective land-use allocation. International Journal of Geographic Information Science, 22(6), 601–622. Ligtenberg, A., Bregt, A., & van Lammeren, R. (2001). Multi-actorbased land use modeling: Spatial planning using agents. Landscape and Urban Planning, 56, 21–33. Little, C. (1990). Greenways for America. Baltimore, MD: Johns Hopkins. Lorek, H., & Sonnenschein, M. (1999). Modelling and simulation software to support individual-based ecological modelling. Ecological Modelling, 115, 199–216. Luck, M., & Wu, J. (2002). A radient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology, 17(4), 327–339. Machado, J., Saraiva, M., Silva, E., Rocha, J., Ferreira, J., Morgado, P., et al. (1997). Greenways network for the Metropolitan Area of Lisbon. In J. R. Machado & J. Ahern (Eds.), Environmental challenges in an expanding urban world and the role of emerging information technologies (pp. 281–289). Lisbon, Portugal: National Centre for Geographic Information Systems. Maclean, I., Hassall, M., Boar, M., & Lake, I. (2006). Effects of disturbance and habitat loss on papyrus-dwelling passerines. Biological Conservation, 131(3), 349–358. Malkina-Pykh, I. (2000). From data and theory to environmental models and indices formation. Ecological Modelling, 130, 67–77. Marull, J., Pino, J., Mallarach, J., & Crodobilla, M. (2007). A land suitability index for strategic environmental assessment in metropolitan areas. Landscape and Urban Planning, 81(3), 200–212.

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177 Masri, A., & Moore, J. (1993). Integrated planning information systems: Context, design requirements, and prospects. Computers, Environment and Urban Systems, 17(6), 491–511. Matsushita, B., Xu, M., & Fukushima, T. (2006). Characterizing the changes in landscape structure in the Lake Kasumigaura Basin, Japan using a high-quality GIS dataset. Landscape Urban Planning, 78(3), 241–250. McGarigal, K. & Marks, B. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure (USDA Forest Service General Technical Report PNW-351). Washington, DC: USDA. Messina, J., & Walsh, S. (2005). Dynamic spatial simulation modelling of the population—environment matrix in the Ecuadorian Amazon. Environment and Planning B: Planning and Design, 32(6), 835–856. Molofsky, J., & Bever, J. (2004). A new kind of ecology? Bioscience, 54(5), 440–446. Nassauer, J., & Corry, R. (2004). Using normative scenarios in landscape ecology. Landscape Ecology, 19, 343–356. Naveh, Z. (2001). Ten major premises for a holistic conception of multifunctional landscapes. Landscape and Urban Planning, 57(3–4), 269–284. Ndubisi, F. (1997). Landscape ecological planning. In G. F. Thompson & F. R. Steiner (Eds.), Ecological design and planning (pp. 9–44). New York: John Wiley and Sons. O’Neill, R., Hunsaker, C., Jones, K., Riitters, K., Wickham, J., Schwartz, P., et al. (1997). Monitoring environmental quality at the landscape scale. BioScience, 47(8), 513–520. Openshaw, S., & Openshaw, C. (1997). Artificial intelligence in geography. Chichester, UK: Wiley. Palmer, J. (2004). Using spatial metrics to predict scenic perception in a changing landscape: Dennis, Massachusetts. Landscape Urban Planning, 69(2–3), 201–218. Pettit, J. (2005). Use of a collaborative GIS-based planning-support system to assist in formulating a sustainable-development scenario for Hervey Bay, Australia. Environment and Planning B: Planning and Design, 32(4), 523–545. Prigogine, I., George, C., Henin, F., & Rosenfeld, L. (1973). A unified formulation of dynamics and thermodynamics. Chemica Scripta, 4, 5–32. Prigogine, I., Lefever, R., Goldbeter, A., & Herschkowitz-Kaufman, M. (1969). Symmetry breaking instabilities in biological systems. Nature, 223(5209), 913–916. Quon, S., Martin, L., & Murphy, S. (2001). Effective planning and implementation of ecological rehabilitation projects: A case study of the regional municipality of Waterloo (Ontario, Canada). Environmental Management, 27(3), 421–433. Raj, P. (1995). Multicriteria methods in river basin planning, A casestudy. Water Science and Technology, 31(8), 261–272. Risser, P. (1987). Landscape ecology: State of the art. In M. Turner (Ed.), Landscape heterogeneity & disturbance (pp. 3–14). New York: Springer-Verlag. Rohde, K. (2005). Cellular automata and ecology. OIKOS, 110(1), 203–207. Rohde, S., Hostmann, M., Peter, A., & Ewald, C. (2006). Room for rivers: An integrative search strategy for floodplain restoration. Landscape and Urban Planning, 78(1–2), 50–70. Romero-Calcerrada, R., & Luque, S. (2006). Habitat quality assessment using weights-of-evidence based GIS modelling: The case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest. Ecological Modelling, 196(1–2), 62–76.

175

Roseland, M. (2000). Sustainable community development: Integrating environmental, economic and social objectives. Progress in Planning, 54(2), 73–132. Silva, E. A., & Ahern, J. (1999). Biodiversity models for landscape planning. Fifth World Congress of IALE – International Association for Landscape Ecology, 29 July–3 August. Snowmass, CO. Silva, E. A., & Clarke, K. (2005). Complexity, emergence and cellular urban models: Lessons learned from applying SLEUTH to two Portuguese cities. European Planning Studies, 13(1), 93–115. Silva, E. A., & Clarke, K. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, environment and urban systems computers. Environment and Urban Systems, 26(6), 525–552. Silva, E. A. (in press). ‘Complexity and CA, and application to metropolitan areas’. In: G. de Roo, & E. A. Silva, A planner’s meeting with complexity. Aldershot, UK: Ashgate Publishers Ltd. Silva, E. A. (2000). Managing metropolitan areas as multifunctional entities, In: J. Brandt, Tress, B. & Tress G., Multifunctional landscapes. Interdisciplinary approaches to landscape research and management, 18–21 October, Roskilde (Denmark), supplement in book of proceedings, page 5. Silva, E. A. (1999). Os efeitos estruturantes das vias de comunicac¸a˜o na transformac¸a˜o dos usos do solo. Observac¸a˜o e estudo da A´rea Metropolitana de Lisboa [‘The structuring effects of transportation infrastructures in land use change: Observation and study of the Lisbon Metropolitan Area’]. Lisbon, Portugal: Patrimo´nia. Silva, E.A. (2003). Flocking urban growth with an environmental agenda: Landscape ecological strategies in urban growth using two cellular automation models. IFHP international spring conference: Enhancing urban quality—the green dimension, London, 31 May–3 June. Abstract in conference publications report (no page number assigned). Silva, E. A. (2004). The DNA of our regions: Artificial intelligence in regional planning. Futures, 36(10), 1077–1094. Silva, E.A. (2002). Beyond modelling in environmental and urban planning. PhD dissertation. Amherst, MA: University of Massachusetts. Silva, E. A., Ahern, J. & Wileden, J. (2003). The CVCA model: Loose coupling of landscape ecological strategies with a cellular automata model of urban growth. In: A. Franks, D. Pearson & K. Wagar, Crossing frontiers: Landscape ecology down under (abstracts, p.116); IALE 2003-6th International Association of Landscape Ecology World Congress, Darwin (Australia), 13–17 July. Silvertown, J., Holtier, S., Johnson, J., & Dale, P. (1992). Cellular automaton models of interspecific competition for space—the effect of pattern on process. Journal of Ecology, 80, 527–534. Sirakoulis, G., Karafyllidis, I., & Thanailakis, A. (2000). A cellular automaton model for the effects of population movement and vaccination on epidemic propagation. Ecological Modelling, 133, 223. Smith, D., & Hellmund, P. (1993). Ecology of greenways. Design and function of linear conservation areas. Minneapolis, MN: University of Minnesota Press. Soncini-Sessa, R., Castelletti, A., & Webster, E. (2003). A DSS for planning and managing water reservoir systems. Environmental Modeling and Software, 18(5), 395–400. Sorensen, A. (2000). Land readjustment and metropolitan growth: An examination of suburban land development and urban sprawl in the Tokyo metropolitan area. Progress in Planning, 53(4), 217–330.

176

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

Sorrell, J. (1997). Using geographic information systems to evaluate forest fragmentation and identify wildlife corridor opportunities in the Cataraqui Watershed. Online paper. Ontario, Canada: Faculty of Environmental Studies, York University. Steiner, F. (1999). The living landscape. An ecological approach to landscape planning (2nd ed.). New York: McGrawHill. Steinitz, C. (1990). A framework for theory applicable to the education of landscape architects (and other environmental design professionals). Landscape Journal, 9(2), 136–143. Stevens, D., & Dragic´evic´, S. (2007). A GIS-based irregular cellular automata model of land-use change. Environment and Planning B: Planning and Design, 34(4), 708–724. Swaffield, S., & Primdahl, J. (2004). Spatial concepts in landscape analysis and policy: Some implications of globalization. Landscape Ecology, 20, 657–673. Thompson-Fawcett, M., & Bond, S. (2003). Urbanist intentions for the built landscape: Examples of concept and practice in England, Canada and New Zealand. Progress in Planning, 60(2), 147–234. Tippett, J., Handley, J., & Ravetz, J. (2007). Meeting the challenges of sustainable development: A conceptual appraisal of a new methodology for participatory ecological planning. Progress in Planning, 67(1), 9–98. Tobler, W. (1979). Cellular geography. In S. Gale & G. Olosson (Eds.), Philosophy and geography (pp. 279–386). Dodrecht: D. Reidel. Toffoli, T. (1998). Cellular automata as an alternative to (rather than an approximation of) differential equations. Physica D, 10, 117–127. Toth, F., & Hizsnyik, E. (1998). Integrated environmental assessment methods: Evolution and application. Environmental Modelling and Assessment, 3, 193–207. Turner, M. (1987). Landscape heterogeneity and disturbance. New York: Springer-Verlag. Turner, M., & Gardner, R. (1991). Quantitative methods in landscape ecology. New York: Springer. Uuemaa, E., Roosaare, J., & Mander, U. (2005). Scale dependence of landscape metrics and their indicatory value for nutrient and organic matter losses from catchments. Ecological Indicators, 5(4), 350–369. Ulam, S. (1961). Monte Carlo calculations in problems of mathematical physics. Modern mathematics for the engineer: Second series, New York: McGraw-Hill. pp. 261–281.

Van der Veen, A., & Rotmans, J. (2001). Dutch perspectives on agents, regions and land use change. Environmental Modelling and Assessment., 6, 83–86. Van Mansvelt, J., & Van der Lubbe, M. (1999). Checklist for sustainable landscape management. Amsterdam: Elsevier. von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press. Webster, C., & Wu, F. (1999). Regulation, land use mix, and urban performance. Part 2. Simulation. Environment and Planning A, 31(9), 1529–1545. Wei, Y., & Hoganson, H. (2005). Landscape impacts from valuing core area in national forest planning. Forest Ecology and Management, 218(1–3), 89–106. White, R., Engelen, G., & Uijee, I. (1997). The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B, 24, 323–343. White, S. S., & Ellis, C. (2007). Sustainability, the environment, and new urbanism: An assessment and agenda for research. Journal of Architectural and Planning Research, 24(2), 125–142. Wintle, B., Elith, J., & Potts, J. (2005). Fauna habitat modelling and mapping: A case study in the Lower Hunter Central Coast region of NSW. Australian Ecology, 30(7), 719–738. Wileden, J., Silva, E. A., & Ahern, J. (2003). The CVCA model: A cellular automaton model of landscape ecological strategies. European Simulation and Modelling Society, ESM 2003, 206–209. Williams, G. (1999). Metropolitan governance and strategic planning: A review of experience in Manchester, Melbourne and Toronto. Progress in Planning, 52(1), 1–100. Wood, A. J., Ackland, G. J., & Lenton, T. M. (2006). Mutation of albedo and growth response produces oscillations in a spatial Daisyworld. Journal of Theoretical Biology, 242(1, 7), 188–198. Wu, F., & Webster, C. (2000). Simulating artificial cities in a GIS environment: Urban growth under alternative regulation regimes. International Journal of Geographical Science, 14(7), 625–648. Wyatt, R. (1999). Computer-aided policymaking: Lessons from strategic planning software. London: E & FN Spon. Yang, X., & Lo, C. P. (2003). Modeling urban growth and landscape change for Atlanta metropolitan region. International Journal of Geographical Information Science, 17, 463–488. Zeiler, M. (1999). Modelling our world. Redlands, CA: ESRI Press.

Dr Elisabete A. Silva is a University Lecturer in Planning in the Department of Land Economy and Fellow of Robinson College, at the University of Cambridge, where she teaches undergraduate and graduate modules in Planning. Dr Silva holds a PhD in regional planning (University of Massachusetts, USA), and has a research track record of approximately 15 years, both in the public and private sector. Her research interests are centred on the application of new technologies to spatial planning, and in particular, city and metropolitan dynamic modelling through time; land use, transportation and metropolitan planning; regional planning, integrated planning (urban/transportation/environmental planning); geographic information systems and planning support systems, AI models (particularly CA models). John F. ‘Jack’ Ahern, is a professor in the Department of Landscape Architecture & Regional Planning at the University of Massachusetts—Amherst, MA. Jack Ahern (BA (Massachusetts), MA (Pennsylvania, 1980) PhD (Wageningen University, Netherlands 2002)) is a registered landscape architect (MA) and Fellow of the American Society of Landscape Architects. He has received numerous awards for his work in applied landscape ecology and greenways, including a Fulbright Research and Teaching Fellowship to Portugal, Honour Awards from the American Society of Landscape Architects and Boston Society of Landscape Architects for research into greenways and for his book, A Guide to the Landscape and Architecture of Boston, and a Places Research Award from the Environmental Design Research Association for a study of the landscape character of the Cape Cod national Seashore. His research

E.A. Silva et al. / Progress in Planning 70 (2008) 133–177

177

focuses on the integration and application of landscape ecology in landscape planning and design, with an emphasis on green urban resources—greenways, and green infrastructure—and landscape urbanism at multiple scales. His books include: Measuring landscapes: A planner’s handbook (Washington, DC: Island Press, 2006) (co-author); Biodiversity planning and design: Sustainable practices (Washington, DC: Island Press, 2006) (lead co-author); Greenways as strategic landscape planning: Theory and application (Wageningen University, Netherlands, 2002); A guide to the landscape architecture of Boston (Cambridge, MA: Hubbard Educational Trust, 1999); and Greenways: The beginning of an international movement (New York, Elsevier Science, 1996) (co-author with Julian Fabos). Jack C. Wileden received the AB degree in mathematics and the MS and PhD degrees in computer and communications sciences from the University of Michigan in 1972, 1973 and 1978, respectively. He has been on the faculty of the University of Massachusetts at Amherst since 1978, and is currently a Professor in the Department of Computer Science, and Director of the Convergent Computing Systems Laboratory (CCSL). Professor Wileden’s research interests are programming languages and interoperability. His work is primarily directed towards producing tools, techniques and formal foundations to support the development and evolution of maximally seamless systems comprising interoperating components.