Modelling land-use effects of future urbanization using cellular automata: An Eastern Danish case

Environmental Modelling & Software 50 (2013) 1e11

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Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft

Modelling land-use effects of future urbanization using cellular automata: An Eastern Danish case Morten Fuglsang a, b, *, Bernd Münier a, Henning Sten Hansen b a b

Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark Aalborg University Copenhagen, A.C. Meyers Vænge 15, 2450 Copenhagen, Denmark

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 October 2012 Received in revised form 13 August 2013 Accepted 16 August 2013 Available online 20 September 2013

The modelling of land use change is a way to analyse future scenarios by modelling different pathways. Application of spatial data of different scales coupled with socio-economic data makes it possible to explore and test the understanding of land use change relations. In the EU-FP7 research project PASHMINA (Paradigm Shift modelling and innovative approaches), three storylines of future transportation paradigm shifts towards 2040 are created. These storylines are translated into spatial planning strategies and modelled using the cellular automata model LUCIA. For the modelling, an Eastern Danish case area was selected, comprising of the Copenhagen metropolitan area and its hinterland. The different scenarios are described using a range of different descriptive GIS datasets. These include mapping of accessibility based on public and private transportation, urban density and structure, and distribution of jobs and population. These indicators are then incorporated in the model calculations as factors determining urban development, related to the scenario outlines. The results calculated from the scenarios reveals the great difference in urban distribution that different spatial planning strategies can produce, changing the shape of the urban landscape. The scenarios visualized showed to outline different planning strategies that could be implemented, creating a more homogenous urban structure targeted at a reduction of transportation work and thus energy consumption. This will lead to less impact on climate from transportation based on a more optimal localization and transport infrastructure strategy. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Urban modelling Scenario interpretations Transportation paradigm shift Cellular automata GIS

1. Introduction Establishing methods for assessing future consequences of spatial plans and policies is critically important for urban and regional planners (Al-Ahmadi et al., 2009). Urban growth is a subject that has received great attention in recent decades, because it is constantly reshaping the landscape. These urban changes should be managed to ensure both environmental and social sustainability for the ever-growing urban population of the world (Engelen et al., 2007), which by 2050 is expected to account for 60% of the world population according to UN predictions (ESA, 2007). CA (cellular automata) models are well-proven methods of predicting development. Using these models the future outcome of a suggested planning scenario can be analysed. The scope of the

* Corresponding author. Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark. E-mail addresses: [email protected], [email protected] (M. Fuglsang), bem@ dmu.dk (B. Münier), [email protected] (H.S. Hansen). 1364-8152/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envsoft.2013.08.003

conducted analysis can vary from nature conservation as presented by Mitsova et al. (2011), forest monitoring (Ménard and Marceau, 2007) and cultivation pattern monitoring by Wickramasuriya et al. (2009) e to planning specific scenario analysis as conducted in this paper. All applications however treat elements of the same societal challenge, the land-use area competition created by the increasing urbanization. The different applications of the CA technique are based on the knowledge of the drivers of land use change, which according to Dendoncker et al. (2007) often are classified as: 1. 2. 3. 4. 5.

Biophysical constraints and potentials Economic factors Social factors Spatial policies Spatial interaction and neighbourhood (Dendoncker et al., 2007)

characteristics

Based on spatial data of different scales coupled with relevant socio-economic data, the models estimate the future land use demand based on an analysis of the drivers of land use change. The

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most common factors incorporated as drivers into CA models are according to Santé et al. (2011), among others, accessibility and distance to roads, distance to urban centres, population density and urban zoning (Santé et al., 2011). All locations are evaluated, and based on the factors incorporated in the model the most feasible areas are predicted to become urban areas. Thereby it is possible to explore and analyse the impact of planning on land use change relations by modelling the often complex local and regional relations in the land use system. CA models have been increasingly used to simulate such complex urban systems. Empirical data can be used to calibrate the CA models so that correct urban patterns can be generated (Liu et al., 2008). These models are capable of measuring the sensitivity of key variables in terms of the land change patterns that they may influence, allowing the model to be configured and calibrated precisely (Veldkamp and Lambin, 2001). Furthermore, the models have proliferated because of their simplicity, flexibility and intuitiveness, and particularly because of their ability to incorporate the spatial and temporal dimensions of the processes (Santé et al., 2011). The goal of the paper is to present an interpretation methodology for the PASHMINA qualitative scenarios, by adapting them to local planning context and visualizing them through the LUCIA e Land Use Change Impact Assessment e model (Hansen, 2007). The work carried out in this paper is based on previous work with CA modelling of long-term scenarios as by de Njis et al., (2004), Reginster and Rounsevell (2006) and Rounsevell et al. (2006). The work done by (Rounsevell et al., 2006) describes an approach that is similar to the analysis conducted in the current paper, where the analysis is based on a series of scenario interpretations of the Special Report on Emissions Scenarios e SRES (Nakicenovic and Swart, 2000). Our work is based on scenarios of global economy, and transportation paradigms under specific global development trends from the PASHMINA EU-FP7 research project. The objectives under PASHMINA included production of exploratory scenarios (Qualitative storylines) of future global change options up to 2030 and 2050, coupled with analysis of the consequences of the scenarios on the energy-transportenvironment nexus related to the urban functions: housing, mobility and recreation. Therefore the work with the interpretation of the PASHMINA scenarios illustrates an approach where the economic scenarios are translated into planning. We have focused our work with the scenarios to conform to practices regarding scenario-based work described by, among others, Shearer (2005) and Xiang and Clarke (2003). The analysis can be used as input for planners, extending knowledge gained from planning support frameworks such as the work by Forgie (2011) and work on general knowledge about incorporating spatial tools in planning, e.g. by Oxley et al. (2004), by incorporating advanced spatial modelling in the planning process. The visualisations of the scenarios created will demonstrate the impact of the core scenario ideas, as they would evolve when subjected to actual planning practice. This will make it possible to evaluate the scenario consequences on land use and to analyse them in regional context, providing valuable insight for the planners. 2. Methods

Based on the narrative and scenarios from the PASHMINA project, key elements regarding land use planning and transportation focus were derived. The implementation follows the methodology suggested by Rounsevell et al. (2006): a. Qualitative descriptions of roles of land use drivers from the PASHMINA scenarios b. Quantitative assessment of relevant drivers. c. Spatial allocation rules specific to each scenario used to model the geographic space of the case region (Rounsevell et al., 2006). A more detailed description of the implementation of the three steps will be conducted in the following paragraphs. The model was calibrated using available observed data from 1990 until 2002, and the modelling was done from 1990 until 2040 for all scenarios. The data used for the modelling and calibration is a product combining the EEA CORINE mapping products (EEA-ETC/TE, 2002) with the detailed Danish Building and Housing Register, to produce year by year development maps, based on the CORINE classification. To illustrate the Danish ‘business as usual’ case in relation to land use change, a scenario with drivers from current planning practice was selected. This scenario was calibrated against the 12-year observation period. Furthermore a ‘Growth within limits’ scenario with focus on transportation via public transportation was modelled, as well as a ‘ New welfare’ scenario where work related transportation becomes less important, were the two other key scenarios modelled in the analysis. In order to produce significant results and to highlight the difference between the planning approaches, the focus on enforcing the narrative directives had a high priority in the creation of the scenario results. 2.2. Urban CA modelling of scenarios Many different approaches of modelling the urban landscape and its development have been proposed to gain better understanding of the drivers and consequences of urban growth predictions. These approaches comprise of a series of different techniques, to predict land-use-changes. These approaches include amongst others agent based methods as implemented in work conducted by Filantova et al. (2013), Murray-Rust et al. (2013), Gaube et al. (2013) and Ralha et al. (2013), neural network modelling as demonstrated by Grekousis et al. (2013) and Tayyebi et al. (2011), and cellular automata modelling as presented in this paper. The CA based methods are one of the common approaches used for urban prediction (Al-Ahmadi et al., 2009). The core methodology behind the CA models is based on a grid of cells, for which the model accounts for the state of the cells. The models apply transition rules to determine the potential of state change for each cell, based on a series of factors and weights on each cell (Santé et al., 2011). The CA methods were originally developed in the 1940s by Ulam (1976) and the methodology was soon applied to physics, natural and mathematical science. The first application of CA modelling in geography was conducted by Tobler (1979), and during the 1980s the applications of geographical CA modelling emerged (Batty and Xie, 1994). With the increase in computational power and capabilities, the first actual simulation of case specific urban development was made during the 1990s, and based on the CA technique several urban models have been proposed, to gain insight into the processes and consequences of urban evolution (Couclelis, 1997). To describe the future using the CA modelling technique, a range of different scenarios describing the future is utilized. By creating detailed descriptions of alternative pathways that planning policies can develop through, it is possible to analyse the effects and magnitude of the land use development (Koomen et al., 2008). The construction of the scenarios determines how the predictions of the future evolve. It is not likely that the scenarios will be able to predict the most likely prospect of the future; they will more likely present a range of different possibilities of potential change. Embedded in the scenarios, there will always be a large range of potential uncertainties. These can be of general nature relating to the socioeconomic predictions that drive the scenarios, as well as from the data used for the modelling (Dendoncker et al., 2008). Building the scenarios while being aware of the implied uncertainties increases the understanding of the effects of planning on land use, and may become an important aid in the decision making process (Hansen, 2008). The usage of scenarios in the planning process contributes in several ways:
Planners can envision a set of hypothetical development strategies, matching a certain goal or strategy
Planners can incorporate explicit assumptions to construct specialized alternatives
Search for ways to achieve certain goals, thereby be able to inform policy and decision makers (Song et al., 2006).

2.1. Overview of approach For the modelling of land use change interactions, a Danish case area was selected, comprising of the Copenhagen metropolitan area including Zealand and the other Eastern Danish islands. Within the case area, the LUCIA land use change model was applied to conduct the modelling of the scenarios of the PASHMINA project regarding transportation paradigm shifts.

Many examples of applied CA modelling exist and are extensively described in literature. Major models, which have been successfully applied to European data, include the SLEUTH and the MOLAND models: 1. Within the framework of the Joint Research Centre’s mission to provide scientific and technical support for the definition and implementation of EU

M. Fuglsang et al. / Environmental Modelling & Software 50 (2013) 1e11 policies, the MOLAND urban and regional scenario simulation model has been created. The MOLAND model is an extension of work conducted by White and Engelen (1997), and a description of its computational background and scientific approach is described by Barredo et al. (2004). The recent contributions to the field of applied scenario based CA modelling with the MOLAND model approach include work conducted by Petrov et al. (2009): modelling development in tourism in the Algarve region of Portugal, Cabello et al. conducted modelling in the IMPRINTS project in France/Spain, and Haase (2010) within the PLUREL project carrying out participatory modelling for the Leipzig/Halle region. 2. The SLEUTH model (slope, land use, exclusion, urban extent, transportation and hill shade), Urban Growth Model, was developed by Dr. Keith C. Clarke at UCSanta Barbara (Clarke et al., 1997). Recent contributions applying the SLEUTH model include work by Jantz et al. (2010) conducting 30-m resolution modelling in the Chesapeake Bay drainage basin in the eastern United States, and by modelling the Dongguan central urban area of the ‘business as usual’ River Delta, China (Feng et al., 2012). In Europe, the SLEUTH model has been applied to the Lisbon area by Silva and Clarke (2002). Recent contributions using the SLEUTH model include Onsted and Clarke (2011) in the United States, and Heinsch et al. (2012) conducting land use change modelling in Germany.

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Other recent contributions include assessments of the impact of climate change on urban development conducted by Hansen (2010), the future urban development in Europe by Reginster and Rounsevell (2006), incorporation of household decision making in the CA process by Lauf et al. (2012), and urban expansion scenarios for Beijing by He et al. (2006).

2.3. The LUCIA model The CA model applied for the modelling of the PASHMINA scenarios, is the Land Use Change Impact Analysis model (LUCIA) developed by Hansen (2007 & 2010) It is not within the scope of this paper to give a detailed technical description of the functionality and calculation abilities by the LUCIA model, so only a brief introduction to the model will be given here. Further description of the model is given in the work conducted by Hansen (2007, 2010). The LUCIA model uses representations of data in grid format, assigning land use change potential of cells as the base for determining land use changes. The transition potential of cells in LUCIA is determined by neighbourhood and transition rules. The neighbourhood rules that define the distance relationships between land use classes in LUCIA, are derived by applying spatial metrics to the observed land use data,

Fig. 1. Case region and centre distribution.

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M. Fuglsang et al. / Environmental Modelling & Software 50 (2013) 1e11 1980 till 2040 can be seen (DST, 2002). For the modelling each municipality is treated individually. Fig. 2 shows the general population development aggregated for the entire region. Data for the case region was analysed to determine the main drivers of land use change in relation to Dendoncker et al. (2007). In the period from 1990 to 2002, the observed changes were analysed, regarding their spatial dependencies to key factors. The factors analysed was both statistical and socio-economic parameters, spatial and ecological constraints in the landscape and spatial relationships between different land-use categories. In Fig. 3, the relationship between distance to centres and main roads can be seen. It appears, that there is a strong spatial relationship between these variables and land-use change. For each of the potential drivers of land-use change in the case region, an analysis of spatial distribution and statistical distribution was conducted in relation to the second step of Rounsevell et al. (2006), where the knowledge gained about drivers will be the main source of knowledge for the analysis. 2.5. The PASHMINA scenarios

Fig. 2. Population development in case region from 1980 to 2040.

making the rules based on existing spatial structures and patterns (Hansen, 2010). In the classification of transition rules proposed by Santé (2011), the LUCIA model utilises ‘Type II’ transition rules, which states that the key driver of urban evolution is the transition potential. This means that the probability of each cell changing to a specific land use is a function of the current land use of the cell and its neighbours, and of other factors that constrain land-use evolution. The reason for applying the LUCIA model in this study was, that the LUCIA model previously had been configured on Danish data, whereas the SLEUTH and MOLAND models have not been used in Denmark before. Furthermore, data availability and time constraints of the project made it more feasible to select the LUCIA model. The scope of the work done with the LUCIA model is less focused on the descriptive elements of the CA technique; however, a validation of the baseline ‘business as usual’ scenario has been included. Focus is more on the predictive tasks, where it is the land use changes as consequence of the scenarios in the future that is the main results of the study. 2.4. Land use dynamics in case region For the modelling of land-use consequences of the PASHMINA scenarios, a Danish case area was selected, comprising of the Copenhagen metropolitan area, which includes Zealand and other Eastern Danish islands. The case study covers an area of approximately 9200 km2, with a population of about 2.5 million individuals, or nearly half of the total Danish population. Located at the eastern Oresund coast of Zealand, the city of Copenhagen is the capital of Denmark with approximately 1.9 million people in the greater Copenhagen metropolitan area. Over the past decades, Copenhagen and the rest of Zealand have been geographically and functionally integrated. In this case study we thus consider all of Zealand as part of the Copenhagen city region. Fig. 1 shows the population distribution in city centres throughout the case region. It is to be noticed, that there is a large concentration of individuals in Copenhagen, leaving the remaining part of the region with only a limited population. In Fig. 1, the population development and official forecast from Statistics Denmark for the entire region from

Based upon a study of scenarios from a range of other projects, and a Delphi survey amongst experts, three scenarios of future development were suggested by Sessa et al. (2011) for the PASHMINA project. The scenarios address the main longterm evolutions of key economic, social, technological and environmental indicators showing possible future states of the system in the year 2050. These include impacts of different paradigm shifts that may result as a consequence of unfolding changes. These range from demographic economic, social and cultural trends to global environmental changes and breakthroughs, such as the emergence of new technologies and applications (Sessa et al., 2011). The scenario overview can be seen in Fig. 4. The PASHMINA project operates with three suggested shifts, represented by the arrows in Fig. 4. The objective is to transition from a “Do it fast and alone” society, to a ‘Do it together’ or ‘Do it slow’ society. The three scenarios imply different levels of shifts. The ‘Growth within limits’ scenario represents a so-called ‘green shift’ based on more traditional ‘green’ thinking focussing on the ‘Do it together’ transition. The ‘Decline’ scenario expresses what could be called a ‘red shift‘, focussing on individual solutions and actions. Finally, the ‘New Welfare’ scenario is characterised by new ways of thinking and acting in order to obtain a more environmentally and socially stable world via what is called a ‘blue shift’. A more detailed description of the scenarios and the background is provided by Sessa et al. (2011). Based on the scenarios and the narratives describing them, elements related to spatial planning practice and societal structures were selected for the modelling of land-use change. For our modelling purpose the ‘Decline’ scenario was ignored and instead a baseline scenario was created and modelled based on the ‘business as usual’ narrative. The interpretations made to address the conceptual drivers were based on Danish planning practices to match the context of the scenarios with Danish planning implementation. The modelling period is from 1990 to 2040 based on the population projections, which is currently only available from the Statistics Denmark for the period until 2040. 2.6. Interpretation of scenarios In order to work with the PASHMINA scenarios in relation to land-use modelling, according to Rounsevell et al. (2006), the first step is to identify key drivers in each scenario. Many different approaches for conducting future studies have been proposed, each varying with the scope of the scenarios and the contents of the narratives. Börjeson et al. (2006) describe three different scenario types that studies can be classified into, being PREDICTIVE e what will happen, EXPLORATORY e what can happen, and the NORMATIVE e how can a certain target be reached (Börjeson et al., 2006). The modelling of the PASHMINA scenarios can be classified into two of those categories. The ‘business as usual’ scenario is carried out as a predictive scenario,

Fig. 3. Distance relationships of new urban cells to selected variables.

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Fig. 6. The ‘New Welfare’ scenario narrative on planning.

Fig. 4. The PASHMINA scenarios.

where a forecast of the current planning and land-use trends is created. Development in this scenario is characterised by a relative large degree of disperse urban development with elements that resemble sprawl. The main drivers of the development are the accessibility to main roads and the cost of land. Hence we apply these elements to the modelling and adjust the proximity factor to facilitate nondense urban development. The other modelled scenarios can be classified as explorative scenarios, where several different system structures are analysed for effects based on the description of the narrative. The narrative contains a detailed description of the world in relation to economic, social, and in this case important planning aspects. The key elements that express planning can be seen in Figs. 5 and 6 for the ‘Growth within limits’ (Growth within limits) and ‘New Welfare’ scenarios (Beyond growth). The ‘Growth within limits’ scenario can be considered the ideal planning layout in Danish planning theory. Under these planning regulations, development would be placed in areas with high access to public transportation to encourage the use of other means of travel than by car. Based on the narrative description of the scenario as based on ‘network cities’, planning zones with strict guidelines would be implemented to prevent sprawl-like development and to create a more compact city structure. For the implementation of this scenario, we elaborate on the ‘Finger-plan’ concept, applied to Copenhagen, where urban development and green areas are placed in a distinct pattern (Matthiessen, 1999). Furthermore we incorporate and enforce the ‘closeness to stations’ principle (Gaardmand, 1993), meaning that all development must happen within a close distance to a station where public transportation can be accessed. Furthermore, we reduce the effect of road accessibility in the model, as well as increasing the influence of proximity to facilitate development closer to existing cities to reduce the individual transportation requirements as stated in the narrative. Contrary, the ‘Beyond growth scenario has to be considered as a break with previous planning traditions, and thereby a shift towards a new planning paradigm. The narrative describes ‘slow cities’ with smart travel behaviour reducing both small and long distance travel. It facilitates a degree of urban densification, meaning that we in our modelling of the scenario enforce urban density through both proximity measures and through job- and urban density measures. Furthermore, the scenario dictates a different approach to transportation strategy, where transportation to work becomes less important. According to this outline we incorporate accessibility to recreational services alongside the ordinary accessibility, which focuses on jobs and centres. This is implemented to illustrate the Internet economy and reduced work time, where, among others, teleworking has great focus. Thereby, the locational preferences are expected to change, creating slow cities with low work-based travel demand.

2.7. Modelling setup and configuration The LUCIA model calculations are based on Equation (1) shown below. The model calculates the cell transition potential for each cell based on a set of factors, being suitability, proximity, zoning constraints and additional scenario specific factors (Hansen, 2007).

P L ðt þ 1Þ ¼ C1L ðtÞ*C2L *.CnL *

(1)

where: P ¼ Transition potential, C ¼ Constraints (Value 0e10), F ¼ Factors (Value 0e 10). W ¼ Individual weight factors (Value 0e10), L ¼ Land-use type.

Table 1 Scenario configuration of drivers. Scenario

Drivers

Weight

Description

Business as usual

Suitability

8

Proximity

3

Property value

5

Main road accessibility Urban center accessibility Public transportation accessibility Suitability

5

Land protection status, non-wetland, slope etc. Distance functions derived by the LUCIA model Land value pr. square metre Distance relation to main roads Distance to nearest urban center Job accessibility by public transportation

Growth within limits

Main road accessibility Proximity

Beyond growth

Fig. 5. The ‘Growth within limits’ scenario narrative on planning.

 X WjL *FjL

6 2

8 1 6

Property value Public transportation accessibility Urban density

3 6

Suitability

4

Main road accessibility Proximity

3 6

Urban density

3

Job density

5

Recreational accessibility Specialized planning zones

5

4

Land protection status, non-wetland, slope etc. Distance relation to main roads Distance functions derived by the LUCIA model Land value pr. square metre Job accessibility by public transportation Urban area within 10 km distance Land protection status, non-wetland, slope etc. Distance relation to main roads Distance functions derived by the LUCIA model Urban area within 10 km distance Number of jobs within 10 km search radius Distance to selected recreational opportunities Adapted planning-zones

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Fig. 7. Sensitivity analysis of selected variables. As illustrated by Equation (1) LUCIA applies a traditional multi-criteria evaluation technique with factors and constraints to estimate the potential for a cell to change state (land-use). The factors are added together and afterwards multiplied with the constraints. This gives another dynamics than e.g. MOLAND, where all factors and constraints are multiplied to estimate the change potential. Thus, in LUCIA a zero value factor can be compensated by higher values for the other factors, while in MOLAND a zero factor means no potential for change. After having selected and standardised the necessary factors in the model, they were assigned a nominal factor weight between 0 and 10 statistically distributing the values into the classes. See Fuglsang et al. (2011) and Fuglsang et al. (2012) for a description of factor creation and selection. A combination of the LUCIA model’s capabilities of determining its own neighbourhood rules, as well as the driver inputs of weighting, represents the third step in Rounsevell et al. (2006), creating specific detailed setups for each scenario. The determination of the factor weights for the three scenarios was done by a stepwise approach, adjusting one factor weight at a time, visually comparing the results to the previous runs. The analysis provided insight as to how the factors influence the results, both individually and together. The produced urban pattern was subjected to a discussion within the project group of what were the most illustrative results given the factors. The final weights were selected based on the results produced, and a visual analysis of how feasible the patterns where e if they could be considered achievable through planning. Afterwards a sensitivity analysis was conducted were the influence of factors on the results was measured. In Fig. 7 the weight applied to two factors are stepwise reduced to zero, and the number of pixels matching compared to the original ‘New Welfare’ scenario is measured. It can be seen, that a lowering of both factors by one means a significant change of factor influence, meaning that the weight applied is a significant factor influencing the final result. Table 1 gives a list of the final weights applied to the scenarios. By creating and analysing these three setups, we generate future projections, which are designed to express very different planning strategies and expectations regarding the individual transport behaviour of the citizen. Thereby we are capable of investigating the consequences of the strategies, and in turn analysing which improvements are the most beneficiary for making sustainable planning. 2.8. Validation of LUCIA 1990e2002 results As mentioned earlier, based on the description of development, the ‘business as usual’ scenario can be considered as a continuation of normal planning practice for the case region. Therefore, in order to validate the work conducted with the LUCIA model, the output of the ‘business as usual’ scenario should be evaluated. The evaluation is conducted against the observed land-use development used as input for the model. Many different approaches to validation of CA modelling have been utilized. Many applications of CA models utilize a visual comparison as validation. Examples of this approach can be found in Al-Ahmadi et al. (2009) and Yang et al. (2008). Based on the extensive analysis of error prediction conducted by Pontius et al., (2008), two important elements are highlighted in analysing the models error predictions:
Analysis of observed change and the predicted change in order to interpret the model error.
The importance of characterising the map differences in terms of quantity disagreement and location disagreement.

Table 2 Kappa statistics 1990e2002 for the ‘business as usual’ scenario.

Most studies also use statistical measures such as the Moran’s I0 index as comparison of patterns suggested by Wu and David (2002) or the use of Kappa Statistics as implemented in White et al. (1997). Examples of this approach can be found in Li and Liu (2006), Barredo et al. (2003) and Liu et al. (2008). Other validation methods such as fragmentation index (Caruso et al., 2005) and coincidence matrix Cheng and Masser (2004), are used in some specific models. In other studies where the LUCIA model has been applied, a combination of visual comparison and Kappa statistics has been utilised. However, the evaluation obtained from Kappa statistics are generally too optimistic and therefore untrustworthy in many cases. Therefore, for the validation of modelling carried out in this study a mixed measure based on Kappa and fragmentation analysis has been chosen. The fragmentation is included to analyse the quantity and locational disagreements described by Pontius (2000). The analysis is included to test the urban pattern that the model produces, to analyse the degree of sprawl it produces. For the fragmentation measure, we calculate the degree of urban fragmentation of new development, comparing the pattern in the observed and modelled data, based on patch fragmentation measures as described by Chen et al. (2001). Fragmentation index ¼

Patches Pixels*g

(2)

The fragmentation calculation can be seen in Equation (2). The variable g is a scaling factor applied to determine the magnitude of the result. The calculation is conducted using a GIS based routine where a region group algorithm clusters areas of the same class, which afterwards are summarised on municipality level. A fragmentation index of 1 (or 1/g) means that each patch only consists of one individual cell while a very small figure indicates few large continuous patches. This index only describes the pattern that the model creates at a more general level, but it can be applied as an overall guideline alongside the Kappa measures to describe if the model produces acceptable patterns.

3. Results 3.1. Accuracy of baseline modelling Comparison of the active land-use classes in the observed and modelled data was carried out using image comparison from the Map Comparison Kit (Visser & de Nijs, 2006). The results expressed as Kappa statistics can be seen in Table 2. The calculation is based on the difference between how much agreement is actually present, compared to how much agreement would be expected to be present by chance alone. Kappa is a measure of this difference, standardised to lie on a 1 to 1 scale, where 1 is perfect agreement, 0 is exactly what would be expected by chance, and negative values indicate agreement less than chance, i.e. potential systematic disagreement (Viera and Garrett, 2005). K-location is based on work by Pontius (2000) suggesting the usage of K-location used to analyse the spatial allocation of the quantity over the entire map (Hagen, 2002). The Kappa values found are generally high, indicating a good match, but if the calculation is conducted only on the changed cells of active classes, the accuracy drops below 30%, which is in line with other LUCIA findings (Hansen, 2010). Table 3 Fragmentation results for the observed and modelled data.

Kappa statistics Observed 2002/Modelled 2002

Fragmentation

Mean

Std. Dev.

Kappa KLocation

Observed Modelled

3.9 3.5

1.25 1.92

0.953 0.961

Fraction correct KHisto

0.972 0.991

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Fig. 8. Fragmentation results of municipalities.

In order to test the overall pattern that the model produces, we analyse the pattern produced on a municipality level in terms of similarity and fragmentation. The overall fragmentation average of the municipalities can be seen in Table 3. The patterns that are produced in terms of fragmentation can be considered to be in line with the observed data. The actual municipality pattern values can be seen in Fig. 8. There are differences within the municipalities as described by the standard deviation in Table 3, which probably can be related to municipality specific conditions that the model cannot account for. In general though, the fragmented urban patterns in the observed data can be reproduced to satisfactory extent in the case area as a whole by the LUCIA model in our calibration, which is shown by average fragmentation analysis. 3.2. Scenario results The modelling produces predictions of the three scenarios towards 2040 for the entire case region. The results of each scenario for one selected municipality can be seen in Fig. 9. It is clear, that the difference of the scenarios is large, producing very different urban landscapes. In the ‘Business as usual’ scenario, the development is scattered across the municipality, with a sprawl like pattern. In the ‘Growth within limits’ scenario the development is more concentrated around the larger urban areas in the region, providing a more compact urban surface, with higher degree of

urban coherence. Finally in the ‘New welfare scenario’, the urban development is shifted dramatically towards the south-western parts of the area, and aggregated into few larger areas of new development, creating an urban surface that has limited resemblance to the other two scenarios. The results produced by the modelling can be evaluated in several different ways in order to describe the outcome. In the following paragraph the produced results will be analysed and discussed further. 4. Discussion To be able to express the PASHMINA storylines as regional planning guidelines, a conceptualisation of the main driving forces of the scenarios was created, defining new drivers that were to be utilised in the modelling. The analysis carried out at indicator level, resulted in a range of new spatial representations of socioeconomic parameters. New methodologies for describing accessibility and thus citizen’s potential demand for new housing areas and transportation were created. Different spatial indicator maps have been developed and integrated into the model, to act as the drivers described in Table 1. The different spatial indicators work as factors in the model as shown in equation (1). The final process made directly in the LUCIA model was to fit the internal weighting of the indicator maps, in order to create scenarios reflecting the

Fig. 9. Scenario results for one selected municipality for the three simulations (left: ‘business as usual’, middle: ‘Growth within limits’, right: ‘New Welfare’).

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overall setup of the scenario storylines in PASHMINA. Simulations have been carried out, showing potential development of the regions under different paradigm shift scenarios on a yearly basis towards 2040. 4.1. Consequences of paradigm shifts on land-use First the land-use classes that are changed must be examined in order to determine the impacts of the scenario results. In Table 4, the land-use change of selected classes can be seen. The value is the number of cells that is changed from its original class into urban, meaning that each count represents a 100  100 m cell i.e. 1 ha. Firstly, it can be observed, that due to the fact that we do not use constraints within the urban area, changes occur within the urban area, thereby making it denser. This means that green urban areas and leisure facilities change to built-up where the model finds it optimal. It is obvious that the ‘New Welfare’ scenario enforces urban densification. Thus the areas within the urban zone converted in this scenario are dominating. In most planning practices this would probably be avoided, but since the scope of the scenarios was to create a denser urban structure - easier to access by foot, bike or public transport, intra-urban reclassification should not be prohibited beforehand. Secondly, the area converted from cultivated agriculture to urban is relatively stable between the scenarios. The ‘Growth within limits’ scenario mostly consumes arable land, while the ‘New Welfare’ scenario consumes more of the complex cultivation land-use cells. Since the soil type and composition of the case region is clay based, it is mostly covered by agriculture and accordingly the urban expansion that the scenarios predict will most likely occur on agricultural land. Thirdly, in our setup there is no protection of forest areas with the same argumentation as with the urban areas. The ‘Growth within limits’ scenario ‘consumes’ most forest of the three scenarios, while the ‘New Welfare’ scenario only ‘consumes’ about half of the ‘Growth within limits’ amount. This means that from a forest preservation standpoint, the ‘New Welfare’ scenario would be preferable. Fig. 10 illustrates the three scenarios in relation to the fragmentation pattern that they produce. It can be seen, that the ‘Growth within limits’ and ‘New Welfare’ scenarios reduce the urban fragmentation, creating more compact urban landscapes than the ‘business as usual’ scenario. 4.2. Dissemination of scenario consequences The area projection of the model is determined by the population driver data that is associated to the model. Based on these Table 4 Other land-use changed to urban for the three scenarios (Selected CORINE land cover classes). CORINE 06 land-use class Type Green Urban Areas Sport and Leisure Facilities Non-irrigated Arable Land Pastures Complex Cultivation Patterns Land Principally Occupied By Agriculture Broad-leaved Forest Coniferous Forest Mixed Forest Natural Grassland Transitional Woodland-shrub

Business as usual (Ha)

Growth within limits (Ha)

New welfare (Ha)

188 765 17,861 159 90 1172

219 686 19,148 68 41 1426

441 898 16,765 311 1778 697

347 49 196 12 77

396 33 314 53 20

121 339 179 265 213

Fig. 10. Fragmentation comparison of the three scenarios.

values, the model calculates the potential for each municipality each year. If we look at these numbers, we can gain insight into the development future of the entire region. Fig. 11 states that the municipalities outside of Copenhagen experience by far the greatest cell-by-cell growth. If we look at the relative counts, it can be seen that in relation to area, the development in the Capitol region is still by far exceeding the rest of the case area. This means that there is quite a large magnitude of difference between the municipalities, why we must conduct our scenario analysis in relation to this information and assess the municipalities according to size and development degree. Another aspect, which is interesting to examine regarding the scenarios, are the land values of the properties that are proposed turning into urban development. From the Danish Building and Housing Register, the price of the property is stored as attribute of the cadastral map. With this information, it is possible to calculate the price per square metre for the entire case region, expressing the actual property value. This will give an indication of the cost for the society when implementing such a development. In the past decades, land has been urbanised in areas that have had high desirability, making them expensive. This cost of the land is amongst other things a guideline for taxation. In Fig. 12, the overall price of land for the development in each scenario can be seen. Here it is evident, that the cost of land in the ‘Growth within limits’ scenario is much greater than in the two other scenarios. Property value is used as a locational driver in the creation of the ‘Business as usual’ scenario, but the weighting of it is quite low, so it does not account for the major difference. The ‘closeness to stations’ principle that is applied in the ‘Growth within limits’ scenario has a large effect on this result. Since development is confined to areas around stations, it will select land that would not be as attractive as others, and thereby has a lower value. There are large variations between the municipalities, but the general tendency is that the ‘Business as usual’ scenario is the most expensive. The nearby municipalities to the north and west of Copenhagen also generally have the highest values, since the property values here are influenced more by Copenhagen, than the municipalities in the rest of the region. For example, the municipality of Slagelse, placed 95 km south west of Copenhagen on Zealand, shows the highest number of new urban cells in the region while only having property values in the region of 10e20 billion, which is half of some of the municipalities located close to Copenhagen. If we analyse the economic aspect deeper, Fig. 10 shows that the municipalities to the north-east of Copenhagen account for the highest values of property in the ‘Business as usual’ scenarios. However, if planning where to follow the path of the ‘Growth within limits’ scenario, it can also be seen in Fig. 10 how the cost of land

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Fig. 11. Urban development figures. Left in number of pixels (a), right in % of area (b).

would differ between two of the scenarios. The area around Copenhagen would be much less costly, and for the whole region, the count of the most expensive classes will be reduced. This would have consequences, as the implementation of one of the two alternative scenarios would result in a graduate adjustment of the property prices in the municipalities, if the plans were to be implemented. It is clear, that in the ‘Growth within limits’ scenario, the areas around stations etc. would become more attractive, since they are enforced as planning zones, channelling focus there. Moreover, the cost of land can be used as a measure of how great the change from current planning practice is e making it an indicator for the impact of the scenario.

5. Conclusion The results of the pathway analysis show that according to the LUCIA setup, a shift away from ‘Business as usual’ might lead to significant advantages for the Danish society. Consequences are more dense urban settlements and will, in the case of implementing our scenario in both the ‘Growth within limits’ and ‘New Welfare’ scenario, lead to a development that will facilitate and thus increase the use of public transportation, by producing more centralised and compact urban areas. The prediction of land-use change, according to the scenarios, shows that the main land-use changes are from agricultural land-uses. On average a total of 180 km2 are to be urbanised in the following 30 years in the region, hence spatial and transport infrastructure planning practice is of

great importance if the development has to be diverted away from sprawl patterns with focus on individual motorised transportation. The densification of urban areas might result in a loss of urban green areas, since these are likely to be included in the planning strategies. This will result in new tasks for the planners to ensure urban quality and to secure access to green urban spaces for recreation and supporting biodiversity in the urban areas. The modelling has shown the impacts of adaptation of planning strategies, and based on the work carried out, it can be concluded that the suggested implementations of the PASHMINA scenarios in planning practice will be a potential key issue in changing the transportation demands and habits of individuals by ensuring better placement in relation to work and recreational opportunities. Implementation of a land-use competition model like LUCIA in planning practice could provide important supplemental information aiming at an optimal placement in relation to work and recreational opportunities and consequences for other types of land-use. The consequences of the implementation of planning policy on regional level can be visualised with high detail using CA based models. Tools like LUCIA could with great benefit be included directly into the planning process. It has been shown how current planning focus and future changes in strategy will affect urban development. The changes in planning practice have direct impact of the landscape. By implementing adjusted planning strategies, urban sprawl can be counter-acted, resulting in more sustainable land use patterns where focus of people transportation shifts away from individual to public transportation modes.

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Fig. 12. Scenario cost of land developed. Left the ‘business as usual’ scenario (a), right the ‘Growth within limits’ scenario (b).

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