D. Taniar et al. (Eds.): ICCSA 2010, Part I, LNCS 6016, pp. 77–88, 2010. © Springer-Verlag Berlin Heidelberg 2010

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy on Environment in

Danshuei Township, Taiwan

Feng-Tyan Lin and Wan-Yu Tseng

National Cheng Kung University, College of Design and Planning, National Taiwan University, Graduate Institute of Building and Planning

Taipei, 10617 Taiwan (R.O.C) ftlin@ntu.edu.tw, cindyfish68@gmail.com

Abstract. The impact of urban growth on eco-environment leading to global climate change has attracted great concern. In this research, we use NetLogo, a cellular automation based software, to simulate the development process of Danshuei from an original settlement to a highly developed satellite town of the Taipei Metropolitan area, Taiwan. The study area is a good case showing how to balance between urban development and natural resource protection. The model is validated against the official data extracted from National Land Use Survey of 2008. Four specific scenarios, which have different rules for urban development and strategies for natural resource conservation, are used to simulate the spatial pattern of urban growth. The first scenario simulates the urban growth using slope factor only. The outcome reveals that the areas with lower slope obtain higher potential for urban development. Following the first scenario, the second scenario adds neighbor effect and road gravity as the development rules in the model. The third and fourth scenarios assume a managed growth with moderate and restricted protection of natural resources respectively. The result demonstrates that restricted control could avoid from dramatic loss of forest and agricultural areas.

Keywords: Cellular Automata; urban growth model; policy simulation; environmental impact.

1 Introduction

Urban growth modeling was commenced in the 1960s; however, cities which are open and non-linear systems have not been satisfactorily modeled by the traditional mathematical methods. The complex factors in urban system are difficult to be predicted by traditional mathematical models and therefore the predictions of urban growth were often inaccurate [1]. Consequently, instead of prediction, many researches adopted simulation tools to discuss how the future development will differ and where urban growth is likely to occur by assuming different urban growth scenarios [2]. Advanced technologies in remote sensing and GIS have helped the development of more sophisticated approaches in urban growth models [1] [3-5], such

78 F.-T. Lin and W.-Y. Tseng

as spatial simulation models. Cellular automata (CA) which is a simple but promising approach, has gained great attention from urban researchers in recent years [6] [7]. They have the potential to simulate the complex systems like cities [7] [8].

CA, which is a grid-based model, considers spatial organization in terms of evolution and provides another point of view to spatial evolution [9] [10]. The status of each grid is decided by rule of status of transformation. Its mathematical form is defined in equation 1:

),(1 Ω=+ t

ij

t

ij

t

ij SS F

(1)

St

ij represents the status of cell at location (i,j) at time t. Ω t

ij is a collection of status

of neighboring cells at time t. The definition of ‘neighbor’ is the surrounding cells having k distances to the cell (i,j). F is a transition function. The status of cell (i,j) and its surroundings at time t determine the impact on a particular cell (i,j) at time t+1 [9] [10] . CA model can operate a bottom-up approach of integrating land use factors, which is different from the previous research of top-down process to describe policy governed urbanization [1] [9] [10] . Increasing amount of researches have applied CA model to simulate urban development patterns, the dynamic growth of urban spatial structure, and evaluate environmental impacts. For instance, Hakan [6], Jantz et al.[7], Wu [11] ,Dragicevic et al.[12] and Zhang et al.[13] had used CA model to dynamically simulate the recent hundreds years of development and consequences of land use patterns and changes under different scenarios of land development policies. Syphard [1] forecasted the effects of urban growth on habitat pattern in southern California with three different levels of development prohibited on slope, and evaluated the fragment of landscape. Silva et al. [14], Wu et al. [15] and Hakan et al. [16] had focused on calibration of simulation models using historic images. Amongst, SLEUTH model, formerly the Clarke Cellular Automaton Urban Growth Model, was widely used. SLEUTH is an acronym for the six customized input data of the model: Slope, Land use, Exclusion, Urban extent, Transportation and Hillshade, [6]. These researches revealed that slope, road gravity and land use policy (zoning) are influencing factors to urban growth.

This study uses GIS technique and NetLogo, which is a CA based model (more detail in section 2.1), to simulate the urban growth of Danshuei in Taipei Metropolitan area, Taiwan. The reasons to choose NetLogo as simulation tool includes not only its flexible programming function, but relatively less applied in urban growth model.

Increasing demands of economic growth accelerate the rate of over development of land resources. Inappropriate urban sprawl and unsuitable configuration of land use destructively impact the ecological environment. Urban development with environmental consideration should be considered and this could be achieved by land use planning, policy and government. In view of this, urban growth simulation technique could be an evaluation tool to review the effectiveness of land use policy.

The real estate and transportation development of Danshuei township has been carrying out to reduce the over-dense population pressure of Taipei city, which is the political and financial center of Taiwan. The construction of new infrastructure due to the new development adversely creates significant environmental impact. The study area is a good case showing how to balance between urban development and natural

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy 79

resource protection. Four specific scenarios, which have different rules for urban development and strategies for natural resource conservation, were studied. The impacts on forest and agricultural lands under different level of land use control were also assessed.

2 Materials and Methods

Danshuei township is located at the northwest coast of Taipei County, Taiwan, and north of the estuary of the Danshuei River (Fig. 1). Taipei County governs ten county-controlled cities, four urban townships, and fifteen rural townships, while Danshuei belongs to the urban township. Danshuei encompasses an area of 70.65 Km2 with a population density of 1968.5 p/km2, which is the 12nd largest among the Taipei metropolitan statistical areas in 2009. Although over 80% of the Taipei county residents live in the 10 county-controlled cities of Taipei County, the recent construction policy on real estate and transportation in Danshuei has attracted developers and urban workers to invest in this place. The population growth of Danshuei township has increased by 87.30% from 1986 to 2009 reaching 139,073. The building coverage has also been significantly increased, from 2.8 km2 to 7.6 km2 (Fig. 2).

Danshuei was an important port for the shipping route to the Southeast Asia in the 16th century. Human settlement had been living in this place for more than 7000 years and their living had been relied upon fishery and farming. The aboriginal residents were known as Pepo-frau. Due to its excellent geographical location for business and military purposes, Spainish, Dutch and Japanese developed this place after conquering the north of Taiwan. After the long term colonial development of Danshuei, the place remains with many historical heritages such as forts, schools, transportation stations and traditional streets. The excellent scenery with the historical heritages and golf courses creates huge potential for tourism business.

Fig. 1. Location of Danshuei Township

Danshuei township

Taipei city

Keelung city

80 F.-T. Lin and W.-Y. Tseng

Fig. 2. Population and building coverage growth by year

Apart from the artificial scenery, natural resources like forest and agricultural land are great treasure to the environment and economic income. Danshuei has used to supply a great amount of agricultural products to Taipei County. However, with the agricultural policy being adjusted, the government is now promoting Danshuei and transforming its agricultural industry from production based to recreational and tourism based. Also, since 1980, the Government has been proactively promoting the revitalization of traditional streets, establishment of access roads to Taipei city and neighboring townships and development of new township to promote the tourism industry. However, all of these policies are likely to be risks to the conservation of natural resources, especially when unsuitable configurations of land use are planned. Therefore, this study uses zoning map to adjust coefficients of green resources in the simulation models, and to assess the amount of conserved green resources.

2.1 NetLogo Simulation Tool

NetLogo simulation software was developed by Northwest University, US (http://ccl.northwestern.edu/netlogo/). It can generate complex phenomena through interactions among individuals, and organize macro data and geometric attribute data for spatial analysis. NetLogo has been applied to simulate the complicated mechanism of land use change on urban growth and rural village [17].

NetLogo is adopted in this study because it can simulate and dynamically display the temporal and spatial changes of urban form during the urban growth processes. It has friendly user interfaces and programming functions which are easy to manipulate.

2.2 Input Data, Simulation and Validation

This study uses official topographic maps of 1986, 1994, 2003 and National Land Use Survey data of 2008 as the basic inputs to acquire the land use data and describe the urban growth in Danshuei township. These official topographic maps are firstly digitized, followed by overlaying with 100 by 100 meters square grid in GIS software. Then, 9 categories of terrain features, namely built-up, infrastructure, transportation,

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy 81

agricultural land, forest, grassland, barren land, wetland and water land, are identified according to the maps. These 9 categories are separated into different layers; and the coverage of each category is mapped manually, using a cell-by-cell approach, onto the corresponding layer. The coverage of each category, except the transportation, is graded from 0-4 based on the percentage of the area occupied by that particular category to the total area of the grid The five level corresponds to 0%, 1~20% (10%), 21~50% (35%), 51~80% (65%) and 81~100% (90%) respectively. The transportation layer uses a classification system different from the others. The cell is graded in 4 levels based on the class of road system, such as national high way, provincial highway, prefectural highway and country road. The grading does not consider the area of the road system within the cell but only “existence or non-existence”. As for the built-up area category, this study only concerns whether or not the cell is urbanized or non-urbanized but not the grading for built-up area. The cell will be regarded as urbanized once the coverage area is greater than 0.

The model domain in NetLogo simulation software is divided into 18125 (125*145) cells, and Danshuei occupied 7211 cells. Urban growth in the model is determined by growth rules set by users. Starting point (seed) is assigned or randomly placed in a particular cell. The seed begins to grow and occupies one of adjacent cells which are vacant. The newly occupied cell becomes a seed in the next iteration. This process repeats until the maximum number of iterations predefined in the model is reached.

This study considers four scenarios which are interacted by three main parameters that are seeds, growth rules and different restriction levels of policies (Table 1). Each of the four scenarios simulates a particular aspect of the development process. The iterations of the simulation required to reach the ultimate development stage vary with scenarios and cases. In this study 35 Monte Carlo iterations are sufficient.

In this study, the starting points are set according to the six original settlements of Pepo-frau aboriginal residents recorded by historical literatures. The four scenarios of growth models are initialized with these six seeds in time zero. The simulation ceases when settlements grow till stable states are reached. The growth rules are set by three growth coefficients: slope, neighbor effect and road gravity. Slope coefficient considers a seed cell to seek the nearest unoccupied neighbor cell with the lowest

Table 1. Scenarios studied and parameters used in the scenarios

Growth Scenarios Parameters Growth Coefficients 1 2 3 4

Starting points six six six six Slope ▲ ▲ ▲ ▲

Neighbor effect ▲ Growth rules Road gravity ▲

Non ▲ Managed growth with moderate protection

▲ Policy Restricted Condition Managed growth with

restricted protection ▲

82 F.-T. Lin and W.-Y. Tseng

slope. Neighbor effect considers the summation of building coverage of nine cells, which are the seed cell and the eight neighboring cells. Road gravity considers the distance from each cell to the nearest cell with road. There are two policies which manage urban growth with moderate or restricted protection.

The process of simulation has two stages: model validation and subsequent prediction. Firstly, the model derives a set of values from physical results, which can effectively describe the urban growth of Danshuei township. The actual growth in 2008 of Danshuei township is used to compare with the ultimate urban development form simulated by the urban growth model. The model accuracy is assessed based on three parameters: predicted accuracy rate, over-predicted rate and less-predicted rate. These parameters describe the amount of land area that accurately, over- and under-predicted the urban growth phenomena. It is to note that over-predicted rate does not mean wrong prediction. They may indicate that the places have a high potential to be urbanized in the future.

Forest and agricultural loss rate are simulated in the four scenarios. As there is no data available before 1986, the loss rate of agricultural and forest areas are calculated based on the data of 1986. The agricultural and forest areas are 27.6 km2 and 24.2 km2

respectively in 1986.

3 Simulation Results

3.1 Scenario 1

The growth rule of Scenario 1 only considers coefficient of slope. Figure 3 shows the simulation result of building area growth and the comparison to the satellite photo of 2009 acquired from Google Earth map. In the simulation result, red color corresponds to accurate prediction; blue color represents over-prediction; and grey color indicates under-prediction. The gradient of the grey color is indicative of the density of building coverage. Darker grey means higher density of building coverage.

The simulation result shows that comparing with the satellite image of DanShuei as at 2009 the predicted accuracy rate reaches 59.43% and the over-predicted rate reaches 33.59%. Forest and agriculture loss rates reach 27.12% and 38.78% respectively. If the areas, that are rated as over-predicted rate and are feasible to be urbanized, were developed in future, then forest and agriculture loss rates would increase by 13.41 % and 12.47% respectively.

The cause of the over-predicted areas may not be fully explained. However, for example: (1) the white circle in figures 3 indicates the new township planning. Although the site of the new township has few fragmented buildings, the site planning has been done for years. The area circled in white could not be regarded as over-prediction as it will be developed in future. (2) The orange circle shows that the model over-predicts in an area which has a golf course. This again could not be regarded as over-prediction as a golf course could be seen as an occupied/developed land.

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy 83

a) satellite image b)simulation result

Fig. 3. The result of scenario 1-1 and comparing to satellite photo

3.2 Scenario 2

This scenario adds road gravity and neighbor effect in the urban growth model. Figure 4 shows the effects of neighbor effect on the predicted accuracy rate and over-predicted rate. When the neighbor effect is ranged from 100 to 900 m2, the predicted accuracy rate is higher than 50%. However, when the value for neighbor effect is equal or greater than 1,000, the predicted accuracy rate reduces significantly. Based on this observation, it is suggested that the urban development in Danshuei township would spread over an area instead of concentrated at a particular place.

Fig. 4. Predicted rate of different levels of neighbor effect

84 F.-T. Lin and W.-Y. Tseng

Figure 5 shows that the predicted accuracy rate increases with the value for road gravity. This suggests that the urban development in Danshuei township tends to occur at a place which is 250m or above away from a road system. However, as the urban development gets further away from a road system, the forest and agriculture loss rate increases. The loss rate is noticeable when the distance between new urban development and a road system is greater than 2000m.

Fig. 5. Predicted rate of different road gravity

4 Environmental Impact Simulations

The first and second scenarios of growth model do not consider policy protection against development. In this situation, natural resources including agriculture land, forest, parks, water and wetland are proven to be exceedingly damaged. Therefore, apart from the above growth scenarios, two additional scenarios with consideration of policy restricted conditions are simulated: (1) managed growth with moderate protection, (2) managed growth with restricted protection. The controlled elements are referenced by present zoning map. Agriculture lands of zoning map are divided into three divisions, one category belongs to Agriculture District of Urban Planning; the others belong to non-urban area which is further categorized into General Agricultural Zone and Special Agricultural Zone. Special Agricultural zone is defined by Agricultural Development Department as high quality agricultural land and should be highly protected. The coefficients for these two scenarios are summarized in Table 2. Other controlled elements include other green resources and water. In the Other green resource category it is further divided into recreational area, general forest districts, urban parks, National Parks, slop land conservation zone. As for water category, it comprises wetland and river district.

Figure 6 shows the results of scenario 3 (a) and 4 (b). In the figures, the pink area indicates the potential area where may be deforested in the future under the policy restricted conditions; and the yellow area is indicative of the potential area where agricultural land may loss in the future.

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy 85

Table 2. Scenarios studied and parameters used in the scenarios

Excluded Element From Development Scenario 3 Scenario 4

Element

Managed growth with moderate

protection

Managed growth with restricted

protection Agriculture District of

Urban Planning x

General Agricultural Zone

x Agriculture Land

Special Agricultural Zone

x x

Recreational area x General forest district x

Urban parks x National Parks x x

Other green resources

Slop land conservation zone

x x

Wetland x x Water

River District x

a) b)

Fig. 6. The result of scenario 3 (a) and 4 (b)

86 F.-T. Lin and W.-Y. Tseng

Comparing scenario 3 with scenario 1 suggests that the environmental restriction policy can reduce the forest loss rate by 9.2% and the agricultural land loss rate of 8.03%. As in scenario 4, the reduction for forest loss rate and agricultural land loss rate is 11.17% and 9.33% respectively (Table 3). The results show that the environmental restriction policy is an effective method to conserve natural resources.

Table 3. Comparison of scenario results

S1 S3 S4 Accuracy rate 0.5943

Over-predicted rate 0.3359 Forest-damaged rate 0.2712

Agriculture-damaged rate 0.3878 Future damaged rate of forest 0.1341 0.0421 0.0224

Future damaged rate of agriculture 0.1247 0.0444 0.0314

5 Results

First scenario assumes that slope is the main factor of affecting urban growth of Danshuei township. It achieves a reasonable accuracy rate comparing to the actual urban growth of 2008. The over-predicted areas are proven to be the high potential development areas. However, it also shows that over-predicted areas occupy some natural resource districts. Results from first scenario indicates that the Danshuei township has lost 27.12 % forest and 38.78% agriculture land, and a considerable amount of them may further to be lost by 13.41 % and 12.47% respectively. Therefore, protection policy strategies are assigned in third and fourth scenarios. The results presents that restricted protection policy will have a distinguished outcome for preservation of natural resource. 11.17% of forest and 9.33% of agricultural land areas can be saved under the most restricted policy in fourth scenario.

6 Conclusion and Discussions

Increasing urban sprawl and its results in degradation of environments has attracted concerns throughout the world. Understanding the driving forces of urban growth and its dynamical evolution is necessary for decision makers and urban planners. Evaluating the impacts of urban growth on the natural resources is also needed to involve into the simulation models. CA model shows an appropriate approach to urban growth modeling. This study explores the adequate achievement of applying NetLogo model for urban scenario planning and decision making process. The visualization of urban growth model and potential land use change areas (forest and agricultural land loss areas) is a useful tool for urban planners and decision makers.

With the recent trend in compact city, it is expected to decrease the amount of new development occurring in rural area or outlying areas. Urban growth model, with consideration of policy restricted conditions in the modeling process, appears to be an effective tool for developing and refining conservation policy strategies. Danshuei township is used as case study. It is a rural area with abundant natural resource and is

Using Cellular Automata Model to Simulate the Impact of Urban Growth Policy 87

facing a development pressure on the near future. How to efficiently preserve natural resource avoiding incidentally development from loosen control of zoning alteration has to be addressed in Danshuei. Therefore, this study formulates four scenarios to simulate urban growth and its impacts on forest and agricultural land. The outcomes of these four scenarios show that slope factor is the main driving force of urban growth. Neighbor effect and road gravity applied in the second scenario are lesser influential comparing to slope factor. Scenarios of environmental impact analyses show that restricted zoning control could have considerable preservation of forest and agriculture land in the future.

Three major aspects will be addressed and further investigated in this urban growth modeling study. They are model calibration, investigation of other driving forces/growth rules; and methods for accuracy assessment.

Although the CA model has been successfully used to simulate urban patterns, they have been criticized for not being able to model the urban transformation processes [12]. The reason for this is lacking historical images/data. The same reason defers from a detailed calibration of urban growth model to be performed. As a result, more historical images/data will be collected to undergo a detailed calibration as well as to improve the model capability in modelling urban transformation process.

The driving forces and constraints of urban development adopted in the model play an important role in determining the urban patterns. Yeh [18] mentioned that most of the researches applied CA models under the assumption that urban density in uniform in each cell. Urban density is a critical factor in urban planning and affects urban form substantially. Therefore, to improve the model prediction accuracy, urban density and other social factors will be included in future simulation models; and also combination of the strategies of development constraints will be considered.

The assessment methods for model accuracy applied in this study are predicted accuracy rate, over- and less-predicted rate. Further assessment methods such as Kappa, fuzzy Kappa statistic and other index will be examined in future research. Acknowledgements. This study has been supported by National Science Council, No. NSC 98-2621-M-002-023.

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