CA-BASED SIMULATION OF ASIAN URBAN DYNAMICS: A CASE STUDY OF TAIPEI METROPOLITAN AREA, TAIWAN

Bing Sheng Wu and Daniel Z. Sui

Department of Geography, Texas A&M University

ABSTRACT

The rapid urbanization process in Asia has contributed to a distinctive urban pattern that is different from the West. Though it is believed Asian urbanization process is driven by local activities, some argue globalization also plays a key role in Asian urban dynamics and the distinctive urban pattern. To examine interactions of global/local factors in Asian urban dynamics, this study incorporates remote sensing imagery, GIS technology, and socio-economic data into a CA-based urban model. Taipei metropolitan area, one of the fast growing urban areas in Asia, is selected to examine this model. Our preliminary results indicate that the interaction between local and global processes closely interrelates to drive urban dynamics and shapes the unique urban pattern in Asia.

Index Terms— Cellular automata, urban dynamics, Taipei

1. INTRODUCTION

Urban regions are considered as non-linear, complex, self-organizing, and dynamic systems. Cellular automata (CA), serving as a powerful metaphor for the complex interaction of urban dynamics [1], is viewed as useful as a tool for modeling urban spatial dynamics [2]. Derived from conventional western urban theories, most CA-based urban models pay much attention to the relationship between physical driving factors, for instance, terrain or accessibility to city centers or transportation network. Nonetheless, under the wave of globalization taking place in late 20th Century, rapid urbanization in Asia has exhibited a process distinctively different from that of the West [3]. Factors contributing to time-space compression of Asian urbanization include not only traditional physical constraints but also global economic features (e.g. capital flows from developed countries to developing countries). However, current CA-based urban simulation does not systematically discuss the importance of globalization in Asian urban dynamics. In this paper we focus on foreign direct investment and shift of economic activities as essential indicators to represent influences of globalization on Asian urbanization, and combine these features with GIS and

remote sensing data to develop a CA-based Asian urban model.

2. STUDY AREA AND DATA ACQUISITION

Taipei metropolitan area, a rapid urbanizing region in Asia, is selected in terms of export-oriented policies and deep interaction with global economy. The spatial extent of this area covers City of Taipei, Taipei County, and Taoyuan County. As the largest metropolitan area in Taiwan, the area is 3,678 km2, approximately 10% of the total area of Taiwan. At the end of 2007, around 8.4 million people, 37 percentage of total population, live in this area. In addition, restricted by the rugged terrain, the population distribution is uneven. Most residents aggregate in north-west side of this region. In south-east side of Taipei metro area is mountainous landform, which belongs to part of Central Mountain Ridge, the back-born ridge of Taiwan.

This region has experienced a typical Asian urbanization process: well-established infrastructure, rapid urbanization in major cities and the peri-urban areas simultaneously, sectoral shifts from agricultural economy to industrial activities, and highly depends on foreign investment. During 1953 to 1960, the main economic activity in Taiwan was placed on agriculture sector. In the 1970s, the government carried out “the Ten Infrastructures Project” to construct a solid foundation for future industrialization. Furthermore, the government encouraged investments in labor-intensive industries as well as exporting industries for the expansion of foreign trade [4]. The growth of foreign direct investments on manufacturing industries attracted more young rural residents to change their agricultural activities to industrial activities. As expected, share of primary sector declined and secondary sector rose up. Furthermore, rural population growth took place in terms of small manufacturing factories in towns and villages around major cities such as Taipei. The interaction of population dynamics and various levels of economic activities created a new type of urban dynamics and shaped the unique pattern in Asia.

To examine how the specific urban form is influenced by multi-scale factors including global/local economic activities and population growth, we apply various sources of data: two SPOT satellite images, taken in 1993 and 2000, are used to examine urban expansion and spatial structure of

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the study area. Other GIS data including boundary of Taipei metropolitan area, road network, and DEM data are also acquired for estimate traditional physical constraints on Asian urbanization. Socioeconomic data applied in this model contain: foreign direct investment data (service and manufacturing), population data, and industrial activity data (primary, secondary, and tertiary sector). The temporal scale of the socioeconomic data ranges from 1985 to 2007.

Fig. 1 The geographic location of Taipei metropolitan area

3. METHODOLOGY

In order to bridge socioeconomic impacts on the two landuse classes (non-urban and urban) and implement the urban dynamics via CA modeling, two approaches are applies: multi-scale processes and micro-scale transformation. The approaches are designed to reveal: 1) how globalization interacts with regional/ local scale of driver forces, and 2) how these multi-scale factors influence urban landscapes. The conjunction of these two approaches lies in the key element— population, because population is influenced by (multi-scale) economic activities and population growth changes urban land use. Therefore we use standard Monte Carlo mechanism [5] to estimate the probability of conversion from non-urban to urban areas in terms of the change of population and economic activities. 3.1. Multi-scale processes First of all, the relationship of the all county-level economic data can be written as: Popcounty = f(Primary, Secondary, Tertiary, FDImanufacturing, FDIservice) Since all the data are linearly changing, the above relationship can be expressed as a linear regression equation for estimating population in county level:

servFDImanuFDI

tertiaryondaryprimarycounty

XbXb

XbXbXbaPop

_5_4

3sec21

Although population with external economic factors is successfully connected, county-level population is still too coarse for analysis. Therefore the next step is to breakdown the estimated county-level population into district level. According to the annual demographic data, percentage of population for every district in each county is linear related. Therefore the percentage of district-level population (w) can be estimated through linear regression based on demographic data, and the equation to estimate population for each district can then represent the influence of multi-scale processes on district-level population growth:

Pop district = Pop county * w

3.2. Micro-scale transformation To implement micro-scale transformation, land-use types are required. To acquire land-use types for Taipei metropolitan area, two Spot satellite images are applied for different years (1993 and 2000). Since we want to know how each spatial unit transforms from non-urban to urban land-use type, these satellite images are classified to two categories: non-constructed areas and constructed pixels. Non-constructed pixels include farm, vacant land, or grassland while constructed pixels mean artificial landscape like factories, buildings, or roads. After creating the above two dummy classes, the next step is to develop transition rules to estimate the probability of conversion from non-constructed to constructed pixels. Influential factors for the conversion in Taipei metropolitan area contain distance to road (Dr), distance to city cores (Dc), and slope for development(S). Each factor is created from GIS data, and is converted to raster-based buffer grids. Each of buffer grids is further reclassified into several groups. To estimate the probability of the conversion from a non-constructed to constructed pixel, a multinomial logistic regression is conducted as a stochastic approach. The multinomial logistic model is useful for discovering regression and estimating probability of change from condition i to j on the use of nominal data. The equation of multinomial logistic regression is as following: Therefore, to estimate the probability of conversion for each cell (Pnon_urban urban), multinomial logistic regression model will be applied for dealing with the above factors, and the transition rule will be defined as:

(1)

(2)

y

y

ji ee

1Prob

n

iii XY

1 where (3)

(4)

slopeicenterecoiroadi XXXY _ where

y

y

urbanurbannon ee

1Prob _

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3.3. Monte Carlo mechanism Through multi-scale processes, we can estimate population at district level from multi-scale economic activities. From micro-scale transformation, we get a transition rule derived from multinomial logistic regression to distinguish if a spatial unit will change it status from none-constructed to constructed condition. Since population growth stimulates urban growth, to some extent, population growth also result in the increase of constructed areas. From the classified satellite images, we’ve had the total amount of constructed pixels for different year. With the district-level administration boundary, urban/non-urban pixels can be counted for each district by zonal statistical analysis. Compared with the change from none-constructed to constructed pixels and population growth for each district from time t to t+1 in two years, a linear equation can be conducted: Based upon this equation, we employ standard random Monte Carlo mechanism to determine if an increased constructed pixel should be assigned in space. First, the estimated population for a district in different years can be estimated from Eq. 2. The increase of population ( Pop) can be direct calculated. Applying the increase of population into Eq. 5, the amount of converting pixels (non-urban to urban) can be estimated. The last step is to assign the changed pixels in the metropolitan area. Since the probability of the conversion from a non-constructed to a constructed pixel (Pc) is computed from Eq. 4, we can randomly assign a value (P random) between 0 and 1 for each non-constructed cell in the space. If P random < Pc, it implies the non-constructed cell has higher opportunity to change its status, so the cell will change it status to constructed. The comparison/ conversion process will repeat until all converting pixels are assigned to the space. Through the Monte Carlo simulation, we can successfully connect two different methods and link multi-scale processes to implement a CA-based urban model and illustrate the interaction of urban processes, especially globalization, on the desakota urban pattern.

4. PRELIMINARY RESULTS Unlike the clear distinction between urban and rural in the West, the major significance of Asian urban landscape is that there is no clear-cut division between urban and rural areas [6]. After classifying SPOT images by using ISODATA classification method in ENVI 4.2, part of the spatial structures of Taipei metropolitan area in 1993 and 2000 are shown in Figure 2. Comparing two images, several towns are growing rapid and gradually merge together in the center area. Urban expansion can also be found on the upper

right corner. The classification results reveal distinctive urban landscape in Asia.

Fig. 2 Classification results (Dark: urban, Gray: non-urban) The expansion of urban regions is influenced by two major factors: physical constraints and globally/ locally socioeconomic drivers. Physical constraints including buffer of slopes, distance to economic centers, and buffer of roads are used to evaluate the probability of conversion from non-urban to urban pixels. We randomly pick up 20 thousands sample points and record nominal values of these physical constraints. Through multinomial logistic regression model in SPSS 15, the equation of probability is established, and the probability curve is shown as below. Although the curve does not perfectly fit in the S-shape, it represents a relatively strong relationship between the conversion from non-urban to urban and physical constraints.

Fig. 3 Probability curve through multinomial logistic regression

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability

1_ ttdistricturbanurbannon PopArea (5)

SPOT 1993

SPOT 2000

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The relationship between population and multi-scale economic activities is defined through linear regression model. The county-level populations of three counties are influenced by various economic factors: Pop (City of Taipei) ~ secondary, tertiary, and FDIservice Pop (Taipei County) ~ tertiary, and FDIservice Pop (Taoyuan County) ~ secondary, and FDImanufacturing City of Taipei is the core of the metropolitan area, so service industry, domestically and internationally, plays an important role in population growth. Taipei County is suffering rapid conversion from secondary to service sector. Therefore population growth is related to tertiary industry. The major economic industry in Taoyuan County is manufacturing industries. Hence the relationship between population and manufacturing industry is strong.

5. SUMMARY The combination of hierarchical approaches helps us develop a CA-based model to represent the important influences of globalization with domestically economic/ population drivers on Asian urban dynamics and quantitatively draw characteristics of Asian urbanization processes, which are distinctly different from Western societies. Through integrating remote sensing imagery, GIS technologies, and spatial statistical analyses, this model successfully bridge static urban landscape with dynamically urbanization processes, and how the wave of globalization change urban structure in Asia. In addition, through this model, we also learn how the dazzling urban development of Taipei metropolitan area is growing in recent years. Our next step is to implement the conceptual urban growth model so we can simulate various scenarios and help stakeholders such as governments, urban planners and policy makers to design feasible plans and reach the goals of sustainable urban development.

6. REFERENCE

[1] H. Couclelis, “From cellular automata to urban models: New principles for model development and implementation,” Environment and Planning B-Planning & Design, vol. 24, no. 2, pp. 165-174, 1997. [2] X. Li and A. G. O. Yeh, “Modeling sustainable urban development by the integration of constrained cellular automata and GIS,” International Journal of Geographical Information Science, vol. 14, no. 2, pp. 131-152, 2000. [3] D. Z. Sui and H. Zeng, “Modeling the dynamics of landscape structure in Asia's emerging desakota regions: a case study in Shenzhen,” Landscape and Urban Planning, vol. 53, no. 1-4, pp. 37-52, 2001.

[4] P. K. C. Liu and H. H. Tsai, “Urban Growth and Employment in Taiwan,” The Extended Metropolis: Settlement Transition in Asia, University of Hawaii Press, Honolulu, pp. 193-213, 1991 [5] Y. Xie, M. Batty, and K. Zhao, “Simulating Emergent Urban Form Using Agent-Based Modeling: Desakota in the Suzhou-Wuxian Region in China,” Annals of the Association of American Geographers, vol. 97, no. 3, pp. 477-495, 2007 [6] T. G. McGee, 1991. “The emergence of desakota regions in Asia: expanding a hypothesis,” The Extended Metropolis: Settlement Transition in Asia, University of Hawaii Press, Honolulu, pp. 3-26, 1991

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