Urban Planning and Design Research (UPDR), Volume 3, 2015 www.seipub.org/updr doi: 10.14355/updr.2015.03.002
Urban Expansion and Its Influences on The Suburban Land Use Change in Jakarta Metropolitan Region (JABODETABEK) Ryota Nagasawa*1, Asuka Fukushima2, Lissa Fajri Yayusman3 and Dandy Aditya Novresiandi4 Faculty of Agriculture, Tottori University, 4‐101 Koyama Minami, Tottori, 680‐8553, Japan
1, 2
The United Graduate School of Agricutural Sciences, Tottori University, 4‐101 Koyama Minami, Tottori, 680‐ 8553, Japan 3, 4
nagasawa@muses.tottori‐u.ac.jp (Corresponding Author)
*
Abstract The recent urbanization process in emerging countries is entirely different from the process seen in developed countries. In Asia’s emerging countries, urbanization occurs along with fast population growth that follows rapid economic development, and industries and population are centralized into large cities. One typical example of this centralization is seen in the Jakarta Metropolitan Region (JABODETABEK). In this study, the satellite remote sensing images, as well as the existing GIS data, were integrated to generate temporal (2001 and 2009) land use/cover maps which could be used to understand the recent expansion of urban areas and the spatial changes in land use of the suburbs (suburban farmlands). Those are also used to analyze land use with a landscape ecological patch analysis, and to explain land use change from traditional agricultural farmlands to urban areas due to the sprawling phenomenon, which indicates chaotic urban expansion. The result shows that urban land use was rapidly expanded to suburban areas. Agriculture and forest lands were mainly converted into urban land use, and urban and traditional agriculture landscapes were, consequently, extremely mixed. An analysis using landscape metrics also quantitatively revealed that the mixture of land uses and the fragmentation of agricultural landscapes widely existed in the suburban areas at a patch level. Keywords Urbanization; Suburban Areas; Land Use/Cover Change; Landscape Patch; Logistic Regression Analysis; JABODETABEK
Introduction The recent urbanization process in emerging countries is entirely different from the process seen in developed countries. The global urban population rapidly increased from 732 million (29% of the total world population) in 1950 to 3.2 billion (49% of that the total world population) in 2005. According to United Nations, World Urbanization Prospects [12], the world urban population will increase to 4.9 billion (60% of the total world population) by 2030. Of the 25 most populous cities in the world, 17 are located in Asia. In Asia’s emerging countries, urbanization occurs along with fast population growth that follows rapid economic development, and industries and population are centralized into mega‐cities. One characteristic of these mega‐cities is that the urban area with its large population and vast suburban and rural areas expands without definite boundaries [4], [5]. McGee [8] called the recent situation of urbanization in ASEAN countries “mega‐urbanization.” McGee [7] also defined the term of “desa‐kota” (it comes from Indonesian, “desa” means ʺvillageʺ, while “kota” means ʺcityʺ) for characterizing the mixed landscapes of urban and rural village widely seen in the large cities in the Asia (Figure 1). The Jakarta Metropolitan Region called JABODETABEK is composed of the Jakarta special metropolitan district (DKI Jakarta : Daerah Khusus Ibukota Jakarta) and eight surrounding administrative units, and it functions as the center of economic growth in the Republic of Indonesia. Recently, the population has decreased in the central area of the DKI Jakarta, and has rapidly and continuously been increasing in the suburban areas. Urban sprawl into the suburban areas has led to the recognition that it will have adverse effects on the environment and ecology of the
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farmlands in the future [9]. More recently, Rustiadi et al. [10] also surmarized the trend of urbanization in JABODETABEK. According to their article, the process of urban transformation in JABODETABEK has been driven by economic expansion such as industrial complex and new satellite towns, and it has resulted in extended areas of mixed land use of city peripheries. JABODETABEK, as a mega urban system, is still undergoing physical growth and the development of various activities.
FIG. 1 TYPICAL DESA‐KOTA LANDSCAPE SEEN IN KAB. TANGERANG (URBAN LAND USE INVADED INTO THE RURAL LANDSCAPE IN THE SUBURBS)
However, no up‐to‐date data is yet available to provide a full understanding of these effects. This makes actual urban and regional planning difficult to apply to the appropriate environmental and ecological management of these farmlands. Continuous development of a city and appropriate management of its suburban land use and land resources depend on a correct understanding of the expansion processes of the city and the effects of its expansion. Changes in land use reflect changes in the natural, social, economic, and cultural environments of a region. Therefore, understanding land use changes is most important for explaining the characteristics and environmental issues of the region. The ability to construct a database of this information can greatly benefit land use planning. In this study, the temporal satellite images and the existing Geographic Information System (GIS) data were integrated to generate map information for the JABODETABEK that could be used to understand the recent expansion of its urban areas and the geo‐spatial changes in land use of its suburban areas. The objectives of this study are to provide a quantitative investigation of the area and density, to analyze land use by a landscape ecological method of patch analysis, to predict the future land use changes by means of logistic regression analysis in conjunction with GIS‐based spatial information. Materials and Methods Study Area As shown in Figure 2, The JABODETABEK is composed of nine administrative units; the DKI Jakarta, Kota Bekasi, Kabupaten (Kab.) Bekasi, Kota Tangerang, Kota Tangerang Selatan (separated from Kab. Tangerang in 2010), Kab. Tangerang, Kota Depok, Kota Bogor and Kab. Bogor. The JABODETABEK has an area of 6,392 km2, and its center, the DKI Jakarta is 664 km2. The population in 2010 was 28,019,545 for the JABODETABEK and 9,588,198 for the DKI Jakarta. The Indonesian Government estimates that the population of the JABODETABEK will reach 32 million before 2016. Further expansion and development of the megalopolis are predicted.
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FIG. 2 STUDY AREA (JABODETABEK) (BACKGROUND IMAGE : ALOS AVNIR‐2 TAKEN ON JULY AND AUGUST, 2009)
Data and Analytical Tools Used This study produced land use/cover maps (7 categories) by means of the unsupervised (ISODATA) classification of ALOS Avnir‐2 PRISM Pan‐Sharpen (data taken on 2009/07/13 and 08/03) and Landsat ETM+ (data taken on 2001/05/03 and 07/15). Those created a temporal dataset that allowed analysis of temporal changes in land use/cover over 9‐years period. GIS was applied for integrating those land use/cover maps with various spatial data such as topo‐maps, administrative boundaries map with population census, road network map, DEM (ASTER GDEM2) and existing land use plan map. Quantification of landscape pattern of urban and agricultural land uses were attempted using spatial pattern analysis software, FRAGSTATS, which is a computer software program developed at Oregon State University, for computation of a wide variety of landscape metrics for categorical map patterns [6]. The logistic regression analysis (uses SPSS) was also applied to predict the potential urbanization areas (sub‐district level) on a basis of the established spatial dataset in this study. Results and Discussion Land Use/Cover Change between 2001 and 2009 Temporal land use/cover classification maps were produced using the time series remotely sensed data. In case that an image was covered by clouds, it was removed by making a cloud mask using the iterative self‐organizing data analysis technique (ISODATA) method. The normalized difference vegetation index (NDVI) was then obtained to create water area mask images. For images with only lands (non‐water areas) were extracted using the NDVI. Subsequently, ISODATA classification was hierarchically performed to classify the land use/cover into 7 categories: built‐up area, water bodies, bare land, forest, grass land, agricultural land and fish pond (Figure 3). As the accuracy assessment shows 83.03% and 85.24% of comprehensive total classification accuracy for the year of 2001 and 2009 respectively (Table 1), it is assumed that those maps can contribute to subsequent analysis of land use/cover change and landscape metrics. As a tendency of urbanization, the built‐up area had increased 29,213ha (+21.56%) between 2001 and 2009 (Figure 4). On the other hand, the forest area had decreased 21,636ha (‐8.51%), and farmland had also decreased 14,489ha (‐5.78%). The change detection analysis shows that the newly urbanized areas are most widely identified in the Kota and Kabupaten Tangerang (western suburbs of DKI Jakarta), and the
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Kota Bekasi (eastern suburbs of DKI Jakarta) follows especially along the express high ways. In the suburbs of Kota Bogor, urban land use was largely seen expanded to the traditional rural area in the shape of a sprawl.
FIG. 3 TEMPORAL LAND USE/COVER MAPS OF JABODETABEK TABLE 1 RESULTS OF ACCURACY ASSESSMENT
FIG. 4 TEMPORAL LAND USE/COVER CHANGE BETWEEN 2001 AND 2009
The Landscape Patch Analysis The process of rapid mega‐urbanization in Asia’s emerging/developing countries, because of the disorderly development of residential areas and industrial parks in suburban farmlands, leads to splitting and fragmentation of traditional agricultural land use and results in land use changes. In suburban areas, strong artificial influences have been exerted and patch‐like landscapes consisting of various small‐scale land uses now appear. Investigation
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of the present distribution characteristics of each land use requires the utilization of landscape metrics, such as shape, proximity, and contagion metrics, as indices for each patch, in addition to measuring the area of each land use. In this study, landscape coefficients that indicate proximity, connection, diversity, and mixture degree of land use patches were analyzed for the present situation of landscapes. The landscape patch is defined as a relatively homogeneous area that differs from its surroundings [2]. In this study, the patch metrics analysis was conducted for the land use/cover map of 2009. As described in previous reviews [1], [3], [11], the patch density (PD), largest patch index (LPI), and mean patch fractal dimension (MPFD) were selected as simple indicators for evaluating the landscape structure (Figure 5). These landscape metrics were computed using a 15x15 km sized mesh within the area of the JABODETABEK (Figure 6). In addition, in order to detect the urbanization gradient of landscape pattern, we conducted a series of analyses along both east–west and north–south transects from the National Monument (MONAS) located in the city center of the DKI Jakarta.
FIG. 5 LANDSCAPE METRICS USED IN THIS STUDY
FIG. 6 MESH AND TRANSECT FOR THE LANDSCAPE PATCH ANALYSIS
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With respect to landscape metrics, patch density (PD) indicates fragmentation, landscape shape index (LPI) indicates occupancy, and mean patch fractal dimension (MPFD) indicates complexity. Using these indices, an east‐ west transect analysis clearly indicated the differences in land use patch characteristics between Kab. Bekasi and Kab. Tangerang on the east and west suburbs of DKI Jakarta respectively (Figure 7). More specifically, while paddy lands were still dominant in Kab. Bekasi, the fragmentation of farmland had been advanced, along with extreme urbanization, in Kab. Tangerang. The shape index of forest area was more complicated in Kab. Tangerang than in Kab. Bekasi, although there was no clear difference observed in the farmland shape between these two districts. A north‐south transect indicated that the agriculture landscape still remained in an area between Kota Depok and Kota Bogor, and the fragmentation and complication of the landscape was not extremely advanced in that area compared to that found in Kab. Bekasi and Tangerang as of 2009. However, the degree of landscape fragmentation was even higher in that area than that located 60 km south. Therefore, future landscape degradation of the area between DKI Jakarta and Kota Bogor is anticipated. Thus, the present study could reveal the characteristics of the spatial distribution of land use and cover at a patch level using the landscape indices, information which could not be elucidated by simply totalizing areas.
FIG. 7 RESULTS OF TRANSECT ANALYSIS (E‐W AND N‐S DIRECTIONS)
The Potential Land Use Change Prediction Logistic‐regression analysis uses the occurrence and the probability of a certain phenomenon as objective variables and estimates the probability (p) of the occurrence of the phenomenon using explanatory variables. Formula (1) shows a logistic‐regression model applied in this study (Figure 8). Using this model, we attempted to sort out social and environmental factors to construct a potential land use prediction model. The rate of change in the area for urban land use (2001‐2009) was obtained as an objective variable from a time‐series land use/cover map, and social and environmental factors related to urbanization were used as explanatory variables (Table 2). Based on the predicted values obtained from the model, the potential urbanization map was created to identify areas at a sub‐ district level with a high potential for the conversion into urban land use. log
p 0 1 X 1 2 X 2 r X r (1) 1 p
0 = invariable r = coefficient of multiple partial correlation X r = each variable
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FIG. 8 ANALYTICAL FLOW OF LOGISTIC REGRESSION ANALYSIS TABLE 2 LIST OF EXPLANATORY VARIABLES
Factor
Variable
Unit
Geography
Distance from CBC
Km
Total roads length
Km
Population increasing rate (2000‐2008)
%
Elevation
m
Slope
degree
Forest area change rate (2001‐2009)
%
Agriculture area change rate (2001‐2009)
%
Urban area occupancy (2009)
%
Forest area occupancy (2009)
%
Agriculture area occupancy (2009)
%
Social
Topography
Land use
The variables finally selected using the stepwise method were the rate of change in the forest area, the rate of change in the agriculture area, the distance from the central business district (CBD), and the population growth rate (Table 3). The standardized partial regression coefficient indicated that the effect of the variable on the predicted value increased as the distance of the coefficient value from 0 increased. The rate of change in the forest area affected the predicted value of the potential urban region model the most. Areas with predicted values of the potential urbanization model larger than 0.5 were defined as potential urban areas and these were mapped (Figure 9). The land use was classified (threshold setting) into four classes using the geometric distribution. Consequently, in the central area of Jakarta, the population growth rate decreased and the potential for urban expansion was low (i.e., a doughnut phenomenon was clearly observed). In contrast, a high probability of urban expansion was indicated in Kab. Tangerang and Kab. Bogor, which are the suburbs of Jakarta (i.e., urban regions were presumed to have expanded further into the eastern and western suburbs of Jakarta).
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TABLE 3 RESULTS OF LOGISTIC REGRESSION ANALYSIS
Partial regression
Standard partial regression
Standard error
Significant probability
Odds ratio Exp(b)
‐1.232
‐7.08
0.32
0.000
0.292
Agriculture area change rate (2001‐ 2009)
‐1.237
‐4.64
0.46
0.007
0.290
Distance from CBC Population increasing rate Invariable
0.105 ‐0.038 ‐2.391
1.72 ‐1.48
0.04 0.02 0.87
0.004 0.030 0.006
1.111 0.962 0.092
Forest area change rate (2001‐2009)
Model x2 text p<0.01; hosmer‐Lerneshow test p=1.00; Discriminatory probability=95.2%
FIG. 9 POTENTIAL URBANIZATION BASED ON LOGISTIC REGRESSION ANALYSIS
The Inconsistancy between Actual Urbanization and Land Use Plan It is the fact that urbanization in JABODETABEK area is chaotic and has been disorderly expanding the urban land use into the suburban areas. The reality of process is in progress against the existing land use plan. Therefore, in this study, we attempted to identify the inconsistency between the actual urbanization and land use plan. For this purpose, an existing land use map was collected and the planning zones were aggregated to create a land use regulation map with zone A (Conservation Zone), B (Agriculture Zone) and C (Urban Zone). This map was overlaid on a land use/cover maps (2001‐2009), and then identify the urbanized areas according to the land use regulation level (Figure 10). The results showed that over 10,000 ha of agriculture land designated level B were actually converted in the urban area. Next, the land use plan map was overlaid on the potential urban expansion map to extract potential urban areas according to the land‐use regulation level. The ratio of potential urban areas was the highest in urban development areas designated zone C, and their area occupancy rate was 48.9%. The area occupancy rate of potential urban expansion areas designated zone B was 42.2% (i.e., many patches would be urbanized with high probabilities). When potential urban areas were examined according to the land use regulation level, in the nature conservation
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zone designated A, the high value areas are seen in the southern parts along the national road to the Puncak resort area. In agriculture zone designated B, the value was generally higher in western parts than in eastern parts, and was particularly high in the suburbs of Kota Bogor and Kab. Tangerang. In urban zone designated C, the value was high in many suburbs, particularly in the areas along expressways. Moreover, the high potential areas of urbanization were identified in Kota Tangerang, southern parts of Kab. Tangerang and Kab. Bogor.
FIG. 10 OVERLAY RESULTS OF POTENTIAL URBANIZATION AND EACH LAND USE REGULATION
Conclusions In JABODETABEK area, urban land use was rapidly expanded to the suburban areas, especially to Kab. Tangerang, Kab. Bekasi and Kab. Bogor where the traditional rural landscapes were widely distributed. Agriculture and forest lands were mainly converted into urban land use, and urban and agriculture landscapes were, consequently, extremely mixed. The analysis using landscape metrics revealed that the mixture of land uses and the fragmentation of agriculture landscapes existed at a patch level. The analysis using east‐west and north‐ south transects revealed that these spatial distributions were not homogeneous from the city center but were irregularly expanded and heterogeneous. By constructing the potential urbanization model using logistic‐ regression analysis, potential urban areas could be mapped while considering variables related to urbanization. By overlaying the potential urban areas on the land use plan map, the inconsistency between the existing plan and the future expansion of urban land use could be spatially delineated, and information useful for future land use planning administration could be obtained. REFERENCES
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