Cellular Automata Algorithm for Spatial Modelling of Urban Physical Development in Dubai, United Arab Emirates Akbar Muammar Syarif, M. Ulul Lizamun Ningam, Fithrothul Khikmah Departement of Geography Information Science, Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Jalan Kaliurang, Bulaksumur, Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
[email protected] Abstract Dubai is one of the most populous cities in the world that has rapid urban development in the last several years. Urban development identified from built up area development in Dubai. Landsat 7 ETM+ imagery (acquisition date in 2003) and Landsat 8 OLI (acquisition date in 2016) is useful for extraction an urban development data. Multispectral classification use to produce the spatial data of built up area from two periods (2003 and 2016). Urban development identified from monitoring the spatial data of built up area in two periods. Cellular automata used for predict built up area development in 2030 consider of urban development trend in several years before. The model also considering the driving factors of urban development, such as slope, primary roads, secondary-tertiary roads, and public facility. Result of spatial analysis present the spatial trend of urban development in 2003 – 2016 direct to south-western from city centre. Spatial statistical analysis explains than slope factor has a highest influence of urban development. Spatial modelling of urban development explains the development in 2030 is centralized. It caused by sparsely the spatial distribution of public facilities in suburban area. Keywords: Built up area, urban development, Landsat, cellular automata, Dubai
INTRODUCTION Regional development can identified using expansion of built up area development. The development brings any positive and negative impact. Understanding of urban physical characteristics needs to avoid the negative impact of physical development (Yunus, 2005). Control of urban regional development needs to consider in arranging the urban and regional planning. The problems will appear when the urban and regional planning less in optimizing the spatial data such as satellite imagery. This data can be used to identify spread of urban/cities physical development and the directive of physical development trend. Besides that, the data can be extracted to urban morphology, urban space structure, urban land cover, built up area development, directive of urban physical development, and the expansion of development. Spatial data have a function to detect the urban elements so can be used as a trigger of decision making in regional planning. The planning will be complex in the rapid urban development, such as Dubai, Uni Emirat Arab (UEA). Early 1980, many institutions predict that Dubai’s economy will not survive if the focus of business sectors is only in crude oil. Government and corporation need to think about investment in other sectors, such as real estate investment that to be a backbone of Arab’s economic (Amin, 2014). This is triggered the increasing of built up area. The control needs to be done to avoid the negative impact of development. The controls such as minimize, limit, directive, and give recommendation the directive of land development which considering the environmental condition.
This study aims to know the urban land cover changes, the intensity of built up area growth, and the prediction of urban physical development in Dubai. This study also did to give recommendation the area of built up land in the future. The aims can be obtained by integrating remote sensing and Geographic Information System (GIS). Urban land covers interpretated by pixel based classification using change analysis based on multitemporal data. This study gives knowledge about distribution wide and diective of urban physical development in thirteen years, from the 2003 until 2016. Result of this prediction are a spatial model of urban physical development in 2030. This model can be used to control the urban management, helpful to minimize the uncontrol development, and support the equitable development in the future.
LITERATURE REVIEW Urban The city is an area that has non agriculture activities with a function as the places of government services and social-economic services (Anonim, 1999). The city showed by domination of built up area that remark by relation of public services and economy with the density of activities and economy. From the geographic perspective, a city is a cultural landscape that formed by natural and non natural elements with the high of population center tendency. This area has different characteristics that very heterogen. Considering the perspective of urban physical morphology, a rural and urban often evident by comparison of agriculture and open land. The high density of settlement and complex of road network pattern in a compact settlement (contiguous)
relatively higher than the unit of rural settlement around them. It is indicate the existing of urban physics (Yunus, 2005). Urban physical development that occurs in a region can influence the land development, see from the urban physics form that similarity as road network system and built up blocks (Yunus, 2000). The rapid of urban physics growth influence by four factors (Suhadi et al, 2002), such as, 1. Concentration of citizens and their activities 2. Accessibility from the center of activities and cities (central business district) 3. Road network and transportation tools, and 4. Arbitration, refers to distance that connects a region with the center of activities and the higher services. Remote Sensing Remote sensing is an application technique that developed by a famous scientist to help tap the information without direct contact the object (Lillesand et al, 2004). Withdraw the information in the earth surface didn’t contact in the field. Object field in geography sciences is the all phenomena in the earth surface. The perspective of geography sciences sees the remote sensing as the sciences, knowledge, arts, and technique, in collected the data from the earth surface object without directly contacted by the object. Acquisition the data from the earth surface pass through the roles of the sensor and a special vehicle. Background the use of remote sensing imagery (Sutanto, 1986) such as, 1. Imagery represents the object, region, and symptom of the reality of the earth surface. 2. Imagery represents the complete of the object, region, and symptom in the wide and permanent area. 3. Object that recorded can produce in three dimensions from the stereoscopic perspective. 4. Imagery represents the condition of a difficult region to travel with the terrestrial methods so make easy the field survey. Remote sensing process through the specific steps to obtain the goals in remote sensing use. Some the specific steps continuous such as, physical object, sensor data, information extraction, and application (Campbell et al, 2011). Physical object is an object in the earth surface that to be many studies on the remote sensing roles. Satellite imagery provides information that extracted from the sensor data as the result of processing. The information can be supplied by some institutions in the decision making. Geographic Information System (GIS) Geographic Information System (GIS) provides savings, processing, and analysing the simultant
data system, so getting information about spatial aspect (Prahasta, 2005). Data that used in GIS operation are a spatial data with geographic reference and coordinate system. Completion of SIG such as geodatabase as to be important because is a basis of data that integrated and be a sources of data. This data can access in some application that provides for information need and spatial analysis (Hartoyo et al, 2010). The characteristic of GIS, such as 1. Input data subsystem, receive and process spatial data from many sources and provide transformation process for spatial data with different saving format. 2. Saving and calling data subsystem, possible to call, editing, and updating the spatial data. 3. Manipulating and analyzing data subsystem, providing data roles, grouping and regrouping, estimating the parameters and resisted, and modelling function. 4. Reporting subsystem, providing all and part of the database in table, graph, and map. (Demers, 1997) Cellular Automata (CA) In this era, cellular automata present at the optimal methods that applied in remote sensing studies specially for simulation of satellite imagery processing. The pixel population changes can occur based on space and time. In every iteration, all pixel checked and give a rule that applied in transition function in every pixel, calculated around the neighbourhood to changes the pixel population (Leguizamon, 2006). Cellular automata experience evolutionary process is because a pixel usually changes the pixel population in the different iteration. Then, cellular automata be reputed as the optimal facility in remote sensing applied, specially for modelling simulation. Early Cellular automata in use in the enhancement process of imagery and identify the object boundaries. Cellular automata has been developed for spatial modelling of the environment such as land dynamic modelling (Leguizamon, 2006).
METHOD This study area is Dubai as the one of the greatest cities in the world. Dubai has rapid urban development in last several years. Dubai use as the study area as the city see from their phisycal characteristics, not see from the administrative. This cities was choosen because one of the big cities in the world that have high of built up area development.
Figure 1. Study area Materials that used in this study are, (1) Landsat 7 ETM+ imagery acquisition in May 2003, spatial resolution 30 meters; (2) Landsat 8 OLI acquisition in September 2016, spatial resolution 30 meters; (3) Digital Elevation Model (DEM) data; and (4) Spatial data of roads network and public facilities. Correction level of the sattelite imagery in condition on geometric correction, so didn’t need to do rectification process. In this study, radiometric correction is not used because classification based on spectral pattern distribution not in spectral value. The quality of imagery see from the presentation of cloud cover. Landcover Data Extraction Spatial data become important in the study. Landsat 7 ETM+ (acquisition date in 2003) and Landsat 8 OLI (acquisition date in 2016) extracted to get land cover information in the urban area of Dubai, Uni Emirates Arab (UEA) in path 160 and row 43. Classification of land cover in the first level divided into four class, include water, vegetation, open land, and built up land. But, in this study classification of land cover was conducted into built up and non-built up area. Spatial data extracts using multispectral algorithm classification of support vector machine, it was maximum likelihood. This algorithm was applied to processes of land covers classification based on pixel analysis. Election of Region of Interest (ROI) becomes an important step in the multispectral classification.
Urban development analysis and trend analysis Pattern and spread of spatial land use change identified using multitemporal approach that applied in two spatial data with different time periods. The spatial data that used in this study extract to land cover in 2003 and 2016. Monitoring of land cover change did by Land Change Modeler of TerrSet software using two multitemporal data of land cover have as a base raster. The result of multitemporal analysis was spasial data of land cover spreading. Spatial trend of the physical urban development can be seen from the wide of new built up area based on the direction of development. The trend analysis automatic identified using TerrSet software. Data of built up spreading in two periods were the based of spatial process in development trends. The input of land cover that will be anlyze the development did by giving input map spatial trend from non built up area to built up area. The result of trend analysis show the development of built up area in all direction with different intensity. The most rapid development of built up area occured from the center of city to the south west influence by Abu Dhabi City as the capital city of United Arab Emirates. Urban physical development in the north west occured more slowly influence by Persia Bay as the barrier of development. Determination and recommendation urban development priority in the future needs to achieve the equitable development of the city that followed by equitable of community services. The most proper recommendation for urban development planning are in the south east and north east of the city center. The physical condition of this area are composed by sand materials like in the south west, south, and east that more developed before. Urban Development Prediction This study conducted to make spatial model of Dubai’s urban physical development in 2030. Year input did in the prediction date. Driving factors of physical urban development was a primary input of the prediction process of land cover spreading in 2030. The factors such as slope (extracted from DEM), primary roads, secondary-tertiary roads, and facities. Euclidean distance analysis in the primary roads, secondary-tertiary roads, and facilities do to know the distance categories of each object that become the driving factors of urban physical development. It is done at the base of assumption about the area are more closed with the object have potential to more development. This analysis based on Marcov Chain principle.
RESULT AND DISCUSSION Landcover Data Extraction Dubai as one of the big cities in the world estimate have quick built up development. Identification of built up area did by multispectral classification using support vector machine applied in Landsat acquisition in 2003 and 2016. The result of this classification shows the spatial spreading and wide of built up area. The process resulted in data about very significant of built up area changes in Dubai. Spatial pattern of built up area in 2030 is not so different with the existing built up area in 2016. The visible changes that occured are the expansion of the built up area and increasing of built up density. This condition can occurs in the center and surrounding of the city centre. Increasing the density of the city may be occurring that influence by accessibility of roads and public facilities. The wide of built up area in 2003 to 32,385.1 hectares and increase to 94,875.9 hectares in 2016 (Figure 2). Built up area of Dubai in 2013 has linear pattern following the shoreline. Settlement patterns tend to be linear because the existence of the sea in the west Dubai as a barrier of urban development. The development was not sporadically occured in any direction influenced by sand materials that composed the land. The land condition caused appearing of some obstacle if the area developed for human activities. Sand materials can not catch the waters so the availability of soil water difficult to achieve (so deep). Besides that, domination of sand materials potential to increase the temperature in an area. Soil moisture did not find caused the radiation of high temperature in the earth can not absorb so potential to increase the temperature around the area. It is a limit of human activities because temperature is one of the indicators of society pleasure. a
b
Non built up area to built up area
Figure 3. Spatial pattern of built up area change from 2003 to 2016 The result of spatial trend analysis show that built up land development occurs in all direction with different intensity. The most rapid development of built up area occured from the center of the city to the south west influence by Abu Dhabi City as the capital city of the United Arab Emirates that located about 122 kilometers from Dubai (Figure 4). The region between both these cities dominated by open land that potential development to be built up area because has good access from two big cities in the United Arab Emirates. Urban physical development in the south west relatively lower that the others. It is caused by obstacle barriers of urban development, such as sea waters, Persia Bay. Urban developed in the north and north east reputed not so optimum because this area has been influenced by Sharjah Citiy development.
Figure 2. Spatial data of (a) built up area in 2003; (b) built up area in 2016 Urban physical development and trend analysis Identification of urban physical development will be optimal if supported by land cover change analysis in a time periods. Land use conversion from non built up area to built up the area in 13 years occured in behind the built up area that exist in 2003 and the beach area that now become a landmark of Dubai (Figure 3).
Figure 4. The result of spatial trend analysis Determination and recommendation urban development priority in the future needs to achieve the equitable development of the city that followed
by equity of community services. The most proper recommendation for urban development planning is in the south east and north east of the city center. The physical condition of this area is composed by sand materials like in the south west, south, and east that more developed before. Urban physical development into the south west can not endure because the important connection between Dubai and Abu Dhabi. This connection needs to trigger the development in regional scale so the appearing of new activities centers can not be avoided. Urban physical model in 2030 Spatial model of urban physical development can accomodate by cellular automata algorithm. Determining of prediction date and driving factors of urban physical development are were the important step to produce the spatial spreading of built up area in the future. Driving factor that have high influence the urban physical development, such as slope, primary road, secondary-tertiary roads, and facilities.
a
d Figure 5. a) Slope, b) Primary street, c) Non primary street, d) Public facility. Slope is the most driving factors tha influence the physical urban development eith relevance weigh reached 0.86. Public facilities is the littles of influental factors with the relevance weigh about 0.4. Slope was the physical component of land that influenced for activities in a land. The building up have requirement in the certain slope. The high different of the influence rate occured because slope is the natural aspects that very limited the built up development in the not suitable slope. Road networks influence because one of the accesibility resources. Facilities as the fulfillment of human necessary also very influnced the built up area development. The land that have potential to develop the buil up area was land that have associated by public facilities.
Figure 6. Influence rate of each factors in urban physical development Spatial model of urban physical development did base on probability change. Value of probability measure by influencing rate of driving factors of urban physical development. The spread of roads network and facilities was not smooth, caused the minimum change of built up area change because the limitation of accessibility and facilities. Roads network and facilities are centered in the certain area, dense of built up area.
b
c
Table 1 Nilai probabilitas perubahan Lahan Non lahan terbangun terbangun Lahan 0.9323 0.0677 terbangun Non lahan 0.0877 0.9123 terbangun Nilai probabilitas perubahan lahan terbangun menjadi lahan non terbangun lebih besar dibandingkan nilai probabilitas perubahan lahan non terbangun menjadi lahan terbangun. Hal
tersebut dapat terjadi mengingat persebaran jaringan jalan dan fasilitas yang kurang merata menyebabkan kemungkinan lebih kecil untuk perubahan lahan non terbangun menjadi lahan terbangun di area-area yang kurang tersedianya akses berupa jalan dan fasilitas publik. Sebaliknya keberadaan fasilitas dan jaringan jalan terpusat pada area yang telah berupa area dengan lahan terbangun padat. Kepadatan tersebut membuat nilai perubahan dari lahan non terbangun ke lahan terbangun semakin kecil karena peluang tersebut dibatasi oleh ruang yang tidak tersedia lagi untuk pendirian lahan terbangun.
Figure 8 shows the estimation of land cover condition in 2030. Spatial pattern of will build up area in 2030 is not so different with the existing built up area in 2016. The visible changes that occured are the expansion of the built up area and increasing of built up density. This condition can occur in the center and surrounding of the city centre. Increasing the density of the city may be occurring that influence by accessibility of roads and public facilities. The model of urban physical development can be used as the reference of the consideration and urban planning in medium-long periods. Management of urban regional development is helped by the availability of model prediction data. The availability of planning reference, such as urban development trend, driving factors of built area changes, and the result of built up area in 2030 can appearing the effort of management and planning or urban to be more systematic, comprehensive, affective, also efficient in energy, time, and cost.
Figure 7. Spatial data of built up area development probability The result of model show that the most possibilities change from the non built up area to built up area untill 2030 will happend in the flatslightly slope, high of road networks, and public facilities exist. The high of probability value have high possibilities in changes of non built area to built up area, and the other way. The area in the flat slope that have low changes becase limited in the road networks and minimum of public facilities.
Figure 9. Comparison of spatial distribution of buit up area in 2016 (left) and 2030 (right)
CONCLUSION
Spatial model of urban physical development in 2030 was needed to consider the development trend of the city for the previous year and the driving factors. A probable changes in non built up area into built up area were a major step in the study of the urban physical development based on the spatial data. High of probability value occurs in areas with availability of access, connecting with roads network and public facilities were adequately backed slope conditions are flat, spread across the center of the city and surrounding areas. It triggers the urban physical development was going to happen more inclined to the increase of building up density, while urban expansion will also occur but with lower intensity than the increasing of urban density. Built up area Non built up area Figure 8. Spatial model of urban physical development in 2030
REFERENCES UU Republik Indonesia Nomor 22 Tahun 1999 Tentang Otonomi Daerah. Amin, M. 2014. Ini Alasan Mengapa Dubai Menjadi Kota Sangat Kaya diakses dari www.merdeka.com pada tanggal 25 Oktober 2016 oleh Akbar Muammar Syarif Bintarto, R. 1977. Pengantar Geografi Kota. Yogyakarta : U.P.Spring Campbell, James B., dan Wynne, Randolph H. 2011. Introduction to Remote Sensing Fifth Edition. New York: The Guilford Press. Demers, M.N., 1997, Fundamental of Geographic Information Systems. New York: John Wileys & Sons, Inc. Hartoyo, G. Manjela Eko, dkk. 2010. Modul Pelatihan Sistem Informasi Geografis (SIG) Tingkat Dasar. Bogor: Tropenbos International Indonesia Programe. Lillesand et. al., 2004. Remote Sensing an Image Interpretation, 5th Edition. Michigan University: John Wiley. Leguizamon, S. Modeling land features dynamics by using cellular automata techniques. In: Proceedings of the ISPR Technical Comision 7 MidTerm Symposium “From pixels to Processes”, Enschede, The Netherlands, pp. 497–501 (2006). Prahasta, E. 2005. Konsep-konsep Dasar Sistem Informasi Geografis. Bandung: Penerbit Informatika. Purwantoro Suhadi, Hadi Saiful B. 2002. Study Perubahan Penggunaan Lahan di Kecamatan Umbulharjo Kota Yogyakarta tahun 1987-1996 Berdasarkan Foto Udara. Yogyakarta. Sutanto. 1986. Penginderaan Jauh Jilid I. Yogyakarta : Gadjah Mada University Press. Yunus, H. S. (2005). Manajemen Kota. Yogyakarta: Pustaka Pelajar. Yunus, H. S. (2000). Struktur Ruang Kota. Yogyakarta: Pustaka Pelajar.
