Lanzhou Urban Growth Prediction Based on Cellular Automata 1

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1 Lanzhou Urban Growth Prediction Based on Cellular Automata 1 Yaowen Xie*, Aigong Ma, Haoyu Wang Key Laboratory of West China's Environmental System (Ministry of Education), Lanzhou University, Lanzhou, 700, China * Corresponding author: xieyw@lzu.edu.cn. Abstract Lanzhou City, the capital city of Gansu Province and located in the geometric center of China's land territory, is a typical narrow zonal valley city in the west of China with the Yellow River going through it. It is also an industrial city with many large petroleum and chemical industries. Lanzhou is one of the important traffic hubs and freight transfer stations in northwestern China. The development of Lanzhou City is severely restricted by surrounding mountains so facing a series of problems such as environment pollution, traffic jam, unreasonable land use and sustainability. So, it is necessary to make urban expansion prediction to provide references for the urban planning department of this city. SLEUTH is kind of Model applies self-adaption Cellular Automata to model the urban expansion and land use, and it has been successfully used in predicting the growth of many metropolises all over the world. Taking remote sensing image such as MSS, TM, ETM and CCD as the main data sources and along with the maps of urban planning, topographic and socio-economic statistical, the spatial and attribute database of the study area was built firstly. Then the SLEUTH model was applied to rebuild the sprawl process and predicting the urban expansion in the future. The results show the Urban Area Index had a linearly increasing trend from 7 to 2025 and the pixel number will increase from to Urban Edges Index will grow nonlinearly and the pixel number will increase from 6.31 to Urban Cluster Index will increase rapidly from to by 2013, and then it will stabilize. The Mean Urban Cluster Index will gradually reduce to a stable status by The analyses indicate that the remaining land in Lanzhou for urban expansion is quite limited; urban expansion is mostly influenced by road and traffic situation; slope is a major limitation for urban expansion. In the future, for the development of Lanzhou, it need to strengthen and accelerate the construction of central city area, move administrative organizations out of the centre area, strengthen the modernization of urban traffic, support the development of minor towns around the city so they can share part of responsibilities of metropolitan Lanzhou City. Key words: Lanzhou City, Urban sprawl, Prediction, Cellular Automata, SLEUTH model 1 This study was supported by National Basic Research Program of China (973 Program, No. 9CB421306). I. INTRODUCTION Western Development is one of the strategies initiated by the government of China at the turn of the century to achieve modernization purpose. Since the Western Development having been launched, as a key regional city in the western region, Lanzhou City, the capital city of Gansu Province and the geometric center of China's land territory, achieved fast development both in economy and urbanization. However, due to the narrow valley landform, the urban expansion of Lanzhou is severely limited. Selecting Lanzhou City as empirical region to study urban expansion, there are two obvious advantages: one is Lanzhou is a typical valley city strongly impacted by landform, economic, political and urban planning so making it a natural testing ground for urban model; the other is the realistic urban development forecast of Lanzhou can provide theoretical basis for Lanzhou s urban planning. Lots of previous studies provide a reliable research data and foundation for this study [1-8]. Yong-Chun Yang et al. take Lanzhou and other valley cities as examples to research the theory and practice of urban expansion. Concept plan and Metropolitan Coordinating Region of Lanzhou is discussed in [9-15]. SLEUTH is a Model applying self-adaption Cellular Automata to model the urban expansion and land use, and has been successfully used in predicting the growth of many metropolises all over the world. Taking remote sensing image as the main data sources and along with the other data, this study uses SLEUTH model to rebuild the sprawl process of Lanzhou and predict the urban expansion in the future. II. OVERVIEW OF STUDY AREA Lanzhou is located in the intersection of three natural regions: eastern monsoon area, western arid and semi-arid area and the Qinghai-Tibet alpine area. It also is the largest diversified industrial city, the typical valley-city and the political, economic, cultural and transportation center in northwest area. Due to special factors such as valley-type topography, Lanzhou is facing a series of issues related to environmental, transportation, land use and sustainable development, it urgently need the government branch and experts put forward practical development program. Lanzhou consists of five districts: Chengguan, Qilihe, Anning, Xigu and Honggu, and three counties: Yongdeng, Yuzhong and Gaolan. Because the Honggu District and the three counties are far away from the main urban area of Lanzhou, this paper chooses

2 urban planned areas of Lanzhou as study area boundary to conduct study, which includes four districts i.e. Chengguan, Qilihe, Anning and Xigu (Figure 1). III. RESEARCH APPROACH This study includes the following aspects: A GIS and Remote Sensing Using GIS as the principal means and tools to do the data acquisition, providing input data for empirical research through interpretation of remote sensing and vector quantization, finally this study do spatial analysis for model output. B Integration of Multi-source Data The original data sources this study used are diversiform and their spatial resolutions are not uniform. All the projection coordinate system and spatial resolution of the data were unified using multiple source data integration methods, and then data pre-processing were conducted. C Simulation and Prediction of Cellular Automata Model In order to take full advantage of CA in urban growth simulation, SLEUTH model was used to simulate and predict the urban expansion process of Lanzhou. addition, some socio-economic statistical data were also used in this study. B Data Preprocessing Based on the ERDAS IMAGINE 9.1, the topographic map in 1984 was geo-corrected, and based on whom, the CCD images in 6 was precisely geometric corrected. Then the 6 image was used to correct the images in other years. Determine the study area according to Lanzhou town plans, and then use the study area to cut the corrected image, vectorise the boundaries of the new city in 1978, 1994, 1999, 3 and 6. Using and Lanzhou town plans which registered on the ArcGIS9.2 platform, extracting the urban plans and Lanzhou traffic map in 6 reference to Google Earth, electronic map of Lanzhou in 6, CCD images and field survey data; extracting the Lanzhou traffic map in 1978, 1994, 1999 and 3 from MSS images in 1978, relief map, Landsat TM images in 1994, 1999 and 3. Re-projection of the DEM data in order to make all the data have the same projection. The specific process is shown in Figure 2. Spatial data of Lanzhou Images in 1978, 1994, and 6 1:100,000 Relief map City plan 1:50,000 DEM Re-projection, resample Slope map, Shadow map Crop Vectorization Site exploration Visual interpretation Resample Urban areas of Lanzhou in 1978, 1994, 1999, 3, 6 Traffic roads plans of Lanzhou in 1978, 1994, 1999, 3, 6 Figure 1. Location of the study area IV. DATA SOURCES AND PROCESSING A Data Sources The raw data used in this study are as follows: MSS image in September 27, 1978; TM image in July 19, 1994; ETM image in July 18, 1999; ETM image in May 10, 3; CCD image of China-Brazil Earth Resources Satellite in August 21, 6; DEM with the resolution 30 m; paper topographic map in 1984 with scale 1: ; electronic map of Lanzhou in 7; Lanzhou detailed plans of second edition (1978-0) and third edition (1-2010) with the scale 1: In Figure 2. Follow chart of data processing C Establish of Model Input Data Layers SLEUTH is a model which simulates urban growth and land-use change by using adaptive cellular automata [16]. This model includes two sub-models: Urban Growth Model (UGM) and Land Cover Deltatron Model (LCD), and these models coupled tightly. UGM can run separately, LCD called and drive by UGM. Generating the data which meet to the model runs by ArcMap, and establishing the temporal database of Lanzhou urban expansion. As this paper focuses on the study of urban spatial expansion, dataset of input does not include

3 land-use layer, therefore the LCD didn t activated. The processes of each data layer are as follows: 1) Urban Layer Vector data of urban areas were extracted from remote sensing images from the historical period of the study area (1978, 1994, 1999, 3 and 6), and then transformed into raster data which had different spatial resolution by ArcGIS9.2, these raster data were generated into urban / non-urban (expressed by 255, 0) grid maps which were binary classification (Figure 3). Finally, these grid maps were converting into GIF format. These urban areas data were used for model calibration as the data of control layer in different years 年 3 6 Figure 4. Transportation of year for calibration 3) Slope Layer Slope map (Figure 5) was generated directly from the DEM, and expressed as a percentage, and then changed into integer value through reclassification. Due to the topographic slope of this study area would not change in the short term, the model will remain unchanged in the stages of adjustment and prediction. 4) Hillshade Layer Hillshade layer, as the background image of output model image, has no effect on the operation of model. Like the slope map, hillshade layer is generated from DEM. This layer can be embedded by water bodies such as river to enhance the visual effect (Figure 6). 6 Figure 3. Urban extents of for SLEUTH model in Lanzhou 2) Transportation Layer Transportation layer was extracted by remote sensing technology; images were enhanced to highlight linear feature based on the planning maps and remote sensing image, including edge enhancement, high-pass filtering. Refer to Lanzhou electronic map and Google Earth Maps, depicting the road layer data by manually track digitization after artificial visual interpretation of roads, then the results of digitization was stored as shape format, format conversion was the final step. Because of the spatial resolution is 30 meters, national roads, provincial roads and urban roads can be distinguished easily, but it is difficult to distinguish a normal street. The principle of road digitization in this paper is: only the national roads, provincial roads and urban road were digitized. Due to different road created different forces on the urban growth, they were assigned with different weight values, such as 100, 50, 25, 0 (Figure 4). Figure 5. Slope of study area Figure 6. Hillshade of study area Figure 7. Excluded for calibration

4 5) Excluded Layer This layer was generated from two main sources: the water bodies extracted from remote sensing images by remote sensing techniques; the area which can t take as construction land extracted from city plan. Registration had been done at the first of data acquisition, and then did manual digitizing on non-construction land; eventually these data would convert to Grid format. In the registration process, urbanized area and the area which could be urbanized were assigned to zero; green space were assigned to 50, so it has a certain chance to be urbanized. Area whose slope was too large was assigned to 100, it indicated that the area can t be urbanization, and should be changed into GIF form for data import (Figure 7). V. LANZHOU URBAN GROWTH PREDICTION BASED ON SLEUTH MODEL A. Model Calibration Clarke had a detailed description of the model calibration process; this paper selects Lee-Salee as calibration scheme. To take into account both simulation accuracy and actual operating efficiency, this study using 120m, 60m and 30m resolution in the coarse calibration, precision calibration and final calibration stage separately. The parameter combination which comes from each stage s calibration was analyzed, and the ultimate combination was concluded. B. Lanzhou Urban Growth Prediction from 1978 to 6 Calibration of the SLEUTH model was constantly adjusted its index and the spatial extent of coefficient in order to match the real form and area of urban. With a view to the changes of index behavior, coefficient behavior and spatial extent of coefficient, urban growth simulation range maps were generated (Figure 8). Urban area in 1994 Urban area in 1999 corresponding statistical document were handled and analyzed accordingly (Figure 9). Urban areas in 6 Urbanization probability 50%-60% Urbanization probability 95%-100% Urbanization probability 60%-95% Figure 9. Forecast of urban spatial expansion in Lanzhou during future 20 years VI. RESULTS AND DISCUSSION From figure 10, the Urban Area metric kept linear growth trend throughout the forecast year, it had pixels, and this number increased to Urban numbers of Edges metric had non-linear growth (Figure 11), this metric was 6.31 in 7, by 2025, and it has reached Urban numbers of clusters metric showed a constantly increasing tendency (Figure 12), this trend can divided into two stages: the first stage is from 7 to 2013, in this stage, urban number of clusters metric increased quickly from to , the second stage is from 2014 to 2025, urban number of clusters metric grows steadily during this period. Urban mean Cluster size metric had experienced a process that gradual decrease to a stable (Figure 13), this process also can divided into two stages, the metric decreased quickly from 7 to 2013, it tend to smooth and had a slight increase from 2014 to As time goes on, urban doesn t expend at the steep slope, most of new urbanization pixels grow at the city boundaries, the area of urban enlarge constantly. Urban area in 3 Urban area in 6 Figure 8. Urban expansion simulations from in Lanzhou C. Urban Expansion Simulations of Lanzhou Consider that the urban expansion simulations were based on the parameter settings in initial year (6), in the simulation stage, the best coefficient combination was selected; initialize the operation of SLEUTH model s prediction stage. As the topographic constraint in Lanzhou is comparatively large, the urban expansion area is also very limited relatively, forecast period of Lanzhou urban growth was before Monte Carlo iterations was set as 100, probability map of urban growth and the Area Figure 10. Urban area(pop)metric

5 Edges Clusters Clusters_size Figure 11. Figure 12. Figure 13. Urban numbers of edges metric Urban numbers of clusters metric Urban mean cluster size metric Lanzhou urban growth has obviously been limited by the terrain condition. Construction land s growth in Xigu District and Yantan which in the middle of Chengguan District is quite rapid, the area of growth is quite large, this is because the Xigu District of urban spatial structure is not compact enough, topographic constraint is not obvious, there still has some space for urban growth, this also embodied on the topographic map; southern of Chengguan District, North of Xigu District and Northern of Anning District, the area grow quite slow because of topographic constraint. Despite many years of simulation, urban expansion area is still very limited, thus it can be seen that urban expansion is severely curtailed. On that account, Lanzhou need to expand urban development space, on the other hand, it needs to expand the three-dimensional space actively and to develop the high-rise building for improving the building plot ratio. If left unchecked, the scope of Lanzhou urban will expand constantly, building density in the downtown will also be increasing, and thus the pressure brought to the urban will be growing. This will inevitably bring about a large number of urban problems and seriously affect the living environment of human beings. Therefore, development of the city must make a reasonable planning to ensure sustainable development. REFERENCE [1] X. Xian, Environmental changes of historical periods in the Loess Plateau of Gansu, Gansu Social Science, 1982, 2: [2] C. An, The analysis of location pattern and advantages of Lanzhou City Commercial Center, Geographic Research, 1990, 9(10): [3] H. Mao, et al, Lanzhou city urban development planning, Lanzhou University Press, [4] Z. Zhu, Development course, status and function of Lanzhou, Urban studies of Sino-Western, [5] J. Chen and Z. Ye, Regional structural characteristics and rational development of Lanzhou, Economic Geography, 1996, 16 (3): [6] A. Wang, et al, Land use planning study on Lanzhou city, Lanzhou University Press, [7] A. Wang, et al, Analysis on the characteristic index of land-use in man-earth relationship, taking Lanzhou as a case, Economic Geography, 1999, 19 (1): [8] H. Niu, Y. He, and W. Zhang, Land grading and evaluation study of Lanzhou, Lanzhou University Press, [9] Y. Yang, Formation and development of valley cities in the western China, Economic Geography, 1999, 19 (2): [10] Y. Yang, The research on the developing model of Lanzhou a typical valley basin city in the northwest China, Human Geography, 0,15 (1): [11] Y. Yang, On the urban development and urban spatial structure of valley cities in the western China, Lanzhou: Lanzhou University Press, 3. [12] Y. Yang, Concept planning study in Lanzhou, Lanzhou University Press, 4. [13] Y. Yang and C. Meng, Study on spatial evolvement and mechanism of urban residential district in China sine 1949 a case study of the valley-basin city: Lanzhou, Human Geography, 5,120 (5): [14] X. Dong, L. Liu, and B. Zhang, Study on land use change based on RS and GIS in the metropolitan coordinating region of Lanzhou, Journal of Lanzhou University, 5, 41(1): [15] X. Dong and Z. Zhang, A study of the possibility and strategy of the developing in the metropolitan region of Lanzhou, Advance in Earth Sciences, 6, 21(3): [16] C. K. Dietzel and K. C. Clarke, Toward Optimal Calibration of the SLEUTH Land Use Change Model, Transactions in GIS, 7, 11(1):