URBAN GROWTH SIMULATION: A CASE STUDY

URBAN GROWTH SIMULATION: A CASE STUDY V. Swathi 1, S. Sai Veena 2, K. Srinivasa Raju 3 1 Research Scholar, 2 Under Graduate Student, 3 Professor * Corresponding Author; Email: p201244029@hyderabad.bits-pilani.ac.in. 1 Department of Civil Engineering, Birla Institute of Technology and Science, Pilani- Hyderabad Campus, Hyderabad-500078, India. 2,3 Department of Civil Engineering, Birla Institute of Technology and Science, Pilani-Hyderabad Campus, Hyderabad-500078, India. Abstract In the present paper, LanduseSim 2.0 has been explored to simulate urban growth pattern in a stretch in Charminar, Hyderabad. The software is based on Cellular Automata (CA). The CA algorithm is used to simulate spatial-temporal patterns of great complexity. LanduseSim 2.0 is found to be useful for the chosen stretch and can be explored to simulate urban growth patterns of similar nature. Keywords: Urban, Cellular Automata, LanduseSim 2.0. INTRODUCTION Urbanization has been increasing drastically from past decade throughout the World. People are migrating from villages to cities for livelihood, which is increasing the urban population and is likely to increase in future. Over a period of time, the outskirts of the city are also transforming into urban areas. Accessibility and availability of resources also affect an area in transforming into urban creating major land transitions. But, the urban growth pattern has always been a complex phenomenon to town planners. It is difficult to predict the pattern of urban growth as it is affected by several factors which are difficult to model. In the present paper, LanduseSim 2.0 software has been explored to simulate urban growth patterns for a stretch covering the area from Umada Sagar to Durga Nagar, Charminar, Hyderabad. It comes under Zone 5 of Greater Hyderabad Municipal Corporation (GHMC). The subsequent sections describe the literature review, software, study area, results and discussion followed by conclusions. 1

LITERATURE REVIEW Samat et al. (2011) integrated Geographical Information System (GIS) with markov-cellular Automata (CA) to analyze and forecast land use pattern map of 2020 for the case study of Seberag Perai region, Penang State, Malaysia. They found that major urban development in 2020 was towards the southern districts. The heavy vegetation in Northern districts was the constraint to urban growth. Rajendran and Kaneda (2014) simulated land use/cover change maps for urbanization in Chennai, India. They used GIS and CA. They acquired land satellite data for three decades (1989, 2000, 2012) and digital elevation map for present with 30m resolution. They have classified maps using supervised classification for four land use classes. They created transition probability matrices for all three decades and compared them with each other. They found significant increase in urban area and decrease in barren land. Memarian et al. (2012) validated markov-ca for simulating land use and cover changes at Langat basin, Selangor, Malaysia. They performed the validation using metrics, allocation disagreement and quantity disagreement. They have used data from 1990-1997 for validation and concluded that performance of markov-ca is dependent on uncertainties in the source data. Clarke and Gaydos (2014) have applied GIS and CA to two urban areas, namely, San Fransico and Washington. They analyzed results of both areas and observed that the model could generate long term land use pattern maps for both the areas for 2100. They found the predictions appeared useful for urban planning. Hedge et al. (2005) applied CA and Neural networks to carry out simulation for settlement growth in a hypothetical case study. They tried different sizes and shapes of neighborhood, land use parameters and carried out simulations using traditional CA and CA along with neural networks. It is concluded that CA with neural networks is more appropriate in predicting settlement growth compared to traditional CA. LANDUSESIM 2.0 LanduseSim 2.0 has been developed by Department of Urban and Regional Planning, Sepuluh Nopember Institute of Technology, Indonesia (www.landusesim.com). The software does massive simulation for various land use classes and used to simulate spatial temporal patterns of urban growth. The software is based on CA algorithm, which is in-built. It generates urban growth pattern in the form of maps for various time steps defined by the user and simulates for various land use classes like urban, vegetative, water bodies, barren and saturated areas. LanduseSim requires GIS software for initial data preparation. Brief discussion on cellular automata is as follows: The algorithm generates various patterns of a complex system using simple limited set of rules. A typical CA consists of four primary components: cells, status, neighborhood and transition rules. CA model assumes an action space (usually a grid), a set of initial conditions and a set of behavior rules. The rules are applied to the initial boundary conditions and iterations take place. 2

Next generation is simulated from the present state of the complex system CA has been improved especially in expansion of transition rules (Wang, 2012) STUDY AREA The study area chosen is stretch in Charminar, Hyderabad. It comes under zone 5 of GHMC. The area covers water bodies, namely, Umda sagar, Palle cheruvu, Yerra kunta lakes. The total area covered is 36 Km 2. The Google Earth Pro image of the study area is presented in Fig. 1. Fig. 1. Google pro image of study area Source: Google Earth Pro RESULTS AND DISCUSSION Initial Data Preparation The study area has been identified on Google Earth Pro by giving the latitudes and longitudes of desired locations. The latitudes and longitudes are saved to my places in Google Earth Pro. The map of the study area for the year 2014 has been downloaded and saved in jpeg format. The map is then fed into ArcGIS. The map is classified using supervised classification, among various classification options available (Fig. 2). The supervised classification has been performed with required number of classes defined by the user. The major road network in the study area has been identified and digitized in ArcGIS to create shape file of the road network. Settlement map has been developed by creating polygons of the urbanized residential and 3

complex areas. Euclidean distance maps for roads (Fig. 3) and settlement map (Fig. 4) are also generated. This information of road network is required as it is expected that further urbanization will take place near to the major road network due to the accessibility in the near future. Settlement map will enable to know the present urban area in the locality. All the maps are converted into raster format and clipped to an extent that the rows and columns are same. The land use map, Euclidean roads and Euclidean settlement are then converted to ASCII format. These ASCII files are inputs to LanduseSim 2.0 for carrying out simulations. Fig. 2. Classified map showing various land use classes. Fig. 3. Euclidean map for roads Fig. 4. Euclidean map for settlements 4

Procedural Steps The ESRI ASCII maps are imported into LanduseSim as image file (TIFF). The existing data are customized to Integer or Float format/decimal (choose float if not sure). Then, they are saved to TIFF format. The imported maps are checked using preview grid and number of grids are calculated in each land use map. The Euclidean distance data is then converted to fuzzy set memberships with a range of real numbers. In the next step, the land use maps of the potential expansion with overlaying fuzzy maps are given appropriate weights. Weights are given to various maps. This step is performed for each type of land use, to map the potential expansion of land use simulation. A Neighborhood filter 3 3, operates as a sum function, is used in the simulation. A probability matrix with null values is used. A conversion value of 150000 is given, that represents the possibility of a land use change into another land use. By providing conversion value, the value of the original adjacency will be distinguished by looking at land use changes before. After creating neighborhood filter, transition rules are prepared. The required input data are land use/code, growth, suitability map, dynamic constraints, and change conversion probability. The start and end year are used for simulation. Start date is used as the baseline for land use projections. Sequences of iterations are run and land use map of 2100 is produced. The final image can be viewed when the progress is 100% and map can be viewed in ArcGIS after processing. The difference between the original map and the simulated map can be identified clearly. In the present study, only urban land use class is simulated. It can be inferred from the below original map of the year 2014 (Fig. 5), blue color indicates water bodies and white color indicates urban area (built-up area). The second image is the simulated image of LanduseSim 2.0 for the year 2100 (Fig. 6) where the white color indicates the built-up area. It can be visualized from Fig. 6, that the water bodies are decreasing and built-up areas are increasing. A null probability function was given to the vegetation and barren land, so they remained the same else they could have changed. This inference is based on the chosen input functions such as neighborhood filters, probability functions, transition rules, land use classes and their weights, and may vary for different input functions and various locations. Fig. 5. Land use map of 2014 Fig. 6. Land use map of 2100 5

CONCLUSIONS The present study analyzes land use pattern for year 2014 and 2100. It is observed that built-up area is increasing which may create land transitions. It is relevant to note that the results emanated from the present paper are based on the chosen input functions such as neighborhood filters, probability functions, transition rules, land use classes and their weights, and may vary for different input functions and various locations. However, methodology remains same which is the main focus of the present study. ACKNOWLEDGEMENTS This work is supported by Information Technology Research Academy (ITRA), Government of India under, ITRA-Water grant ITRA/15(68)/Water/IUFM/01 dated Sep 20, 2013, Integrated Urban Flood Management in India: Technology Driven Solutions. The first Author is thankful to Raya Budiarti, LanduseSim Representative for clearing queries, whenever approached. Authors would also like to acknowledge "LanduseSim v.2.2" team for providing us with trial version of LanduseSim 2.0. We thank Google Earth Pro team for providing free access to the website. We are grateful to Ms. Sumasri, Mr Ravinder, Ms. Santoshi of GHMC, Hyderabad and Mr. Ravinder, Voyants Consultancy, Hyderabad for providing the valuable suggestions and all the necessary information. REFERENCES Clarke, K. C., Gaydos, L. J. (1998). Loose-Coupling Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7), 699-714. Hegde, N. P., Muralikrishna, I. V., Chalapatirao, K. V. (2005). Settlement Growth Prediction Using Neural Network and Cellular Automata. Journal of Theoretical and Applied Information Technology, 4(5), 419-428. Memarian, H., Balasundram, S. K., Talib, J. B., Sung, C. T. B., Sood, A. M., Abbaspour, K. (2012). Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia. Journal of Geographic Information System, 4(6), 13. Rajendran, V., Kaneda, T. (2014). A Simulation of Land Use/Cover Change for Urbanization on Chennai Metropolitan Area, India. Proceedings Real CORP. Samat, N., Hasni, R., Elhadary, Y. A. E. (2011). Modelling Land Use Changes at the Peri- Urban Areas Using Geographic Information Systems and Cellular Automata Model. Journal of Sustainable Development, 4(6), 72. Wang, F. (2012). A Cellular Automata Model to Simulate Land-use Changes at Fine Spatial Resolution." PhD Thesis, University of Calgary. www.landusesim.com- LanduseSim website. https://www.google.com/work/mapsearth/products/earthpro.html- Google Earth Pro website 6