Spatial Analysis of Kulon Progo District Development from 2007-2030 with Cellular Automata Markov Model
Abstract
The construction of a new airport in Kulon Progo District as an embodiment of improving transportation infrastructure will have an effect on changes in spatial planning and land use. These changes can cause several impacts, so planning is needed as a preventive measure. Forms of planning are very diverse, ranging from the simplest to very complex. One of the alternatives is to make a simulation through a model approach with the Cellular Automata (CA) method, while the pattern of the direction of physical development of residential areas with the Global Moran’s Index. The research location focuses on three sub-districts in Kulon Progo District, which are the closest sub-districts to the airport construction sites, namely Kokap, Temon, and Wates Sub-districts. The main data used in this research are multi-temporal land use data, namely in 2007, 2012, and 2017. The data from the years of 2007 and 2012 are projected to become in the year 2017 and compared with the original data of 2017 to determine the level of suitability, resulting suitability of 91.53%. The final results of this research show predictions of the development of Kulon Progo District in 2030. In the span of 23 years, from 2007 to 2030, the allocation of residential land increased by 7.98% leaning west and south side. Results from the Global Moran’s Index show that the pattern of development in the Kulon Progo Regency area is random.
References
[1] Maryaningsih, N., et al. (2014). Pengaruh Infrastruktur terhadap Pertumbuhan Ekonomi Indonesia. Bul. Ekon. Monet. dan Perbank, vol. 17, no. 1, pp. 62–98.
[2] Kurniawan, D. A. and Wismadi. (2011). Peran dan Potensi Sektor Infrastruktur Transportasi dalam Mendukung Pengembangan Ekonomi di Provinsi DIY. FSTPT International. Symposium.
[3] Milad, M., et al. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, vol. 52, pp. 380–389.
[4] Pratama, M. A., et al. (2015). Menata Kota Melalui Rencana Detail Tata Ruang (RDTR). Yogyakarta: Andi Offset.
[5] Santé, I., et al. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landcsape and Urban Planning, vol. 96, pp. 108–122.
[6] Milad, M., et al. (2017). Improving the capability of an integrated CA-Markov model to simulate spatiotemporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation, vol. 59, pp. 65–78.
[7] Tong, D. and Murray, A. T. (2012). Spatial Optimization in Geography. Annals of The Association of American Geographers, vol. 102, pp. 1290–1309.
[8] Farzaneh, O. J., et al. (2017). Explanation of Urban Development Patterns in Order to Sustainable Development. Int. J. Urabn Manag. Energy Susainability, vol. 1, pp 15–23.
[9] Eboy, O. V. and Dambul, R. (2011). Viewing preferences of TVRO users in Sabah: Identification of distribution patterns using spatial statistics. Malaysian J. Soc. Sp, vol. 7, pp. 30–37.
[10] Wuryandari, T., et al. (2014). Identifikasi Autokorelasi Spasial pada Jumlah Pengangguran di Jawa Tengah Menggunakan Indeks Moran. Media Statistika, vol. 7, pp. 1–10.
[11] Siahaan, A., et al. (2014). Analisis Pengaruh Pembangunan Perumahan Terhadap Pengembangan Wilayah Kecamatan Siantar Marimbun Kota Pematangsiantar. Ekonom, vol. 17, pp. 103–110.
[12] Guan, D., et al. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, vol. 222, no. 20–22, pp. 3761–3772. https://doi.org/10.1016/j. ecolmodel.2011.09.009