Simulasi Perubahan Penggunaan Lahan Akibat Pembangunan Kawasan Industri Kendal (KIK) Berbasis Cellular Automata

https://doi.org/10.22146/mgi.32272

Muhammad Nur Sadewo(1*), Imam Buchori(2)

(1) ATR/BPN Kantor Pertanahan Kabupaten Badung, Bali
(2) Magister Pembangunan Wilayah dan Kota, Universitas Diponegoro, Semarang
(*) Corresponding Author

Abstract


Kawasan industri Kendal (KIK) dikembangkan dengan luas mencapai 2.200 Ha di utara kecamatan kaliwungu diperkirakan menyerap hingga 500.000 tenaga kerja. KIK akan mengakselerasi pertumbuhan kota yang ditandai dengan proses urbanisasi dan konsumsi lahan yang tinggi. Penelitian ini bertujuan untuk melakukan prediksi penggunaan lahan tahun 2031 dengan adanya KIK di Kendal Timur. Pendekatan yang digunakan yaitu kuantitatif berbasis raster, dengan analisis proyeksi perkembangan lahan terbangun berdasarkan trend perubahan penggunaan lahan tahun 2005 – 2017 dan kebutuhan lahan akibat KIK. Model simulasi perubahan penggunaan lahan dengan model Cellular Automata (CA) dengan faktor pendorong meliputi faktor biofisik, sosial ekonomi, sarana prasarana, aksesbilitas dan ketetanggaan. Hasil penelitian menunjukkan KIK memiliki pengaruh yang kuat untuk mempercepat pertumbuhan kawasan perkotaan kaliwungu. Arah perkembangan Kendal Timur tahun 2031 dominan terjadi di kecamatan kaliwungu kemudian menyebar di kecamatan brangsong, kota Kendal, kaliwungu selatan dan ngampel dengan mengikuti pola perkembangan konsentris linier. Penggunaan lahan yang mengalami pertumbuhan tahun 2031 meliputi industri (2017,96 Ha), permukiman (1007,30 Ha), perdagangan dan jasa (271,39 Ha), dan gudang (18,76 Ha) yang diikuti terkonversinya lahan non terbangun yaitu tambak (1593,5 Ha), sawah irigasi (784,35 Ha kebun campuran (362,34 Ha), tegalan (361,65 Ha), tanah terbuka (145,5 Ha), sawah tadah hujan (66,71 Ha) dan hutan produksi (1,32 Ha).

Keywords


Cellular Automata;kawasan industri;model;perubahan penggunaan lahan; simulasi

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References

Anderson, G., & Ge, Y. (2004). Do Economic Reforms Accelerate Urban Growth ? The Case of China. Urban Studies, 41(4100000732), 2211–2230.

Bogaert, P., & Dendoncker, N. (2007). Spatial analysis and modelling of land use distributions in Belgium Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems, 31(April 2015), 188–205. https://doi.org/10.1016/j.compenvurbsys.2006.06.004

Camagni, R., Gibelli, M. C., & Rigamonti, P. (2002). Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion. Ecological Economics, 40(2), 199–216. https://doi.org/http://dx.doi.org/10.1016/S0921-8009(01)00254-3

Cohen, B. (2004). Urban Growth in Developing Countries : A Review of Current Trends and a Caution Regarding Existing Forecasts. World Development, 32(1), 23–51.

Dadras, M., Shafri, H. Z. M., Ahmad, N., Pradhan, B., & Safarpour, S. (2015). Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. Egyptian Journal of Remote Sensing and Space Science, 18(1), 35–52. https://doi.org/10.1016/j.ejrs.2015.03.005

Damayanti, R. (2010). Pertumbuhan Fisik Kota Karena Pengaruh Industrialisasi , studi kasus Kota Ahmedabad - India. Humanisme, Arsitektur Dan Perencanaan.

Danoedoro, P. (2006). Extracting Land-Use Information Related to Socio-Economic Function From Quickbird Imagery : A Case Study of Semarang Area, Indonesia. In Map Asia, Bangkok (Vol. 1). Bangkok.

Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20–22), 3761–3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009

Han, H., Yang, C., & Song, J. (2015). Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China. Sustainability, 7, 4260–4279. https://doi.org/10.3390/su7044260

Hosseinali, F., Alesheikh, A. A., & Nourian, F. (2013). Agent-based modeling of urban land-use development , case study : Simulating future scenarios of Qazvin city. Cities, 31, 105–113. https://doi.org/10.1016/j.cities.2012.09.002

Leao, S., Bishop, I., & Evans, D. (2014). Simulating Urban Growth in a Developing Nation ’ s Region Using a Cellular Automata- Based Model. Journal of Urban Planning and Development, (July). https://doi.org/10.1061/(ASCE)0733-9488(2004)130

Liu, Y. (2009). Modelling Urban Development with Geographical Information System and Cellular Automata. Boca Raton: CRC Press.

Liu, Y., & He, J. (2009). Developing a web-based cellular automata model for urban growth simulation, 7492, 1–8. https://doi.org/10.1117/12.838657

Maria, R., Omrani, H., Charif, O., & Gerber, P. (2014). Land use changes modelling using advanced methods : Cellular automata and artificial neural networks . The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171. https://doi.org/10.1016/j.apgeog.2014.06.016

Milad, M., Ming, Y., Firuz, M., & Hanan, Z. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models : A review. International Journal of Applied Earth Observations and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007

Pawan. (2016). Urbanization and Its Causes and Effects : A Review. International Journal of Research and Scientific Innovation (IJRSI), III(Ix), 110–112. https://doi.org/10.1038/sdata.2016.34.ISSN

Pratomoatmojo, N. A. (2014). LanduseSim sebagai aplikasi pemodelan dan simulasi spasial perubahan penggunaan lahan berbasis Sistem Informasi Geografis dalam konteks perencanaan wilayah dan kota. Seminar Nasional Cities, 69–80.

Purwadhi, S.H., (2001). Interpretasi Citra Digital. Jakarta : Grasindo.

Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Landscape and Urban Planning Cellular automata models for the simulation of real-world urban processes : A review and analysis. Landscape and Urban Planning, 96, 108–122. https://doi.org/10.1016/j.landurbplan.2010.03.001

Tian, L., Ge, B., & Li, Y. (2017). Impacts of state-led and bottom-up urbanization on land use change in the peri-urban areas of Shanghai: Planned growth or uncontrolled sprawl? Cities, 60, Part B, 476–486. https://doi.org/http://dx.doi.org/10.1016/j.cities.2016.01.002

Torrens, P. M. (2003). Automata ‐ based models of urban systems. In In Advanced Spatial Analysis, P. Longley & M. Batty (Eds.) (pp. 61–79). Redlands: ESRI Press.

Wardana, D. W., Danoedoro, P., & Susilo, B. (2016). Kajian Perubahan Penggunaan Lahan Berbasis Citra Satelit Penginderaan Jauh Resolusi Menengah Dengan Metode Multi Layaer Perceptron dan Markov Chain. Majalah Geografi Indonesia, 30(1), 9–18.

Wolfram, S. (1984). Cellular automata as simple self-organizing systems. Elsevier Science Publishers B.V. Retrieved from http://cds.cern.ch/record/140047



DOI: https://doi.org/10.22146/mgi.32272

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