Prediction of potential deviations in settlement area space patterns based on cellular Automata in Mapanget District, Manado City
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Published:
Apr 1, 2024
Keywords:
Cellular automata, Deviation, Land use change, Land use Sim, Settlement
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Abstract
Mapanget District is one of the administrative parts of Manado City which has service functions, namely housing and new settlements, tourism, and trade and services. The Mapanget District is part of the new city planning in the 2015–2019 RPJMN to account for unequal development in the city core. Predicting the development of residential land use is crucial to avoid uncontrollably detrimental effects on suburban communities. To forecast land development over 20 years, from 2022 to 2042, this study will employ spatial analysis based on the Geography Information System-Cellular Automata and weighting analysis using the Analytic Hierarchy Process (AHP) technique to analyze potential deviations in the spatial pattern development of Manado City settlements, particularly Mapanget District. The software used for this research's simulation procedure is LanduseSim 2.3.1, QGIS, and Archmap 10.8. According to the projection results, residential land use will vary during the following 20 years, starting in 1956.75 Ha, or a 201% increase, resulting from the most important driving factor, which is closeness to existing towns. 1106.79 Ha is the possible deviation depending on the direction of the spatial pattern plan for residential structures. It is advised to plan tall buildings, particularly for settlements, as the current residential land in Mapanget District exceeds both the carrying capacity and the land capacity as of 2042, according to the prediction results.
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Sela, Rieneke Lusia Evani, Andy Anton Mangopa Malik, Sonny Tilaar, and Nadira Astuti Lestari. 2024. “Prediction of Potential Deviations in Settlement Area Space Patterns Based on Cellular Automata in Mapanget District, Manado City”. ARTEKS : Jurnal Teknik Arsitektur 9 (1), 47-58. https://doi.org/10.30822/arteks.v9i1.2655.
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ARTICLES-RESEARCH
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Cabrera, and Lee. 2019. ‘Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines’. Water 11 (11): 2203. https://doi.org/10.3390/w11112203.
Dyan Syafitri, Rivan Aji Wahyu, and Cahyono Susetyo. 2019. ‘Pemodelan Pertumbuhan Lahan Terbangun Sebagai Upaya Prediksi Perubahan Lahan Pertanian Di Kabupaten Karanganyar’. Jurnal Teknik ITS 7 (2). https://doi.org/10.12962/j23373539.v7i2.36453.
Fitawok, Melaku Bogale, Ben Derudder, Amare Sewnet Minale, Steven Van Passel, Enyew Adgo, and Jan Nyssen. 2020. ‘Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach’. Land 9 (4): 115. https://doi.org/10.3390/land9040115.
Gerami Seresht, Nima, and Aminah Robinson Fayek. 2019. ‘Computational Method for Fuzzy Arithmetic Operations on Triangular Fuzzy Numbers by Extension Principle’. International Journal of Approximate Reasoning 106 (March): 172–93. https://doi.org/10.1016/j.ijar.2019.01.005.
Gharaibeh, Anne, Abdulrazzaq Shaamala, Rasha Obeidat, and Salman Al-Kofahi. 2020. ‘Improving Land-Use Change Modeling by Integrating ANN with Cellular Automata-Markov Chain Model’. Heliyon 6 (9): e05092. https://doi.org/10.1016/j.heliyon.2020.e05092.
Kafy, Abdulla - Al, Md. Nazmul Huda Naim, Gangaraju Subramanyam, Abdullah-Al- Faisal, Nessar Uddin Ahmed, Abdullah Al Rakib, Marium Akter Kona, and Golam Sabbir Sattar. 2021. ‘Cellular Automata Approach in Dynamic Modelling of Land Cover Changes Using RapidEye Images in Dhaka, Bangladesh’. Environmental Challenges 4 (August): 100084. https://doi.org/10.1016/j.envc.2021.100084.
Khan, Adeer, and Mehran Sudheer. 2022. ‘Machine Learning-Based Monitoring and Modeling for Spatio-Temporal Urban Growth of Islamabad’. The Egyptian Journal of Remote Sensing and Space Science 25 (2): 541–50. https://doi.org/10.1016/j.ejrs.2022.03.012.
Latif, Sahabuddin, Nurhikmah Paddiyatu, Andi Yusri, and Sumarni Baking. 2021. ‘Infiltration Optimization Effort towards Sustainable Land-Use’. ARTEKS : Jurnal Teknik Arsitektur 6 (3): 481–90. https://doi.org/10.30822/arteks.v6i3.1203.
Margono, Romi Bramantyo, Yulia, Siswanti Zuraida, and Wiwik Dwi Pratiwi. 2021. ‘The Impact of Housing Transformation to Livability in North Bandung Peri-Urban Area’. ARTEKS : Jurnal Teknik Arsitektur 6 (2): 259–68. https://doi.org/10.30822/arteks.v6i2.724.
Morakinyo, Kolawole O., F. O. Eghenure, H. O. Adebiyi, and J. A. Adeola. 2023. ‘Environmental Sustainability and Urban Growth Issues: Empirical Evidence from Osogbo Capital Territory, Nigeria’. Local Engineering 1 (2): 55–66. https://doi.org/10.59810/lejlace.v1i2.74.
Muljadinata, Albertus Sidharta, and Bayu Widiantoro. 2023. ‘Karsten’s Work as a City Branding Strengthener for The Semarang City, Indonesia’. Journal of Architecture and Human Experience 1 (1): 11–22. https://doi.org/10.59810/archimane.v1i1.2.
Ngangi, Reddy Silvano, Papia J. Franklin, and Windy Mononimbar. 2018. ‘Analisis Pertumbuhan Kawasan Mapanget Sebagai Kota Baru.’ Jurnal Spasial 5 (1): 82–92. https://doi.org/https://doi.org/10.35793/sp.v5i1.19098.
Noviani, Rita, Chatarina Muryani, Ahmad, Sarwono, Sugiyanto, and Singgih Prihadi. 2023. ‘Modelling of Urban Growth Based on a Geographic Information System (GIS) and Cellular Automata at Sukoharjo Regency in 2032’. IOP Conference Series: Earth and Environmental Science 1190 (1): 012042. https://doi.org/10.1088/1755-1315/1190/1/012042.
Ou, Cong, Jianyu Yang, Zhenrong Du, Xin Zhang, and Dehai Zhu. 2019. ‘Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China’. Sustainability 11 (9): 2464. https://doi.org/10.3390/su11092464.
Pigawati, B, A Sugiri, I N A M Putra, and T A Suryani. 2020. ‘Prediction and Location Suitability of Settlement Growth at The BWK II of Semarang City’. IOP Conference Series: Earth and Environmental Science 500 (1): 012023. https://doi.org/10.1088/1755-1315/500/1/012023.
Pratomoatmojo, N A. 2018a. ‘Permodelan Perubahan Penggunaan Lahan Berbasis Cellular Automata Dan Sistem Informasi Geografis Dengan Menggunakan LanduseSim’. Jurnal Penataan Ruang 13 (1): 26–30.
———. 2018b. ‘LanduseSim Algorithm: Land Use Change Modelling by Means of Cellular Automata and Geographic Information System’. IOP Conference Series: Earth and Environmental Science 202 (November): 012020. https://doi.org/10.1088/1755-1315/202/1/012020.
———. 2018c. ‘LanduseSim Methods: Land Use Class Hierarchy for Simulations of Multiple Land Use Growth’. IOP Conference Series: Earth and Environmental Science 202 (November): 012023. https://doi.org/10.1088/1755-1315/202/1/012023.
———. 2021. LanduseSim Praktikum :Pemodelan Spasial Perkembangan Permukiman Dan Industri Berbasis Sistem Informasi Geografis Dan Cellular Automata v.0.3.
Putra, Muhammad Rusdin Rangga, and Iwan Rudiarto. 2018. ‘Simulasi Perubahan Penggunaan Lahar Dengan Konsep Celluler Automata Di Kota Mataram’. Jurnal Pengembangan Kota 6 (2): 174. https://doi.org/10.14710/jpk.6.2.174-185.
Regasa, Motuma, Michael Nones, and Dereje Adeba. 2021. ‘A Review on Land Use and Land Cover Change in Ethiopian Basins’. Land 10 (6): 585. https://doi.org/10.3390/land10060585.
Rimal, Bhagawat, Lifu Zhang, Hamidreza Keshtkar, Barry Haack, Sushila Rijal, and Peng Zhang. 2018. ‘Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain’. ISPRS International Journal of Geo-Information 7 (4): 154. https://doi.org/10.3390/ijgi7040154.
Sadewo, Muhammad Nur, and Imam Buchori. 2018. ‘Simulasi Perubahan Penggunaan Lahan Akibat Pembangunan Kawasan Industri Kendal (KIK) Berbasis Cellular Automata’. Majalah Geografi Indonesia 32 (2): 142. https://doi.org/10.22146/mgi.32272.
Subandi, E L, Widiatmaka, and M Ardiansyah. 2019. ‘Spatial Modeling of Paddy Fields in Gorontalo Regency, Indonesia’. IOP Conference Series: Earth and Environmental Science 399 (1): 012037. https://doi.org/10.1088/1755-1315/399/1/012037.
Sumarmi, Sumarmi, Purwanto Purwanto, and Syamsul Bachri. 2021. ‘Spatial Analysis of Mangrove Forest Management to Reduce Air Temperature and CO2 Emissions’. Sustainability 13 (14): 8090. https://doi.org/10.3390/su13148090.
Surya, Batara, Despry Nur Annisa Ahmad, Harry Hardian Sakti, and Hernita Sahban. 2020. ‘Land Use Change, Spatial Interaction, and Sustainable Development in the Metropolitan Urban Areas, South Sulawesi Province, Indonesia’. Land 9 (3): 95. https://doi.org/10.3390/land9030095.
Tatashev, Alexander, and Marina Yashina. 2019. ‘Spectrum of Elementary Cellular Automata and Closed Chains of Contours †’. Machines 7 (2): 28. https://doi.org/10.3390/machines7020028.
Wahyudi, Agung, and Yan Liu. 2016. ‘Cellular Automata for Urban Growth Modelling’: International Review for Spatial Planning and Sustainable Development 4 (2): 60–75. https://doi.org/10.14246/irspsd.4.2_60.
Xia, Chang, and Bin Zhang. 2021. ‘Exploring the Effects of Partitioned Transition Rules upon Urban Growth Simulation in a Megacity Region: A Comparative Study of Cellular Automata-Based Models in the Greater Wuhan Area’. GIScience & Remote Sensing 58 (5): 693–716. https://doi.org/10.1080/15481603.2021.1933714.
Yannis, George, Angeliki Kopsacheili, Anastasios Dragomanovits, and Virginia Petraki. 2020. ‘State-of-the-Art Review on Multi-Criteria Decision-Making in the Transport Sector’. Journal of Traffic and Transportation Engineering (English Edition) 7 (4): 413–31. https://doi.org/10.1016/j.jtte.2020.05.005.