Study of Urban Growth Center Development Factors and Simulation The Mamminasata Urban Area


Emil Salim Rasyidi , Jumadil , Syafri , Rahmawati Rahman , Rusneni , Hamsinah , Muh. Khalil Jibran






Vol. 10 No. 4 (2024): April


Mamminasata, OBIA, Regional Growth Center, Spatial Simulation

Research Articles


How to Cite

Rasyidi, E. S., Jumadil, Syafri, Rahman, R., Rusneni, Hamsinah, & Jibran, M. K. (2024). Study of Urban Growth Center Development Factors and Simulation The Mamminasata Urban Area. Jurnal Penelitian Pendidikan IPA, 10(4), 1976–1988.


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Urban growth starts from a center and affects the surrounding areas, this is due to the emergence of additional centers that will each function as growth poles, to study the dynamics related to urban growth center information, several data and extraction and analysis methods are needed. This study examines several methods of extracting information on urban growth centers from Landsat 8 OLI/TIRS in 2013 and 2023 by utilizing the spectral resolution of Landsat imagery in the Mamminasata area, and integrating spatial modeling to simulate the growth centers of the Mamminasata area in the next 10 years (2043). The results of this research classification method show an accuracy rate of 71.48%. The results of the determinant factor test show that the most influential factors are the distance from the center of shops, slope, then the distance from the university, and the distance from the main road in 2013-2023 Mamminasata Urban Area. The results of this variable drive test are then used in spatial simulations using the markov chain simulation method in the LCM module and show an increase in the area of the growth center in the Mamminasata region, for the entire scope of the Mamminasata region, the Makassar City area shows the highest intensity of regional growth centers and becomes the center of growth in the Mamminasata urban area. The planning concept applied to the results of this study is based on resources, geographic location, and factors affecting the growth center.


Al-Kheder, S. A. (2006). Urban growth modeling with artificial intelligence techniques. Purdue University. Retrieved from

Aldalbahi, M., & Walker, G. (2015). Attitudes and policy implications of urban growth boundary and traffic congestion reduction in Riyadh, Saudi Arabia. International Conference Data Mining, 1–2. Retrieved from

Bai, Y., Deng, X., Jiang, S., Zhang, Q., & Wang, Z. (2018). Exploring the relationship between urbanization and urban eco-efficiency: Evidence from prefecture-level cities in China. Journal of Cleaner Production, 195, 1487–1496.

Bakshi, A., & Esraz-Ul-Zannat, M. (2023). Application of urban growth boundary delineation based on a neural network approach and landscape metrics for Khulna City, Bangladesh. Heliyon, 9(6). Retrieved from

Bouhennache, R., Bouden, T., Taleb-Ahmed, A., & Cheddad, A. (2019). A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto International, 34(14), 1531–1551.

Carlino, G. A., & Mills, E. S. (1987). The determinants of county growth. Journal of Regional Science, 27(1), 39–54.

Cengiz, S., Görmücs, S., & Ouguz, D. (2022). Analysis of the urban growth pattern through spatial metrics; Ankara City. Land Use Policy, 112, 105812.

Cheng, J., & Masser, I. (2003). Urban growth pattern modeling: a case study of Wuhan city, PR China. Landscape and Urban Planning, 62(4), 199–217.

Cheng, J., & Masser, I. (2004). Understanding spatial and temporal processes of urban growth: cellular automata modelling. Environment and Planning B: Planning and Design, 31(2), 167–194.

Danoedoro, P. (2012). Pengantar penginderaan jauh digital. In Yogyakarta: Penerbit Andi.

Dinka, M. O., & Klik, A. (2019). Effect of land use--land cover change on the regimes of surface runoff—the case of Lake Basaka catchment (Ethiopia). Environmental Monitoring and Assessment, 191(5), 278.

Estoque, R. C., & Murayama, Y. (2013). Landscape pattern and ecosystem service value changes: Implications for environmental sustainability planning for the rapidly urbanizing summer capital of the Philippines. Landscape and Urban Planning, 116, 60–72.

Ewing, R., Lyons, T., Siddiq, F., Sabouri, S., Kiani, F., Hamidi, S., Choi, D., & Ameli, H. (2022). Growth management effectiveness: A literature review. Journal of Planning Literature, 37(3), 433–451.

Feng, Y., Liu, Y., Tong, X., Liu, M., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3), 188–196.

Ghosh, A., Ng, K. T. W., & Karimi, N. (2023). An evaluation of the temporal and spatial evolution of waste facilities using a simplified spatial distance analytical framework. Environmental Development, 45, 100820.

Grădinaru, S. R., Iojua, C. I., Onose, D. A., Gavrilidis, A. A., Puatru-Stupariu, I., Kienast, F., & Hersperger, A. M. (2015). Land abandonment as a precursor of built-up development at the sprawling periphery of former socialist cities. Ecological Indicators, 57, 305–313.

Guzha, A. C., Rufino, M. C., Okoth, S., Jacobs, S., & Nóbrega, R. L. B. (2018). Impacts of land use and land cover change on surface runoff, discharge and low flows: Evidence from East Africa. Journal of Hydrology: Regional Studies, 15, 49–67.

He, Q., Tan, R., Gao, Y., Zhang, M., Xie, P., & Liu, Y. (2018). Modeling urban growth boundary based on the evaluation of the extension potential: A case study of Wuhan city in China. Habitat International, 72, 57–65.

He, Y., Ai, B., Yao, Y., & Zhong, F. (2015). Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 38, 164–174.

Li, X., & Yeh, A. G.-O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343.

Liladhar Rane, N., Achari, A., Hashemizadeh, A., Phalak, S., Pande, C. B., Giduturi, M., Khan, M. Y. A., Tolche, A. D., Tamam, N., Abbas, M., & others. (2023). Identification of sustainable urban settlement sites using interrelationship based multi-influencing factor technique and GIS. Geocarto International, 38(1), 2272670.

Liu, X., Li, X., Shi, X., Zhang, X., & Chen, Y. (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24(5), 783–802.

Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., & Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94–116.

Liu, Y., He, Q., Tan, R., Liu, Y., & Yin, C. (2016). Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China. Applied Geography, 66, 109–118.

Mantelas, L., Hatzichristos, T., & Prastacos, P. (2008). Modeling urban growth using fuzzy cellular automata. 11th AGILE International Conference on Geographic Information Science, Girona, Spain, 1-12. Retrieved from

McDonald, R. I., Green, P., Balk, D., Fekete, B. M., Revenga, C., Todd, M., & Montgomery, M. (2011). Urban growth, climate change, and freshwater availability. Proceedings of the National Academy of Sciences, 108(15), 6312–6317.

Müller, K., Steinmeier, C., & Küchler, M. (2010). Urban growth along motorways in Switzerland. Landscape and Urban Planning, 98(1), 3–12.

Osman, T., Divigalpitiya, P., & Arima, T. (2016). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on land use in the Giza Governorate, Greater Cairo Metropolitan region. International Journal of Urban Sciences, 20(3), 407–426.

Oum, T. H., & Park, J.-H. (2004). Multinational firms’ location preference for regional distribution centers: focus on the Northeast Asian region. Transportation Research Part E: Logistics and Transportation Review, 40(2), 101–121.

Pribadi, D. O., Putra, A. S., & Rustiadi, E. (2015). Determining optimal location of new growth centers based on LGP--IRIO model to reduce regional disparity in Indonesia. The Annals of Regional Science, 54, 89–115.

Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108–122.

Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088.

Shahfahad, Talukdar, S., Naikoo, M. W., Rahman, A., Gagnon, A. S., Islam, A. R. M. T., & Mosavi, A. (2023). Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape. Geocarto International, 38(1), 2152496.

Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525–552.

Sunde, M. G., He, H. S., Zhou, B., Hubbart, J. A., & Spicci, A. (2014). Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth. Landscape and Urban Planning, 124, 104–108.

Uhl, J. H., Hunter, L. M., Leyk, S., Connor, D. S., Nieves, J. J., Hester, C., Talbot, C., & Gutmann, M. (2023). Place-level urban--rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, 104762.

Wu, K., & Zhang, H. (2012). Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing Hangzhou metropolitan area, eastern China (1978--2008). Applied Geography, 34, 137–145.

Yang, X., Chen, R., & Zheng, X. Q. (2016). Simulating land use change by integrating ANN-CA model and landscape pattern indices. Geomatics, Natural Hazards and Risk, 7(3), 918–932.

Yasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., & Noor, N. M. (2022). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. IOP Conference Series: Earth and Environmental Science, 1067(1), 12037.

Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.

Author Biographies

Emil Salim Rasyidi, Bosowa University

Jumadil, Bosowa University

Syafri, Bosowa University

Rahmawati Rahman, Bosowa University

Rusneni, Bosowa University

Hamsinah, Bosowa University

Muh. Khalil Jibran, Universitas Hasanuddin


Copyright (c) 2024 Emil Salim Rasyidi, Jumadil, Syafri, Rahmawati Rahman, Rusneni, Hamsinah, Muh. Khalil Jibran

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