MODELING URBAN LAND USE BASED ON POINTS OF INTEREST (POI) DATA TO SUPPORT PARTICIPATORY PLANNING
Keywords:
guna lahan, perencanaan partisipatif , pemodelan spasialAbstract
Urban spatial planning involves the design process of cities, including spatial patterns and land use distribution. Through land use arrangement, the functions assigned to physical spaces determine the intensity of urban development. Urban areas typically host economic, social, or cultural activities. Therefore, land use mapping is commonly carried out through field surveys to supplement information about the activities occurring on land cover. This study proposes a model to identify land use by utilizing data derived from community participation. Using Point of Interest (POI) data, information is obtained regarding the spatial distribution of existing activities. The objective of this research is to develop a model that identifies urban land use based on the POIs representing ongoing activities in urban space. The model is constructed by filtering and counting the number of POIs within each urban block. The results show that the proposed model can effectively map urban land use based on community preferences. Thus, this study offers a new approach to land use modeling to support participatory planning.
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References
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Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475. https://doi.org/https://doi.org/10.1016/j.jag.2021.102475
Yang, Z. (2020). Urban Functional Area and Its Visualization in the Era of Big Data. 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC), 133–137. https://doi.org/10.1109/ICCEIC51584.2020.00034
Alvarez, D. G., Mas, J. F., Paegelow, M., & Olmedo, M. T. C. (2022). Land Use Cover Datasets and Validation Tools: Validation Practices with QGIS. Springer International Publishing.
Alyahya, S. (2020). Crowdsourced software testing: A systematic literature review. Information and Software Technology, 127, 106363. https://doi.org/https://doi.org/10.1016/j.infsof.2020.106363
Andrade, R., Alves, A., & Bento, C. (2020). POI Mining for Land Use Classification: A Case Study. ISPRS International Journal of Geo-Information, 9(9). https://doi.org/10.3390/ijgi9090493
Arsanjani, J. J., Zipf, A., Mooney, P., & Helbich, M. (2015). OpenStreetMap in GIScience. Springer.
Badan Pusat Statistik. (2020). Klasifikasi Baku Lapangan Usaha Indonesia (KBLI) 2020. Badan Pusat Statistik.
Badan Pusat Statistik Kota Yogyakarta. (2024). Kota Yogyakarta Dalam Angka 2024: Vol. XLV. Badan Pusat Statistik Kota Yogyakarta.
Barlacchi, G., Lepri, B., & Moschitti, A. (2021). Land Use Classification With Point of Interests and Structural Patterns. IEEE Transactions on Knowledge and Data Engineering, 33(9), 3258–3269. https://doi.org/10.1109/TKDE.2020.2967381
Bracken, I. (2014). Urban Planning Methods: Research and Policy Analysis. Taylor & Francis.
DeVellis, R. F. (2005). Inter-Rater Reliability. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (pp. 317–322). Elsevier. https://doi.org/https://doi.org/10.1016/B0-12-369398-5/00095-5
Duranton, G., & Puga, D. (2015). Chapter 8 - Urban Land Use. In G. Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of Regional and Urban Economics (Vol. 5, pp. 467–560). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-444-59517-1.00008-8
Gomes, E., Banos, A., Abrantes, P., & Rocha, J. (2022). Chapter 2 - Future land use/cover changes and participatory planning. In P. Pereira, E. Gomes, & J. Rocha (Eds.), Mapping and Forecasting Land Use (pp. 29–53). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-323-90947-1.00001-6
Kresse, W., & Danko, D. M. (2012). Springer Handbook of Geographic Information. Springer Berlin.
Kyttä, M., Randrup, T., Sunding, A., Rossi, S., Harsia, E., Palomäki, J., & Kajosaari, A. (2023). Prioritizing participatory planning solutions: Developing place-based priority categories based on public participation GIS data. Landscape and Urban Planning, 239, 104868. https://doi.org/https://doi.org/10.1016/j.landurbplan.2023.104868
Liao, P., Wan, Y., Tang, P., Wu, C., Hu, Y., & Zhang, S. (2019). Applying crowdsourcing techniques in urban planning: A bibliometric analysis of research and practice prospects. Cities, 94, 33–43. https://doi.org/https://doi.org/10.1016/j.cities.2019.05.024
McClean, S. (2003). Data Mining and Knowledge Discovery. In Encyclopedia of Physical Science and Technology (pp. 229–246). https://doi.org/10.1016/B0-12-227410-5/00845-0
Pemerintah Kota Yogyakarta. (2021). Peraturan Walikota (PERWALI) Kota Yogyakarta Nomor 118 Tahun 2021 tentang Rencana Detail Tata Ruang Kota Yogyakarta Tahun 2021 – 2041. Yogyakarta: Pemerintah Kota Yogyakarta
Pramono, R. W. D. (2023). Teknik Perencanaan Kota dan Kawasan Perkotaan. Deepublish.
Pramono, R. W., & Suminar, R. E. (2019). Ekonomi Wilayah untuk Perencanaan Tata Ruang. Deepublish.
Rodrigue, J.-P. (2020). Transportation and Land Use. In A. Kobayashi (Ed.), International Encyclopedia of Human Geography (Second Edition) (Second Edition, pp. 463–469). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-08-102295-5.10327-0
Satzger, B., Zabolotnyi, R., Dustdar, S., Wild, S., Gaedke, M., Göbel, S., & Nestler, T. (2014). Chapter 8 - Toward Collaborative Software Engineering Leveraging the Crowd. In I. Mistrik, R. Bahsoon, R. Kazman, & Y. Zhang (Eds.), Economics-Driven Software Architecture (pp. 159–182). Morgan Kaufmann. https://doi.org/https://doi.org/10.1016/B978-0-12-410464-8.00008-8
Shakibamanesh, A., & Ebrahimi, B. (2020). Toward Practical Criteria for Analyzing and Designing Urban Blocks. https://doi.org/10.5772/intechopen.90504
Voieta, I., Queiroz, L. C. de, Andrade, L. M., Silva, L. C. S., Fontes, V. F., Barbosa Jr, A., Resende, V., Petroianu, A., Andrade, Z., Antunes, C. M., & Lambertucci, J. R. (2010). Imaging techniques and histology in the evaluation of liver fibrosis in hepatosplenic schistosomiasis mansoni in Brazil: a comparative study. Memórias Do Instituto Oswaldo Cruz, 105.
Voigt, B., & Troy, A. (2008). Land-Use Modeling. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of Ecology (pp. 2126–2132). Academic Press. https://doi.org/https://doi.org/10.1016/B978-008045405-4.00192-0
Wang Ziyi AND Ma, D. A. N. D. S. D. A. N. D. Z. J. (2021). Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLOS ONE, 16(5), 1–20. https://doi.org/10.1371/journal.pone.0251988
Wilson, A., & Tewdwr-Jones, M. (2021). Digital Participatory Planning: Citizen Engagement, Democracy, and Design. Taylor & Francis.
Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475. https://doi.org/https://doi.org/10.1016/j.jag.2021.102475
Yang, Z. (2020). Urban Functional Area and Its Visualization in the Era of Big Data. 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC), 133–137. https://doi.org/10.1109/ICCEIC51584.2020.00034
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Published
2025-05-26
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Mayastuti, T., & Pramono, R. W. D. (2025). MODELING URBAN LAND USE BASED ON POINTS OF INTEREST (POI) DATA TO SUPPORT PARTICIPATORY PLANNING. Plano Madani : Jurnal Perencanaan Wilayah Dan Kota, 14(1). Retrieved from https://journal.uin-alauddin.ac.id/index.php/planomadani/article/view/47441
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