SPATIAL DATA-BASED DEVELOPMENT PLANNING: UTILIZING BIG DATA AND AI FOR SPATIAL PLANNING 2025
Abstract
The development of digital technology is driving significant changes in the spatial planning process. This study discusses the use of spatial data, Big Data, and Artificial Intelligence (AI) as a new foundation for development planning in 2025. Through an analytical approach and literature review, this article explores how the integration of multiscale spatial data, real-time data, and machine learning algorithms can improve prediction accuracy, mapping effectiveness, and the quality of spatial decision-making. The results of the discussion show that Big Data can provide a comprehensive picture of regional dynamics, while AI facilitates the analysis of spatial patterns and simulation of development scenarios. The implementation of these technologies has the potential to accelerate the planning process, strengthen environmental risk mitigation, and encourage sustainable development. Thus, the use of Big Data and AI is a key strategy for realizing spatial planning that is adaptive, inclusive, and responsive to future changes.
References
Adiyanto, D. (2019). Application of remote sensing technology in monitoring land use changes in Indonesia. Indonesian Geomatics Journal, 3(2), 55–64. https://doi.org/10.24895/jgi.v3i2.202
Bappenas. (2022). One Map Policy and its use in national development planning. Ministry of National Development Planning/Bappenas.https://www.bappenas.go.id
Chen, L., Li, Y., & Wang, X. (2021). Big data–driven analysis for urban growth forecasting using integrated geospatial datasets. Computers, Environment and Urban Systems, 85, 101565. https://doi.org/10.1016/j.compenvurbsys.2020.101565
Fajar, M., & Pratama, R. (2021). Smart city systems in Indonesia: Integration of IoT, spatial data, and urban management. Journal of Information and Urban Technology, 5(1), 22–34. https://doi.org/10.36730/jtip.v5i1.421
Fitriyani, S., & Lestari, P. (2021). Integration of GIS and big data in disaster mitigation in Indonesia. Journal of Disaster & Spatial Planning, 4(2), 101–112. https://doi.org/10.31219/osf.io/9hdf3
Goodchild, M. F. (2020). Geographic information science: Developing a framework for spatial data analysis in the era of big data. International Journal of Geographical Information Science, 34(4), 667–689. https://doi.org/10.1080/13658816.2019.1658571
Karim, A., & Lubis, D. (2020). Utilization of drones for large-scale detailed mapping in regional planning. Jurnal Geospatial Nusantara, 2(1), 33–44. https://doi.org/10.7454/jgn.v2i1.112
Kurniawan, H. (2022). Comparison of machine learning models for satellite imagery-based land cover change detection. Journal of Geographic Information Science, 9(3), 201–214. https://doi.org/10.31227/osf.io/rq8wn
Li, S., Cheng, T., & Su, J. (2020). AI-based land-use change detection using high-resolution satellite imagery. Remote Sensing of Environment, 248, 111989. https://doi.org/10.1016/j.rse.2020.111989
Mardiansyah, A., & Putri, L. (2023). Utilizing mobility big data for urban density analysis. Journal of Urban & Regional Planning, 12(1), 77–89. https://doi.org/10.47490/jppw.v12i1.872
Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461. https://doi.org/10.1007/s10708-014-9602-5
Septiani, R., & Aulia, D. (2022). Analysis of the integration of Big Data, GIS, and Artificial Intelligence to support sustainable urban planning. Journal of Information Systems and Public Policy, 7(2), 50–63. https://doi.org/10.31227/osf.io/y4mcu
Susanti, W., & Rudi, M. (2023). Application of artificial intelligence in satellite image processing for spatial planning. Journal of Geospatial Technology, 8(1), 12–24. https://doi.org/10.31227/osf.io/n3cz5
Wijaya, A. (2022). Artificial intelligence for optimizing transportation systems in developing cities. Journal of Urban Technology, 29(6), 1–18. https://doi.org/10.1080/106307
