Clustering algorithms and ai techniques for identifying spatial patterns: A systematic review
Abstract
The present article shows a systematic review focused on the implementation of clustering algorithms for geospatial data analysis and the integration of artificial intelligence techniques into geographic information systems. The aim is to highlight the importance of artificial intelligence and its application as tools for identifying and analyzing information patterns in geospatial environments. To achieve this objective, exploratory, documentary, and descriptive research methods were employed, along with an analysis of various scientific sources, enabling a critical evaluation of the most significant advancements in this field. This systematic review was adapted to the context of clustering algorithms and geographic information systems. This methodology ensured a high-quality search for relevant studies, including processes for inclusion, exclusion, and critical evaluation of sources. The analysis revealed that geographic information systems, when combined with artificial intelligence techniques, offer a wide range of applications across various fields of study, where the identification of spatial patterns has become a key tool for improving the understanding of complex phenomena.
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References
Abubahia, A., & Cocea, M. (2015). A clustering approach for protecting GIS vector data. In J. Zdravkovic, M. Kirikova, & P. Johannesson, (eds.). Advanced Information Systems Engineering (pp. 133–147). Springer. https://doi.org/10.1007/978-3-319-19069-3_9
Aksoy, E. (2006, octubre 8-13). Clustering with GIS: An attempt to classify Turkish district data [Conferencia]. XXIII FIG International Congress, Munich, Alemania.
Amat, J. (2017). Clustering y heatmaps: Aprendizaje no supervisado. https://rpubs.com/Joaquin_AR/310090
Biswas, S., Chen, F., Sistrunk, A., Muthiah, S., Chen, Z., Self, N., Lu, C.-T., & Ramakrishnan, N. (2020). Geospatial clustering for balanced and proximal schools. Proceedings of the AAAI Conference on Artificial Intelligence, 34(9), 13358–13365. https://doi.org/10.1609/aaai.v34i09.7058
Castillo, G. (2023, 16 de febrero). Aprendizaje no supervisado: ¿Qué es y cómo funciona? NeuroK. https://neurok.es/aprendizaje-no-supervisado-que-es/
Folini, A., Lenzi, E., & Biraghi, C. A. (2022). Cluster analysis: A comprehensive and versatile QGIS plugin for pattern recognition in geospatial data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W1-2022, 151–157. https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-151-2022
Fontalvo, H. T., Vega, H. M., & Mejía, Z. F. (2023). Método de clustering e inteligencia artificial para clasificar y proyectar delitos violentos en Colombia. Revista Científica General José María Córdova, 21(42), 551–572. https://doi.org/10.21830/19006586.1117
García, P., Villota, O. W., & Litardo, U. J. (2017). Detección automática del nivel de crimen basado en el análisis de puntos calientes en la ciudad de Guayaquil. Dominio de las Ciencias, 3(2), 367–379.
Kitchenham, B., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075. https://doi.org/10.1016/j.infsof.2013.07.010
Laskowski, J. F., & Tomiło, P. (2023). A new AI-based method for clustering survey responses. Journal of Modern Science, 54(5), 355–377. https://doi.org/10.13166/jms/176171
Lemenkova, P. (2021). Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and k-means clustering. Acta Agriculturae Serbica, 26(52), 159–165. https://doi.org/10.5937/AASer2152159L
Li, W., Arundel, S., Gao, S., Goodchild, M., Hu, Y., Wang, S., & Zipf, A. (2024). GeoAI for science and the science of GeoAI. Journal of Spatial Information Science, 29, 1–17. https://doi.org/10.5311/JOSIS.2024.29.349
Mohammad, N. (2023). A computational theory and semi-supervised algorithm for clustering. arXiv. https://doi.org/10.48550/arXiv.2306.06974
Mouraz, C. P., Almeida, R. M. S. F., & Mendes, S. J. (2022). Combining cluster analysis and GIS maps to characterise building stock: Case study in the historical city centre of Viseu, Portugal. Journal of Building Engineering, 58. https://doi.org/10.1016/j.jobe.2022.104949
Muhammad, S. (2019). Application of labelled k means clustering for GIS contract automation. Journal of Engineering and Applied Sciences, 38(1), 25–34.
Nieto, W., Plata, D., Torres, R., & Solano, E. (2023). Data analysis y clustering para el análisis de crímenes de alto impacto en Barranquilla [Tesis de grado, Universidad del Norte].
Pedada, S. (2023, 07 de junio). ¿Qué es el análisis geoespacial? El plan antes del plan real. Mind the Graph. https://mindthegraph.com/blog/es/analisis-geoespacial/
Ramírez, L. (2023, 11 de abril). Algoritmo k-means: ¿Qué es y cómo funciona? IEBS. https://n9.cl/8zbcu7
Roy, P., & Mandal, J. K. (2012). A novel spatial fuzzy clustering using Delaunay triangulation for large scale GIS data (NSFCDT). Procedia Technology, 6, 452–459. https://doi.org/10.1016/j.protcy.2012.10.054
Sancho, C. F. (2023). Algoritmos de clustering. MatematIA. https://n9.cl/2f0tp4
Schoier, G., & Gregorio, C. (2017). Clustering algorithms for spatial big data. In O. Gervasi, B. Murgante, S. Misra, G. Borruso, C. M. Torre, A. M. A. C. Rocha, D. Taniar, B. O. Apduhan, E. Stankova, & A. Cuzzocrea, (eds.). Computational Science and Its Applications – ICCSA 2017 (pp. 571–583). Springer. https://doi.org/10.1007/978-3-319-62401-3_41
Velandia, R., Guataquira, A., & Prada, J. (2018). Prototipo de un modelo clasificador para la toma de decisiones a partir de la percepción actual de la seguridad en Bogotá. Tecnología, Innovación y Aplicación, 6(2), 18–24.
Vichi, M., Cavicchia, C., & Groenen, P. J. F. (2022). Hierarchical means clustering. Journal of Classification, 39(3), 553–577. https://doi.org/10.1007/s00357-022-09419-7
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