Advanced Spatial Clustering Algorithms for Smart City Development

  • Authors

    • Venkatesh Iyer Professor, SRM Institute of Science and Technology, India Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2018PI6L7D

    Published 01-03-2018

  • Smart Cities, Spatial Clustering, 3D Geo-Clustering, Dendrogram Clustering, Spatiotemporal Data Analysis, Wireless Sensor Networks, Urban Data Analytics, Energy Efficiency, Data Overlap, Scalability

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    [1]
    V. Iyer, “Advanced Spatial Clustering Algorithms for Smart City Development”, IJETMR, vol. 1, no. 1, pp. 01–08, Jan. 2018, doi: 10.67228/30715636/IJETMR-2018PI6L7D.

  • Abstract

    The evolution of smart cities heavily relies on the efficient analysis of spatial data to enhance urban planning, resource management, and service delivery. Advanced spatial clustering algorithms play a pivotal role in extracting meaningful patterns from vast datasets generated by various sensors and devices. This paper provides a comprehensive review of contemporary spatial clustering techniques, emphasizing their applications in smart city development. We explore algorithms such as 3D geo-clustering, dendrogram clustering, and spatiotemporal data-adaptive clustering, assessing their effectiveness in organizing and interpreting complex spatial data. The study also highlights the challenges associated with implementing these algorithms, including data overlap, energy efficiency, and scalability. By synthesizing current research and case studies, we offer insights into the strengths and limitations of each algorithm, guiding future developments in spatial data analytics for urban environments.

  • References

    [1] Azri, S., U. Ujang & A. Abdul Rahman (2018). DENDROGRAM Clustering for 3D Data Analytics in Smart City. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W9, pp. 247-253.

    [2] Padmapriya, A., & N. Subitha (2013). Clustering Algorithm for Spatial Data Mining: An Overview. International Journal of Computer Applications, 68(10), pp. 28-33.

    [3] Chandra, E. & A. V. P. Anuradha (2011). A Survey on Clustering Algorithms for Data in Spatial Database Management Systems. International Journal of Computer Applications, 24(9), pp. 19-26.

    [4] Ester, M., H.-P. Kriegel, J. Sander & X. Xu (1996). DBSCAN: Density-Based Spatial Clustering of Applications with Noise. (Foundational spatial clustering algorithm widely cited across smart city analytics).

    [5] Ankerst, M., M. M. Breunig, H.-P. Kriegel & J. Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. (Density clustering algorithm addressing variable density).

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    [8] Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. Proceedings of the ACM SIGMOD International Conference on Management of Data, 49–60.

    [9] Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (1998). Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2(2), 169–194.

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    [13] Borah, B., & Bhattacharyya, D. K. (2004). An improved sampling-based DBSCAN for large spatial databases. Proceedings of the International Conference on Intelligent Sensing and Information Processing, 92–96.

    [14] Debnath, M., Tripathi, P. K., & Elmasri, R. (2015). K-DBSCAN: Identifying spatial clusters with differing density levels. Proceedings of the International Workshop on Data Mining with Industrial Applications, 51–60.

    [15] Nageswara Rao, K., Govardhan, A., & Reddy, K. V. (2016). Development and validation of OPTICS based spatio-temporal clustering technique. Information Sciences, 369, 388–401.

    [16] Damiani, M. L., Issa, H., Fotino, G., Heurich, M., & Cagnacci, F. (2016). Introducing presence and stationarity index to study partial migration patterns: An application of a spatio-temporal clustering technique. International Journal of Geographical Information Science, 30(5), 907–928.

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