Abstract
The growth of data volume collected in urban contexts opens up to their exploitation for improving citizens’ quality-of-life and city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. Moreover, due to the large-scale diffusion of GPS and scanning devices, most of the available data are geo-referenced. Considering such an abundance of data, a very desirable and common task is to identify homogeneous regions in spatial data by partitioning a city into uniform regions based on pollution density, mobility spikes, crimes, or on other characteristics. Density-based clustering algorithms have been shown to be very suitable to detect density-based regions, i.e. areas in which urban events occur with higher density than the remainder of the dataset. Nevertheless, an important issue of such algorithms is that, due to the adoption of global parameters, they fail to identify clusters with varied densities, unless the clusters are clearly separated by sparse regions. In this paper we provide a preliminary analysis about how hierarchical clustering can be used to discover spatial clusters of different densities, in spatial urban data. The algorithm can automatically estimate the area of data having different densities, it can automatically estimate parameters for each cluster so as to reduce the requirement for human intervention or domain knowledge.
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References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28, 49–60 (1999)
Catlett, C., Cesario, E., Talia, D., Vinci, A.: A data-driven approach for spatio-temporal crime predictions in smart cities. In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 17–24. IEEE (2018)
Catlett, C., Cesario, E., Talia, D., Vinci, A.: Spatio-temporal crime predictions in smart cities: a data-driven approach and experiments. Pervasive Mob. Comput, 53, 62–74 (2019)
Catlett, C., et al.: Plenario: an open data discovery and exploration platform for urban science. IEEE Data Eng. Bull. 37(4), 27–42 (2014)
Cesario, E., Talia, D.: Distributed data mining patterns and services: an architecture and experiments. Concurrency Comput. Pract. Experience 24(15), 1751–1774 (2012)
Cicirelli, F., Guerrieri, A., Mastroianni, C., Spezzano, G., Vinci, A. (eds.): The Internet of Things for Smart Urban Ecosystems. IT. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96550-5
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
Lin, C.Y., Chang, C.C., Lin, C.C.: A new density-based scheme for clustering based on genetic algorithm. Fundamenta Informaticae 68(4), 315–331 (2005)
Liu, P., Zhou, D., Wu, N.: VDBSCAN: varied density based spatial clustering of applications with noise. In: 2007 International Conference on Service Systems and Service Management, pp. 1–4. IEEE (2007)
Mitra, S., Nandy, J.: KDDClus: a simple method for multi-density clustering. In: Proceedings of International Workshop on Soft Computing Applications and Knowledge Discovery (SCAKD 2011), Moscow, Russia, pp. 72–76. Citeseer (2011)
Xiong, Z., Chen, R., Zhang, Y., Zhang, X.: Multi-density dbscan algorithm based on density levels partitioning. J. Inf. Comput. Sci. 9(10), 2739–2749 (2012)
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Cesario, E., Vinci, A., Zhu, X. (2020). Hierarchical Clustering of Spatial Urban Data. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11973. Springer, Cham. https://doi.org/10.1007/978-3-030-39081-5_20
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DOI: https://doi.org/10.1007/978-3-030-39081-5_20
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