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Title	Multi-Density Urban Hotspots Detection In Smart Cities: A Data-Driven Approach And Experiments
ID_Doc	38191
Authors	Cesario E.; Uchubilo P.I.; Vinci A.; Zhu X.
Year	2022
Published	Pervasive and Mobile Computing, 86
DOI	http://dx.doi.org/10.1016/j.pmcj.2022.101687
Abstract	The detection of city hotspots from geo-referenced urban data is a valuable knowledge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccurate results. Since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents CHD (City Hotspot Detector), a multi-density approach to discover urban hotspots in a city, by reporting an extensive comparative analysis with three classic density-based clustering algorithms, on both state-of-the-art and real-world datasets. The comparative experimental evaluation in an urban scenario shows that the proposed multi-density algorithm, enhanced by an additional rolling moving average technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature. © 2022 Elsevier B.V.
Author Keywords	Multi-density city hotspots; Smart city; Urban computing

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