| Title |
Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments |
| ID_Doc |
43711 |
| Authors |
Cesario, E; Uchubilo, PI; Vinci, A; Zhu, XT |
| Title |
Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.pmcj.2022.101687 |
| Abstract |
The detection of city hotspots from geo-referenced urban data is a valuable knowl-edge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccu-rate 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 ur-ban 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 av-erage technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature.(c) 2022 Elsevier B.V. All rights reserved. |
| Author Keywords |
Multi-density city hotspots; Smart city; Urban computing |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000868578700005 |
| WoS Category |
Computer Science, Information Systems; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
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