2022
Cesario, E.; Uchubilo, P. I.; Vinci, A.; Zhu, X.
Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments Journal Article
In: Pervasive and Mobile Computing, vol. 86, 2022, ISSN: 15741192.
Abstract | Links | BibTeX | Tag: Multi-density city hotspots, smart city, Urban computing
@article{Cesario2022,
title = {Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments},
author = {E. Cesario and P. I. Uchubilo and A. Vinci and X. Zhu},
doi = {10.1016/j.pmcj.2022.101687},
issn = {15741192},
year = {2022},
date = {2022-01-01},
journal = {Pervasive and Mobile Computing},
volume = {86},
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.},
keywords = {Multi-density city hotspots, smart city, Urban computing},
pubstate = {published},
tppubtype = {article}
}
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.