Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments

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.