Discovering Multi-density Urban Hotspots in a Smart City

Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.

I. Introduction

Cities all around the world are in constant evolution due to numerous factors, such as fast urbanization and new ways of communication and transportation. In particular, since the first years of the 21 ^st century, we have been experiencing the most rapid urbanization growth in history. United Nations reported that the population living in cities is expected to grow from 2.86 billion in 2000 to 4.98 billion in 2030, thus having 60% of worldwide people living in metropolitan areas by the next decade [1]. Such rapid urbanization brings significant environmental, economic, and social changes; and raises new issues in city development, public policy, and resource management. Also, leveraged by a large-scale diffusion of sensing networks, GPS, and image scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Considering such an abundance of data, the acquisition, integration, and analysis of urban spatial information is becoming crucial [2]. Modern cities largely exploit data-driven models to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows.