The Study of Urban Residential's Public Space Activeness using Space-centric Approach

TL;DR

This paper uses IoT sensors and spectral clustering to analyze and understand the usage patterns of urban public spaces, highlighting key factors like residential density and commercial facilities.

Contribution

It introduces a data processing system that generates space-centric insights into urban public space utilization using sensor data and clustering techniques.

Findings

Residential density significantly influences space activeness.

Commercial facilities are key factors in public space utilization.

Heuristic feature inference identifies main utilization drivers.

Abstract

With the advancement of the Internet of Things (IoT) and communication platform, large scale sensor deployment can be easily implemented in an urban city to collect various information. To date, there are only a handful of research studies about understanding the usage of urban public spaces. Leveraging IoT, various sensors have been deployed in an urban residential area to monitor and study public space utilization patterns. In this paper, we propose a data processing system to generate space-centric insights about the utilization of an urban residential region of multiple points of interest (PoIs) that consists of 190,000m$^2$ real estate. We identify the activeness of each PoI based on the spectral clustering, and then study their corresponding static features, which are composed of transportation, commercial facilities, population density, along with other characteristics. Through the heuristic features inferring, the residential density and commercial facilities are the most significant factors affecting public place utilization.