  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.

Applications;Other Computer Science