Mobility data captures the locations of moving objects such as humans, animals, and cars.
With the availability of GPS-equipped mobile devices and other inexpensive location …

Recent digitalization has led to a significant increase in Data usage and WiFi penetration
rates across the globe. WiFi Media Access Control (MAC) data, allows for unannounced and …

WiFi in indoor environments exhibits spatio-temporal variations in terms of coverage and
interference in typical WLAN deployments with multiple APs, motivating the need for …

In this paper, we describe our approach that enables spatial human analytics using WiFi
probes. Our approach works as follows: we first set up our beacons in the area. Then, we …

This study applies passive mobile positioning data such as Call Volume, Handover, and
Erlang to detect the spatiotemporal distributions of urban activities. We obtained hourly …

Purpose—In this chapter, we will review several alternative methods of collecting data from
mobile phones for human mobility analysis. We propose considering cellular network …

Can we automatically identify relevant places and events happening in the city from the
analysis of mobile network use? In this paper we present a methodology to discover events …

This paper presents Beanstalk, an interactive platform to assist communities in easily
running systematic analysis of mobility patterns of tourists at their destinations, contributing …

In this paper we present a visual analytics approach for deriving spatio-temporal patterns of
collective human mobility from a vast mobile network traffic data set. More than 88 million …

Occupational health can be strongly influenced by the indoor environment as people spend
90% of their time indoors. Although indoor air quality (IAQ) is not typically monitored, IAQ …