2 research outputs found
Identifying Points of Interest and Similar Individuals from Raw GPS Data
Smartphones and portable devices have become ubiquitous and part of
everyone's life. Due to the fact of its portability, these devices are perfect
to record individuals' traces and life-logging generating vast amounts of data
at low costs. These data is emerging as a new source for studies in human
mobility patterns raising the number of research projects and techniques aiming
to analyze and retrieve useful information from it. The aim of this paper is to
explore GPS raw data from different individuals in a community and apply data
mining algorithms to identify meaningful places in a region and describe user's
profiles and its similarities. We evaluate the proposed method with a
real-world dataset. The experimental results show that the steps performed to
identify points of interest (POIs) and further the similarity between the users
are quite satisfactory serving as a supplement for urban planning and social
networks.Comment: Conference paper at Mobility IoT 2018 -
http://mobilityiot2018.eai-conferences.org/full-program
Mining Human Mobility Data to Discover Locations and Habits
Many aspects of life are associated with places of human mobility patterns
and nowadays we are facing an increase in the pervasiveness of mobile devices
these individuals carry. Positioning technologies that serve these devices such
as the cellular antenna (GSM networks), global navigation satellite systems
(GPS), and more recently the WiFi positioning system (WPS) provide large
amounts of spatio-temporal data in a continuous way. Therefore, detecting
significant places and the frequency of movements between them is fundamental
to understand human behavior. In this paper, we propose a method for
discovering user habits without any a priori or external knowledge by
introducing a density-based clustering for spatio-temporal data to identify
meaningful places and by applying a Gaussian Mixture Model (GMM) over the set
of meaningful places to identify the representations of individual habits. To
evaluate the proposed method we use two real-world datasets. One dataset
contains high-density GPS data and the other one contains GSM mobile phone data
in a coarse representation. The results show that the proposed method is
suitable for this task as many unique habits were identified. This can be used
for understanding users' behavior and to draw their characterizing profiles
having a panorama of the mobility patterns from the data