Identifying Points of Interest and Similar Individuals from Raw GPS Data
This work addresses human mobility analysis for urban planning and social networking, but it is incremental as it applies existing data mining methods to GPS data.
The paper tackled the problem of analyzing raw GPS data to identify points of interest and user similarities, with results showing satisfactory performance for urban planning and social networks.
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.