EXTRACTING INFORMATION FROM USER’S LOCALIZATION DATA

Abstract

The availability of location-tracking devices such as GPS, Cellular Networks and other devices provides the facility to log a person or device locations automatically. This creates spatio-temporal datasets of user's movement with features like latitude, longitude of a particular location on a specific day and time. With the help of these features different patterns of user movement can be collected, queues and analyzed. In this research work, we are focused on user's movement patterns and frequent movements of users on a particular place, day or time interval, trajectory and mode of travel. To achieve this we used Association Rule mining concept based on Apriori algorithm to find interesting movement patterns. Our dataset for this experiment is from Geolife project conducted by Microsoft Research Asia [1][2][3] which consist of 17,621 trajectories, 24 million points logged every 1-5 seconds or 5-10 meters per point. First, we considered the spatial part of data; A two-dimensional space of (latitude, longitude) which ranges from minimum to maximum pair of latitude, longitude logged for all users. We distributed this space into equal grids along both dimensions to reach a significant spatial distance range. Grids with high density points are sub-divided into further smaller grid cells. For the temporal part of data; we transform the dates into days of the week to distinguish the patterns on a particular day and 24-time intervals of 1 hours each to split a day in order to distinguish peak hours of movement. Finally, we mine the data using association rules with attributes/features like user id, grid id (unique identifier for each spatial range/region of latitude and longitude), day and time, trajectory and label. This enables us to discover patterns of user's frequent movement and similarly, for a particular grid. This will give us a better recommendation based on the patterns for a set of like users, point of interests and time of day.