Mining GPS data to determine interesting locations

Abstract

It is possible to obtain fine grained location information fairly easily using Global Positioning System (GPS) enabled devices. It becomes easy to track an individual's location and trace her trajectory using such devices. By aggregating this data and analyzing multiple users' trajectory a lot of useful information can be extracted. In this paper, we aim to analyze aggregate GPS information of multiple users to mine a list of interesting locations and rank them. By interesting locations we mean the geographical locations visited by several users. It can be an office, university, historical place, a good restaurant, a shopping complex, a stadium, etc. To achieve this various relational algebra operations and statistical operations are applied on the GPS trajectory data of multiple users. The end result is a ranked list of interesting locations. We show the results of applying our methods on a large real life GPS dataset of sixty two users collected over a period of two years.

Using global positioning systems to study health-related mobility and participation

“…These include Time-Clustering-based Behavior Analysis (TCBA) (Ji et al, 2012), Spatial-Temporal GRID (ST-GRID) (Wang et al, 2006), and Tree-Based Hierarchical Graphing (TBHG) (Khetarpaul et al, 2011). The data mining tool used here is a point density-based algorithm called Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) (Birant and Kut, 2007).…”

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

“…Khetarpaul et al (2011) proposed relational algebra based operations to discover interesting locations by analyzing trajectories of multiple users. Dokuz and Celik (2017) proposed an approach to discover socio-spatially important locations from a group of social media users.…”

A framework for tourist recommendation system exploiting geo-tagged photos

“…This model regards an individual's access to a location as a directed link from the user to that location. In this paper [3], they analyze the aggregated GPS information for multiple users in order to mine a list of interesting locations and rank them. To achieve this various relational algebra operations and statistical operations are applied to the GPS trajectory data of multiple users.…”