Extracting User Habits from Google Maps History Logs

Abstract

The exponential growth in the usage of smart devices, such as smartphones, interconnected wearables etc., creates a huge amount of information to manage and many research and business opportunities. Such smart devices become a useful tool for user movement recognition, since they are equipped with different types of sensors and processors that can process sensor data and extract useful knowledge. Taking advantage of the GPS sensor, they can collect the timestamped geographical coordinates of the user, which can then be used to extract the geographical location and movement of the user. Our work, takes this analysis one step ahead and attempts to identify the user's behavior and habits, based on the analysis of user's location data. This type of information can be valuable for many other domains such as Recommender Systems, targeted/personalized advertising etc. In this paper, we present a methodology for analyzing user location information in order to identify user habits. To achieve this, we analyze user's GPS logs provided through his Google location history, we find locations that user usually spends more time, and after identifying the user's frequently preferred transportation types and trajectories, we find what type of places the user visits in a regular base (such as cinemas, restaurants, gyms, bars etc) and extract the habits that the user is most likely to have.

Mining Human Mobility Data to Discover Locations and Habits

“…The top two MPs are latitude=39.99993, longitude=116.32730 which has 659 visits, and latitude=40.01086, longitude=116.32186 with a counting of 235 times. Here we set the Home (Qinghuayuan Residential District) and Work (Tsinghua University Northwest) locations respectively based on the frequency of these observations as many other works propose [2,5,10,14]. To perform a visual inspection of the formed clusters, figure 3 illustrates the differences obtained using each one of the methods.…”

“…[12] used more than 800 million of CDR data to identify weekly patterns of human mobility through mobile phone data. In [10], the authors present a methodology based on densitybased clustering, clustering-based sequential mining and Apriori algorithm for analyzing user location information in order to identify user habits.…”

Making Maps Available for Play

“…Our hope in providing our catalog, interface properties, and high-level play activities is to not only bridge these two fields of research and design, but to begin to unite their purposes. If current research focuses on understanding how people use their maps, then integrating how game cartography interfaces nudge users toward where they need to go whether they know they should go there or not would meet current research [97].…”

From mobility data to habits and common pathways

“…The authors also use an event‐based algorithm to cluster individuals and identify weekly patterns. In Sardianos et al (2018), a methodology that analyses user location information in order to identify users' habits is introduced by making use of clustering, sequential mining, and Apriori algorithms. In Yang, Cheng, and Chen (2018), the authors proposed a framework for mining individual similarity based on long‐term trajectory data in which emphasizes the essential role of the spatio‐temporal interactions between individuals and their personally significant places.…”

“…Making use of the data generated by these devices at high speeds and decreasing costs, a range of applications can leverage from the discovering of locations, similar users and their habits. Recommender systems (Zheng, Zhang, Ma, Xie, & Ma, 2011), location‐based advertising (Sardianos, Varlamis, & Bouras, 2018), carpooling (Trasarti, Pinelli, Nanni, & Giannotti, 2011) and marketing analysis segmentation (Wu et al, 2009) are among them.…”