Mining GPS data to determine interesting locations
28 Mar 2011-pp 8
TL;DR: This paper aims to analyze aggregate GPS information of multiple users to mine a list of interesting locations and rank them, and shows the results of applying the methods on a large real life GPS dataset of sixty two users collected over a period of two years.
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.
Citations
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TL;DR: This paper focuses on the conceptual benefits and risks such an integration of sensor data into social media in the case of a patient room and introduces a way to deal with these problems.
Abstract: In a hospital, information exchange is essential to save lives and to prevent life-endangering mistakes. Information exchange is supported by a hospital information system (HIS). From a theoretical perspective, the deployment of an HIS is promising because it reduces errors and duplication of information. In practice, however, there are some major problems concerning the usage of such a system. One way to deal with these problems is introduced in this paper: the integration of sensor data into social media. The paper concentrates on the conceptual benefits and risks such an integration may generate. It focuses on the case of a patient room.
47 citations
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TL;DR: A thorough description of a method that can be used to generate a number of different variables related to the constructs of mobility and participation from GPS data, with the help of ST-DBSCAN, a spatiotemporal data mining algorithm is provided.
Abstract: Community participation, as indicated by mobility and engagement in socially meaningful activities, is a central component of health based on the International Classification of Health, Functioning, and Disease (WHO, 2001). Global positioning systems (GPS) technology is emerging as a tool for tracking mobility and participation in health and disability-related research. This paper fills a gap in the literature and provides a thorough description of a method that can be used to generate a number of different variables related to the constructs of mobility and participation from GPS data. Here, these variables are generated with the help of ST-DBSCAN, a spatiotemporal data mining algorithm. The variables include the number of unique destinations, activity space area, distance traveled, time in transit, and time at destinations. Data obtained from five individuals with psychiatric disabilities who carried GPS-enabled cell phones for two weeks are presented. Within- and across- individual variability on these constructs was observed. Given the feasibility of gathering data with GPS, larger scale studies of mobility and participation employing this method are warranted.
31 citations
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TL;DR: In this article, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.
Abstract: We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.
30 citations
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TL;DR: Performing experiments on real users data, it is shown that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if the authors do not account for time features.
Abstract: Current and future mobile applications massively exploit the knowledge of the user’s location to improve the offered services. However, user localization is by far one of the oldest and most difficult issues, due to its dynamism and to unavailability of some technologies in indoor environments. The enhanced localization solution (ELS) proposed in this paper is an innovative self adaptive solution that smartly combines standard location tracking techniques (e.g., GPS, GSM and WiFi localization), newly built-in technologies, as well as human mobility modelling and machine learning techniques. The main purposes of this solution are: to reduce the impact the service has, on the mobile device resources usage (mainly the battery consumption), when it is asked to provide a continuous localization; to help in preserving the privacy of the user, by running the whole system on the mobile device, without relying on a back-end server; and furthermore, to offer an ubiquitous coverage. The aspects mainly explored in this paper are: location prediction and mobility modelling, required to optimally estimate the current location with ELS. We are finding that people tend to move, for most of the time, among a limited set of places and that this can be modelled with a user prediction graph, which is further used to predict the next movement. Performing experiments on real users data, we show that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if we do not account for time features.
21 citations
Cites background from "Mining GPS data to determine intere..."
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TL;DR: This study proposed a method and interest measures to discover socially important locations that consider historical user data and each user’s (individual's) preferences and showed that the proposed algorithm outperforms the naive alternative.
Abstract: Socially important locations are places that are frequently visited by social media users in their social media life Discovering socially interesting, popular or important locations from a location based social network has recently become important for recommender systems, targeted advertisement applications, and urban planning, etc However, discovering socially important locations from a social network is challenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for developing computationally efficient approaches, and the difficulty of modeling human behavior In the literature, several studies are conducted for discovering socially important locations However, majority of these studies focused on discovering locations without considering historical data of social media users They focused on analysis of data of social groups without considering each user’s preferences in these groups In this study, we proposed a method and interest measures to discover socially important locations that consider historical user data and each user’s (individual’s) preferences The proposed algorithm was compared with a naive alternative using real-life Twitter dataset The results showed that the proposed algorithm outperforms the naive alternative
21 citations
References
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TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Abstract: The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.
1,743 citations
"Mining GPS data to determine intere..." refers background in this paper
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TL;DR: This work presents a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales and incorporates these locations into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
Abstract: Wearable computers have the potential to act as intelligent agents in everyday life and to assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
1,155 citations
"Mining GPS data to determine intere..." refers methods in this paper
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TL;DR: An approach based on supervised learning to infer people's motion modes from their GPS logs is proposed, which identifies a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used.
Abstract: Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems As a kind of user behavior, the transportation modes, such as walking, driving, etc, that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs The contribution of this work lies in the following two aspects On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement
918 citations
"Mining GPS data to determine intere..." refers background in this paper
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12 Oct 2003
TL;DR: In this paper, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.
Abstract: We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.
588 citations
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TL;DR: A method called Predestination is described that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses, to produce a probabilistic map of destinations.
Abstract: We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.
531 citations
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