Mining Popular Places in a Geo-spatial Region Based on GPS Data Using Semantic Information
25 Mar 2013-pp 262-276
TL;DR: This paper describes how to extract popular and significant places (locations) by analyzing the GPS traces of multiple users, and takes into account the semantic aspects of the places in order to find interesting places in a geo-spatial region.
Abstract: The increasing availability of Global Positioning System (GPS) enabled devices has given an opportunity for learning patterns of human behavior from the GPS traces. This paper describes how to extract popular and significant places (locations) by analyzing the GPS traces of multiple users. In contrast to the existing techniques, this approach takes into account the semantic aspects of the places in order to find interesting places in a geo-spatial region. GPS traces of multiple users are used for mining the places which are frequently visited by multiple users. However, the semantic meanings, such as ‘historical monument’, ‘traffic signal’, etc can further improve the ranking of popular places. The end result is the ranked list of popular places in a given geo-spatial region. This information can be useful for recommending interesting places to the tourists, planning locations for advertisement hoardings, traffic planning, etc.
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TL;DR: One of the basic tasks in moving object analysis, namely the location of hotspots, is studied, which is a (small) region in which an entity spends a significant amount of time.
Abstract: We study one of the basic tasks in moving object analysis, namely the location of hotspots. A hotspot is a (small) region in which an entity spends a significant amount of time. Finding such regions is useful in many applications, for example in segmentation, clustering, and locating popular places. We may be interested in locating a minimum size hotspot in which the entity spends a fixed amount of time, or locating a fixed size hotspot maximizing the time that the entity spends inside it. Furthermore, we can consider the total time, or the longest contiguous time the entity spends in the hotspot. We solve all four versions of the problem. For a square hotspot, we can solve the contiguous-time versions in O(nlogn) time, where n is the number of trajectory vertices. The algorithms for the total-time versions are roughly quadratic. Finding a hotspot containing relatively the most time, compared to its size, takes O(n3) time. Even though we focus on a single moving entity, our algorithms immediately extend to multiple entities. Finally, we consider hotspots of different shape.
31 citations
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TL;DR: A new Physical-Social-aware Interesting Place Discovery (PSIPD) scheme which jointly exploits the location's physical dependency and the visitor's social dependency to solve the interesting place discovery problem using an unsupervised approach.
Abstract: This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches that did not work for users who have little orno historic (training) data. In this paper, we focused onan interesting place discovery problem where the goal isto accurately discover the interesting places in a city thataverage people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. Inparticular, we develop a new Physical-Social-aware InterestingPlace Discovery (PSIPD) scheme which jointly exploits thelocation's physical dependency and the visitor's social dependencyto solve the interesting place discovery problem using anunsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significantperformance improvements compared to all baselines in termsof both estimation accuracy and ranking performance.
21 citations
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TL;DR: The possibility of enriching tourist locations using crowd sourcing approach, which can be used by Tourist Spot Recommender System (TSRS) for mobile users, is explored and a prototype of proposed systems is implemented using java android software development toolkit.
Abstract: With the increase in number of available interesting locations, it becomes difficult for users to find interesting ones, thus imposes a need for recommender systems to suggest interesting locations. Further, to ease the user's decision making, the amount of supplementary information, such as right time to visit, weather conditions, traffic condition, right mode of transport, crowdedness, security alerts, etc., may be annotated with the list of recommended locations. This paper explores the possibility of enriching tourist locations using crowd sourcing approach, which can be used by Tourist Spot Recommender System (TSRS) for mobile users. Proposed crowd sourcing system focuses on getting work done from the crowd currently available at the location under consideration. In proposed system, the contributed information is not limited to ones available on blogs, web pages and sensor-readings from the device etc., but includes proactively-generated user's opinions and perspectives, that are processed to offer immediate knowledge. Our system works in collaboration with a TSRS, takes the list of locations to be recommended to the current user and performs just-in-time information enrichment for those selected set of locations. We have implemented a prototype of proposed systems using java android software development toolkit and evaluated this system by 76 real users.
17 citations
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TL;DR: A new Social-aware Interesting Place Finding (SIPF) approach that solves the interesting place finding problem in social sensing by explicitly incorporating both the user's travel experience and social relationship into a rigorous analytical framework.
Abstract: Social sensing has emerged as a new application paradigm for smart cities where a crowd of social sources (humans or devices on their behalf) collectively contribute a large amount of observations about the physical world. This paper focuses on an interesting place finding problem in social sensing where the goal is to accurately identify the interesting places in a city where people may have strong interests to visit (e.g., parks, museums, historic sites, scenic trails, etc.). Solving this problem is not trivial because (i) many interesting places are not necessarily frequently visited by the average people and hence less likely to be found by the traditional recommendation systems, (ii) the user's social connections could directly affect their visiting behavior and the interestingness judgment of a given place. In this paper, we develop a new Social-aware Interesting Place Finding (SIPF) approach that solves the above problem by explicitly incorporating both the user's travel experience and social relationship into a rigorous analytical framework. The evaluation results showed that the new approach significantly outperforms the state-of-the-arts using two real-world datasets collected from location-based social network service.
9 citations
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TL;DR: Two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; a clustering approach to recover the main points of interest from GPS trajectories; and a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectory at an aggregate level are proposed.
Abstract: Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives With a special focus on GPS technology in travel su
8 citations
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TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.
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TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.
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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
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TL;DR: The Cyberguide project is presented, in which the authors are building prototypes of a mobile context‐aware tour guide that is used to provide more of the kind of services that they come to expect from a real tour guide.
Abstract: Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to offer greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of different hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.
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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 Popular Places in a Geo-spat..." refers methods in this paper
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