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21
Urban Computing: Concepts, Methodologies, and Applications
"... Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geo ..."
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Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the
Tutorial on Location-Based Social Networks
- In proceeding of International conference on World Wide Web
"... This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on ..."
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This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on
Identifying users profiles from mobile calls habits
- In the Proc. of the ACM SIGKDD Int.Workshop on Urban Computing, UrbComp ’12
, 2012
"... The huge quantity of positioning data registered by our mo-bile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to part ..."
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The huge quantity of positioning data registered by our mo-bile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into pro-files like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the pre-vious step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.
A habit mining approach for discovering similar mobile users
- in Proc. WWW
, 2012
"... Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmenta-tion for market analysis, etc. Recently, the progressing abil-ity to sense user contexts of smart mobile devices makes it ..."
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Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmenta-tion for market analysis, etc. Recently, the progressing abil-ity to sense user contexts of smart mobile devices makes it possible to discover mobile users with similar habits by min-ing their habits from their mobile devices. However, though some researchers have proposed effective methods for mining user habits such as behavior pattern mining, how to lever-age the mined results for discovering similar users remains less explored. To this end, we propose a novel approach for conquering the sparseness of behavior pattern space and thus make it possible to discover similar mobile users with respect to their habits by leveraging behavior pattern min-ing. To be specific, first, we normalize the raw context log of each user by transforming the location-based context data and user interaction records to more general representation-s. Second, we take advantage of a constraint-based Bayesian Matrix Factorization model for extracting the latent com-mon habits among behavior patterns and then transforming behavior pattern vectors to the vectors of mined common habits which are in a much more dense space. The experi-ments conducted on real data sets show that our approach outperforms three baselines in terms of the effectiveness of discovering similar mobile users with respect to their habit-s.
Mining Significant Time Intervals for Relationship Detection
"... Abstract. Spatio-temporal data collected from GPS have become an important resource to study the relationships of moving objects. While previous studies focus on mining objects being together for a long time, discovering real-world relationships, such as friends or colleagues in human trajectory dat ..."
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Abstract. Spatio-temporal data collected from GPS have become an important resource to study the relationships of moving objects. While previous studies focus on mining objects being together for a long time, discovering real-world relationships, such as friends or colleagues in human trajectory data, is a fundamentally different challenge. For example, it is possible that two individuals are friends but do not spend a lot of time being together every day. However, spending just one or two hours together at a location away from work on a Saturday night could be a strong indicator of friend relationship. Based on the above observations, in this paper we aim to analyze and detectsemantically meaningful relationships inasupervisedway.Thatis, with aninterestedrelationship inmind,ausercanlabel some object pairs with and without such relationship. From labeled pairs, we will learn what time intervals are the most important ones in order to characterize this relationship. These significant time intervals, namely T-Motifs, are thenusedtodiscover relationships hiddenintheunlabeledmovingobject pairs. While the search for T-Motifs could be time-consuming, we design two speed-up strategies to efficiently extract T-Motifs. We use both real and synthetic datasets to demonstrate the effectiveness and efficiency of our method. 1
Measuring user similarity with trajectory patterns: Principles and new metrics
- In: Proc. APWeb. LNCS
, 2014
"... Abstract. The accumulation of users ’ whereabouts in location-based applica-tions has made it possible to construct user mobility profiles. Trajectory patterns, i.e., traces of places of interest that a user frequently visits, are among the most popular models of mobility profiles. In this paper, we ..."
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Abstract. The accumulation of users ’ whereabouts in location-based applica-tions has made it possible to construct user mobility profiles. Trajectory patterns, i.e., traces of places of interest that a user frequently visits, are among the most popular models of mobility profiles. In this paper, we revisit measuring user sim-ilarity using trajectory patterns, which is an important supplement for friend rec-ommendation in on-line social networks. Specifically, we identify and formalise a number of basic principles that should hold when quantifying user similarity with trajectory patterns. These principles allow us to evaluate existing metrics in the literature and demonstrate their insufficiencies. Then we propose for the first time a new metric that respects all the identified principles. The metric is extended to deal with location semantics. Through experiments on a real-life tra-jectory dataset, we show the effectiveness of our new metrics. 1
De-anonymization attack on geolocated data
, 2013
"... Abstract—With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which ..."
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Abstract—With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design two distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms such as downsampling. Keywords-Privacy, geolocation, inference attack, deanonymization. I.
K.: Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures. Computers, Environment and Urban Systems 54
, 2015
"... While geocoding returns coordinates for a full or partial address, the converse process of reverse geocoding maps coordinates to a set of candidate place identifiers such as addresses or toponyms. For example, numerous Web APIs map geographic point coordinates, e.g., from a user’s smartphone, to an ..."
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While geocoding returns coordinates for a full or partial address, the converse process of reverse geocoding maps coordinates to a set of candidate place identifiers such as addresses or toponyms. For example, numerous Web APIs map geographic point coordinates, e.g., from a user’s smartphone, to an ordered set of nearby Places Of Interest (POI). Typically, these services return the k nearest POI within a certain radius and measure distance to order the results. Reverse geocoding is a crucial task for many applications and research questions as it translates between spatial and platial views on geographic location. What makes this process difficult is the uncertainty of the queried location and of the point features used to represent places. Even if both could be determined with a high level of accuracy, it would still be unclear how to map a smartphone’s GPS fix to one of many possible places in a multi-story building or a shopping mall. In this work, we break up the dependency on space alone by introducing time as a second variable for reverse geocoding. We mine the geosocial behavior of users of online location-based social networks to extract temporal semantic signatures. In analogy to the notion of scale distortion in cartography, we present a model that uses these signatures to distort the location of POI relative to the query location and time, thereby reordering the set of potentially matching places. We demonstrate the strengths of our method by evaluating it against a purely spatial baseline by determining the Mean Reciprocal Rank and the normalized Discounted Cumulative Gain. Our method performs substantially better than said baseline.
User Oriented Trajectory Similarity Search
"... Trajectory similarity search studies the problem of finding a trajectory from the database such the found trajectory most similar to the query trajectory. Past research mainly focused on two aspects: shape similarity search and semantic similarity search, leaving personalized similarity search untou ..."
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Trajectory similarity search studies the problem of finding a trajectory from the database such the found trajectory most similar to the query trajectory. Past research mainly focused on two aspects: shape similarity search and semantic similarity search, leaving personalized similarity search untouched. In this paper, we propose a new query which takes user’s preference into consideration to provide personalized searching. We define a new data model for this query and identify the efficiency issue as the key challenge: given a user specified trajectory, how to efficiently retrieve the most similar trajectory from the database. By taking advantage of the spatial localities, we develop a two-phase algorithm to tame this challenge. Two optimized strategies are also developed to speed up the query process. Both the theoretical analysis and the experiments demonstrate the high efficiency of the proposed method. 1.
Post-hoc User Traceability Analysis in Electronic Toll Pricing Systems
"... Abstract. Electronic Toll Pricing (ETP), a location-based vehicular service, al-lows users to pay tolls without stopping or even slowing down their cars. User location records are collected so as to calculate their payments. However, users have privacy concerns as locations are considered as private ..."
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Abstract. Electronic Toll Pricing (ETP), a location-based vehicular service, al-lows users to pay tolls without stopping or even slowing down their cars. User location records are collected so as to calculate their payments. However, users have privacy concerns as locations are considered as private information. In this paper, we focus on user traceability in ETP systems where anonymous location records are stored by the service providers. Based on user toll payment informa-tion, we propose a post-hoc analysis of user traceability, which aims at computing a user’s all possible traces. Moreover, we propose several methods to improve the effectiveness of the analysis by combining other contextual information and pro-pose a number of optimisations to improve its efficiency as well. We develop a prototype and evaluate the effectiveness of the analysis by conducting extensive experiments on a number of simulated datasets. 1
