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Extracting Urban Patterns from Location-based Social Networks
"... Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as well as produce services to take advantage of soc ..."
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Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as well as produce services to take advantage of social commonalities between people. In this paper we set out to address the problem of extracting urban patterns from fragments of multiple and sparse people life traces, as they emerge from the participation to social networks. To investigate this challenging task, we analyzed 13 millions Twitter posts (3 GB) of data in New York. Then we test upon this data a probabilistic topic models approach to automatically extract urban patterns from location-based social network data. We find that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.
Checking In or Checked In: Comparing Large-Scale Manual and Automatic Location Disclosure Patterns
"... Studies on human mobility are built on two fundamentally different data sources: manual check-in data that originates from location-based social networks and automatic checkin data that can be automatically collected through various smartphone sensors. In this paper, we analyze the differences and s ..."
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Studies on human mobility are built on two fundamentally different data sources: manual check-in data that originates from location-based social networks and automatic checkin data that can be automatically collected through various smartphone sensors. In this paper, we analyze the differences and similarities of manual check-ins from Foursquare and automatic check-ins from Nokia’s Mobile Data Challenge. Several new findings follow from our analysis: (1) While automatic checking-in overall results in more visits than manual checking-in, the check-in levels are comparable when visiting new places. (2) Daily and weekly check-in activity patterns are similar for both systems except for Saturdays – when manual check-ins are relatively more probable. (3) A recently proposed rank distribution to describe human mobility, so far validated on manual check-in data, also holds for automatic check-in data given a slight modification to the definition of rank. (4) The patterns described by automatic check-ins are in general more predictable. We also address the question of whether it is possible to find matching places across the two check-in systems. Our analysis shows that while this is challenging in areas such as city centers, our method achieves an accuracy of 51 % for places that are not homes of phone users.
Exploring Trajectory-Driven Local Geographic Topics in Foursquare
"... The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich i ..."
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The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users ’ trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users ’ trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.
Transfer Learning for Activity Recognition via Sensor Mapping
- PROCEEDINGS OF THE TWENTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Activity recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving activity recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a la ..."
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Activity recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving activity recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a large number of sensor readings as training data. Labeling sensor readings for their corresponding activities is a time-consuming task. In practice, we often have a set of labeled training instances ready for an activity recognition task. If we can transfer such knowledge to a new activity recognition scenario that is different from, but related to, the source domain, it will ease our effort to perform manual labeling of training data for the new scenario. In this paper, we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in activity recognition problem. We validate our framework on two different datasets and compare it against previous approaches of activity recognition, and demonstrate its effectiveness.
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
"... Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topic model that we call the distant n-gram topi ..."
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Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topic model that we call the distant n-gram topic model (DNTM) to address the problem of learning long duration human location sequences. The DNTM is based on Latent Dirichlet Allocation (LDA). We define the generative process for the model, derive the inference procedure and evaluate our model on real mobile data. We consider two different real-life human datasets, collected by mobile phone locations, the first considering GPS locations and the second considering cell tower connections. The DNTM successfully discovers topics on the two datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model on unseen data. We find that the DNTM consistantly outperforms LDA as the sequence length increases. 1.
Discovering and Predicting User Routines by Differential Analysis of Social Network Traces
"... Abstract—The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore ..."
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Abstract—The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as “user routines”. We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users ’ daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location. I.
1 Tracking Mobile Users in Wireless Networks via Semi-Supervised Co-Localization
"... Abstract—Recent years have witnessed growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labelled data in a wireless sensor network, where a major difficulty arises from t ..."
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Abstract—Recent years have witnessed growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labelled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters, or access points. To solve this problem, we have developed a novel machine-learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile-users ’ locations and the locations of access points. Our framework exploits both labelled and unlabelled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labelled and unlabelled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase co-localization to an online and incremental model that can deal with labelled and unlabelled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed at three different test-beds.
Modeling and Discovering Occupancy Patterns in Sensor Networks using Latent Dirichlet Allocation
"... Abstract. This paper presents a novel way to perform probabilistic modeling of occupancy patterns from a sensor network. The approach is based on the Latent Dirichlet Allocation (LDA) model. The application of the LDA model is shown using a real dataset of occupancy logs from the sensor network of a ..."
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Abstract. This paper presents a novel way to perform probabilistic modeling of occupancy patterns from a sensor network. The approach is based on the Latent Dirichlet Allocation (LDA) model. The application of the LDA model is shown using a real dataset of occupancy logs from the sensor network of a modern office building. LDA is a generative and unsupervised probabilistic model for collections of discrete data. Con-tinuous sequences of just binary sensor readings are segmented together in order to build the dataset discrete data (bag-of-words). Then, these bag-of-words are used to train the model with a fixed number of topics, also known as routines. Preliminary obtained results state that the LDA model successfully found latent topics over all rooms and therefore obtain the dominant occupancy patterns or routines on the sensor network.
Automatic Annotation of Daily Activity from Smartphone-based Multisensory Streams
- In Mobile Computing, Applications, and Services
, 2013
"... Abstract. We present a system for automatic annotation of daily experience from multisensory streams on smartphones. Using smartphones as platform fa-cilitates collection of naturalistic daily activity, which is difficult to collect with multiple on-body sensors or array of sensors affixed to indoor ..."
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Abstract. We present a system for automatic annotation of daily experience from multisensory streams on smartphones. Using smartphones as platform fa-cilitates collection of naturalistic daily activity, which is difficult to collect with multiple on-body sensors or array of sensors affixed to indoor locations. How-ever, recognizing daily activities in unconstrained settings is more challenging than in controlled environments: 1) multiples heterogeneous sensors equipped in smartphones are noisier, asynchronous, vary in sampling rates and can have miss-ing data; 2) unconstrained daily activities are continuous, can occur concurrently, and have fuzzy onset and offset boundaries; 3) ground-truth labels obtained from the user’s self-report can be erroneous and accurate only in a coarse time scale. To handle these problems, we present in this paper a flexible framework for incor-porating heterogeneous sensory modalities combined with state-of-the-art classi-fiers for sequence labeling. We evaluate the system with real-life data containing 11721 minutes of multisensory recordings, and demonstrate the accuracy and ef-ficiency of the proposed system for practical lifelogging applications. Key words: mobile computing, lifelogging, activity recognition, automatic an-notation 1
Helix: Unsupervised grammar induction for structured activity recognition
- in: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), 2011
"... Abstract-The omnipresence of mobile sensors has brought tremendous opportunities to ubiquitous computing systems. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi ..."
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Abstract-The omnipresence of mobile sensors has brought tremendous opportunities to ubiquitous computing systems. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion. In this paper, we propose building a grammar to address all these challenges using a language-based approach. The proposed algorithm, called Helix, first generates an initial vocabulary using unlabeled sensor readings, followed by iteratively combining statistically collocated sub-activities across sensor dimensions and grouping similar activities together to discover higher level activities. The experiments using a 20-minute ping-pong game demonstrate favorable results compared to a Hierarchical Hidden Markov Model (HHMM) baseline. Closer investigations to the learned grammar also shows that the learned grammar captures the natural structure of the underlying activities.
