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1. Semantically Diverse Path Search

   https://doi.org/10.1109/MDM48529.2020.00028  

   Teng, Xu ; Trajcevski, Goce ; Kim, Joon-Seok ; Zufle, Andreas ( June 2020 , 21st {IEEE} International Conference on Mobile Data Management, {MDM} 2020, Versailles, France, June 30 - July 3, 2020)

   Location-Based Services are often used to find proximal Points of Interest PoI - e.g., nearby restaurants and museums, police stations, hospitals, etc. - in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. For instance, rather than picking several close-by attractions with similar features - e.g., restaurants with similar menus; museums with similar art exhibitions - a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. We introduce a novel indexing structure - the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set - i.e., the recommended locations among a set of given PoIs - relying on a greedy search strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of proposed methodology over the baselinemore »alternative approaches.« less
2. Time-sensitive POI Recommendation by Tensor Completion with Side Information

   Hui, Bo ; Yan, Da ; Ku, Wei-Shinn ( January 2022 , Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE))

   Context has been recognized as an important factor to consider in personalized recommender systems. Particularly in location-based services (LBSs), a fundamental task is to recommend to a mobile user where he/she could be interested to visit next at the right time. Additionally, location-based social networks (LBSNs) allow users to share location-embedded information with friends who often co-occur in the same or nearby points-of-interest (POIs) or share similar POI visiting histories, due to the social homophily theory and Tobler’s first law of geography. So, both the time information and LBSN friendship relations should be utilized for POI recommendation. Tensor completion has recently gained some attention in time-aware recommender systems. The problem decomposes a user-item-time tensor into low-rank embedding matrices of users, items and times using its observed entries, so that the underlying low-rank subspace structure can be tracked to fill the missing entries for time-aware recommendation. However, these tensor completion methods ignore the social-spatial context information available in LBSNs, which is important for POI recommendation since people tend to share their preferences with their friends, and near things are more related than distant things. In this paper, we utilize the side information of social networks and POI locations to enhance themore »tensor completion model paradigm for more effective time-aware POI recommendation. Specifically, we propose a regularization loss head based on a novel social Hausdorff distance function to optimize the reconstructed tensor. We also quantify the popularity of different POIs with location entropy to prevent very popular POIs from being over-represented hence suppressing the appearance of other more diverse POIs. To address the sensitivity of negative sampling, we train the model on the whole data by treating all unlabeled entries in the observed tensor as negative, and rewriting the loss function in a smart way to reduce the computational cost. Through extensive experiments on real datasets, we demonstrate the superiority of our model over state-of-the-art tensor completion methods.« less
3. Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) Data.

   https://doi.org/10.1007/978-3-319-71470-7_16  

   Lee, Joey Ying ; Tsou, Ming-Hsiang ( December 2017 , Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham)

   Social media services and location-based photo-sharing applications, such as Flickr, Twitter, and Instagram, provide a promising opportunity for studying tourist behaviors and activities. Researchers can use public accessible geo-tagged photos to map and analyze hotspots and tourist activities in various tourist attractions. This research studies geo-tagged Flickr photos collected from the Grand Canyon area within 12 months (2014/12/01–2015/11/30) using kernel density estimate (KDE) mapping, Exif (Exchangeable image file format) data, and dynamic time warping (DTW) methods. Different spatiotemporal movement patterns of tourists and popular points of interests (POIs) in the Grand Canyon area are identified and visualized in GIS maps. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. We found that winter tourists in the Grand Canyon explore fewer POIs comparing to summer tourists based on their Flickr data. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. Weekend tourists are more likely to stay around the lodge area comparing to weekday tourists who have visited more remote areas in the park, such as the north of Pima Point. These tourist activities and spatiotemporal patterns can be used formore »the improvement of national park facility management, regional tourism, and local transportation plans.« less
4. DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features

   https://doi.org/10.1145/3469085  

   Khezerlou, Amin Vahedian ; Zhou, Xun ; Li, Xinyi ; Street, W. Nick ; Li, Yanhua ( August 2021 , ACM Transactions on Intelligent Systems and Technology)

   Urban dispersal events occur when an unexpectedly large number of people leave an area in a relatively short period of time. It is beneficial for the city authorities, such as law enforcement and city management, to have an advance knowledge of such events, as it can help them mitigate the safety risks and handle important challenges such as managing traffic, and so forth. Predicting dispersal events is also beneficial to Taxi drivers and/or ride-sharing services, as it will help them respond to an unexpected demand and gain competitive advantage. Large urban datasets such as detailed trip records and point of interest ( POI ) data make such predictions achievable. The related literature mainly focused on taxi demand prediction. The pattern of the demand was assumed to be repetitive and proposed methods aimed at capturing those patterns. However, dispersal events are, by definition, violations of those patterns and are, understandably, missed by the methods in the literature. We proposed a different approach in our prior work [32]. We showed that dispersal events can be predicted by learning the complex patterns of arrival and other features that precede them in time. We proposed a survival analysis formulation of this problem and proposedmore »a two-stage framework (DILSA), where a deep learning model predicted the survival function at each point in time in the future. We used that prediction to determine the time of the dispersal event in the future, or its non-occurrence. However, DILSA is subject to a few limitations. First, based on evidence from the data, mobility patterns can vary through time at a given location. DILSA does not distinguish between different mobility patterns through time. Second, mobility patterns are also different for different locations. DILSA does not have the capability to directly distinguish between different locations based on their mobility patterns. In this article, we address these limitations by proposing a method to capture the interaction between POIs and mobility patterns and we create vector representations of locations based on their mobility patterns. We call our new method DILSA+. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014 to 2016. Results show that DILSA+ can predict events in the next 5 hours with an F1-score of 0.66. It is significantly better than DILSA and the state-of-the-art deep learning approaches for taxi demand prediction.« less
5. A systematic investigation of piercing-point-dependent seismic azimuthal anisotropy

   https://doi.org/10.1093/gji/ggab285  

   Jia, Yan ; Liu, Kelly H ; Kong, Fansheng ; Liu, Lin ; Gao, Stephen S ( August 2021 , Geophysical Journal International)

   SUMMARY The vast majority of teleseismic XKS (including SKS, SKKS and PKS) shear wave splitting studies interpret the observed splitting parameters (fast orientation and splitting time) based on the assumption of a spatially invariant anisotropy structure in the vicinity of a recording station. For such anisotropy structures the observed splitting parameters are either independent of the arriving azimuth of the seismic ray paths if the medium traversed by the ray paths can be represented by a single layer of anisotropy with a horizontal axis of symmetry (i.e. simple anisotropy), or demonstrate a periodic variation with respect to the arriving azimuth for a more complicated structure of anisotropy (e.g. multiple layers with a horizontal axis of symmetry, or a single layer with a dipping axis). When a recording station is located near the boundary of two or more regions with different anisotropy characteristics, the observed splitting parameters are dependent on the location of the ray piercing points. Such a piercing-point dependence is clearly observed using a total of 360 pairs of XKS splitting parameters at three stations situated near the northeastern edge of the Sichuan Basin in central China. For a given station, the fast orientations differ as much as 90°,more »and the azimuthal variation of the fast orientations lacks a 90° or 180° periodicity which is expected for double-layered or dipping axis anisotropy. The observed splitting parameters from the three stations are spatially most consistent when they are projected at a depth of ∼250 km, and can be explained by shear strain associated with the absolute plate motion and mantle flow deflected by the cone-shaped lithospheric root of the Sichuan Basin.« less