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  Subjects -> GEOGRAPHY (Total: 493 journals)
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GeoInformatica
Journal Prestige (SJR): 0.479
Citation Impact (citeScore): 3
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1384-6175 - ISSN (Online) 1573-7624
Published by Springer-Verlag Homepage  [2469 journals]
  • ConvGCN-RF: A hybrid learning model for commuting flow prediction
           considering geographical semantics and neighborhood effects

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      Abstract: Abstract Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a ‘preprocessing-encoder-decoder’ hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness.
      PubDate: 2022-05-27
       
  • Online fleet monitoring with scalable event recognition and forecasting

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      Abstract: Abstract Moving object monitoring is becoming essential for companies and organizations that need to manage thousands or even millions of commercial vehicles or vessels, detect dangerous situations (e.g., collisions or malfunctions) and optimize their behavior. It is a task that must be executed in real-time, reporting any such situations or opportunities as soon as they appear. Given the growing sizes of fleets worldwide, a monitoring system must be highly efficient and scalable. It is becoming an increasingly common requirement that such monitoring systems should be able to automatically detect complex situations, possibly involving multiple moving objects and requiring extensive background knowledge. Building a monitoring system that is both expressive and scalable is a significant challenge. Typically, the more expressive a system is, the less flexible it becomes in terms of its parallelization potential. We present a system that strikes a balance between expressiveness and scalability. Going beyond event detection, we also present an approach towards event forecasting. We show how event patterns may be given a probabilistic description so that our system can forecast when a complex event is expected to occur. Our proposed system employs a formalism that allows analysts to define complex patterns in a user-friendly manner while maintaining unambiguous semantics and avoiding ad hoc constructs. At the same time, depending on the problem at hand, it can employ different parallelization strategies in order to address the issue of scalability. It can also employ different training strategies in order to fine-tune the probabilistic models constructed for event forecasting. Our experimental results show that our system can detect complex patterns over moving entities with minimal latency, even when the load on our system surpasses what is to be realistically expected in real-world scenarios.
      PubDate: 2022-05-11
       
  • MTMGNN: Multi-time multi-graph neural network for metro passenger flow
           prediction

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      Abstract: Abstract The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods.
      PubDate: 2022-04-25
       
  • Multi-type clustering using regularized tensor decomposition

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      Abstract: Abstract Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because of the need for spatial and temporal regularization and latent structures within data. Tensor decomposition is a promising approach because it can accommodate multidimensional structure of data (e.g., trajectory information about users, locations, and time periods). To address these challenges, we introduce Multi-Type Clustering using Regularized tensor Decomposition (MCRD), an innovative method for data analysis that provides insight not just about groupings within data types (e.g., clusters of users), but also about the interactions between data types (e.g., clusters of users and locations) in the latent features of complex multi-type datasets. This is done by combining two innovations. First, a tensor representing spatiotemporal data is decomposed using a novel regularization method to account for structure within the data. Next, within- and cross-type groups are found through the application of novel hypergraph community detection methods to the decomposed results. Experimentation on both synthetic and real trajectory data demonstrates MCRD’s capacity to reveal the within- and cross-type grouping in data, and MCRD outperforms related methods including tensor decomposition without regularization, unfolding of tensors, Laplacian regularization, and tensor block models. The robust and versatile analysis provided by combining new regularization and clustering techniques outlined in this paper likely have utility in geospatial analytics beyond the movement applications explicitly studied.
      PubDate: 2022-04-12
      DOI: 10.1007/s10707-021-00457-8
       
  • AP-GAN: Adversarial patch attack on content-based image retrieval systems

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      Abstract: Abstract Key Smart City applications such as traffic management and public security rely heavily on the intelligent processing of video and image data, often in the form of visual retrieval tasks, such as person Re-IDentification (ReID) and vehicle re-identification. For these tasks, Deep Neural Networks (DNNs) have been the dominant solution for the past decade, for their remarkable ability in learning discriminative features from images to boost retrieval performance. However, it is been discovered that DNNs are broadly vulnerable to maliciously constructed adversarial examples. By adding small perturbations to a query image, the returned retrieval results will be completely dissimilar from the query image. This poses serious challenges to vital systems in Smart City applications that depend on the DNN-based visual retrieval technology, as in the physical world, simple camouflage can be added on the subject (a few patches on the body or car), and turn the subject completely untrackable by person or vehicle Re-ID systems. To demonstrate the potential of such threats, this paper proposes a novel adversarial patch generative adversarial network (AP-GAN) to generate adversarial patches instead of modifying the entire image, which also causes the DNNs-based image retrieval models to return incorrect results. AP-GAN is trained in an unsupervised way that requires only a small amount of unlabeled data for training. Once trained, it produces query-specific perturbations for query images to form adversarial queries. Extensive experiments show that the AP-GAN achieves excellent attacking performance with various application scenarios that are based on deep features, including image retrieval, person ReID and vehicle ReID. The results of this study provide a warning that when deploying a DNNs-based image retrieval system, its security and robustness needs to be thoroughly considered.
      PubDate: 2022-04-01
       
  • Dynamic top-k influence maximization in social networks

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      Abstract: Abstract The problem of top-k influence maximization is to find the set of k users in a social network that can maximize the spread of influence under certain influence propagation model. This paper studies the influence maximization problem together with network dynamics. For example, given a real-life social network that evolves over time, we want to find k most influential users on everyday basis. This dynamic influence maximization problem has wide applications in practice. However, to our best knowledge, there is little prior work that studies this problem. Applying existing influence maximization algorithms at every time step provides a straightforward solution to the dynamic top-k influence maximization problem. Such a solution is, however, inefficient as it completely ignores the smoothness of network change. By analyzing two real social networks, Brightkite and Gowalla, we observe that the top-k influential set, as well as its influence value, does not change dramatically over time. Hence, it is possible to find the new top-k influential set by updating the previous one. We propose an efficient incremental update framework that takes advantage of such smoothness of network change. The proposed method achieves the same approximation ratio of 1 − e− 1 as its state-of-the-art static counterparts. Our experiments show that the proposed method outperforms the straightforward solution by a wide margin.
      PubDate: 2022-04-01
       
  • From reanalysis to satellite observations: gap-filling with imbalanced
           learning

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      Abstract: Abstract Increasing the spatial coverage and temporal resolution of Earth surface monitoring can significantly improve forecasting or monitoring capabilities in the context of smart city, such as extreme weather forecasting, ecosystem monitoring and anthropogenic impact monitoring. As an essential data source for Earth’s surface monitoring, most satellite observations exist data gaps due to various factors like the limitations of measuring equipment, the interferences of environments, and the delay or loss of data updates. Although many efforts have been conducted to fill the gaps in the last decade, the existing techniques cannot efficiently address the problem. In this paper, we extensively study the gap-filling problem of satellite observations using imbalanced learning. Specifically, we propose a framework called Reanalysis to Satellite (R2S) to simulate satellite observations with reanalysis data. In the R2S framework, we propose a generic method called Spatial Temporal Match (STM), matching reanalysis data and satellite observations to construct the Reanalysis-Satellite (R-S) dataset used to train the model. Based on the R-S dataset, we propose a novel method called Semi-imbalanced (SIMBA) to handle the imbalance problem of gap-filling by taking advantages of traditional machine learning and imbalanced learning. We construct a hybrid model in the R2S framework for the Soil Moisture Active Passive (SMAP) satellite observations of the tropical cyclone wind speed. Extensive experiments demonstrate the hybrid model outperforms the traditional machine learning model and closely approximates in situ observations.
      PubDate: 2022-04-01
       
  • Graph neural network based model for multi-behavior session-based
           recommendation

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      Abstract: Abstract Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods.
      PubDate: 2022-04-01
       
  • MTLM: a multi-task learning model for travel time estimation

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      Abstract: Abstract Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods.
      PubDate: 2022-04-01
       
  • Parallel discriminative subspace for city target detection from high
           dimension images

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      Abstract: Abstract City Target Detection is an enduring problem that intrigues the researchers all over the world. The great success of existing Target Detection algorithm appears in ubiquitous scenarios: Pedestrian Detection, Vehicle Tracking, etc. However, as for the city target detection in the remote sensing, we are facing with two inevitable problems: Complex Environment and Massive Information. The complicated environment encumbers the accurate extraction of the target profile, and the huge amount of information turns it into a heavy workload to get the final outcome for the conventional CPU- compiler architecture. In this paper, we propose a binary hypothesis framework based on adaptive dictionary and discriminative subspace for hyperspectral city target detection (BHADDS). Furthermore, we have also implemented it on other hardware platform alongside with CPU, such as FPGA. FPGA is a low-power portable and programmble SoC, and also the protocol model for potential massive production of the SoC chipset. Our eventual aim is heading for the high-performance processor with strong instant processing ability for remote sensing. In the final part of the paper, we have given a comprehensive performance comparison over the different platforms and summarized their applicable scenarios.
      PubDate: 2022-04-01
       
  • From multiple aspect trajectories to predictive analysis: a case study on
           fishing vessels in the Northern Adriatic sea

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      Abstract: Abstract In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.
      PubDate: 2022-03-31
      DOI: 10.1007/s10707-022-00463-4
       
  • City indicators for geographical transfer learning: an application to
           crash prediction

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      Abstract: Abstract The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.
      PubDate: 2022-03-22
      DOI: 10.1007/s10707-022-00464-3
       
  • Correction to: Spatial regression graph convolutional neural networks: A
           deep learning paradigm for spatial multivariate distributions

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      PubDate: 2022-03-03
      DOI: 10.1007/s10707-022-00461-6
       
  • An analysis of twitter as a relevant human mobility proxy

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      Abstract: Abstract During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.
      PubDate: 2022-02-15
      DOI: 10.1007/s10707-021-00460-z
       
  • Online meta-learning for POI recommendation

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      Abstract: Abstract Studying the POI recommendation in an online setting becomes meaningful because large volumes of user-POI interactions are generated in a chronological order. Although a few online update strategies have been developed, they cannot be applied in POI recommendation directly because they can hardly capture the long-term user preference only by updating the model with the current data. Besides, some latent POI information is ignored because existing update strategies are designed for traditional recommder systems without considering the addtional factors in POIs. In this paper, we propose an Online Meta-learning POI Recommendation (OMPR) method to solve the problem. To consider the geographical influences among POIs, we use a location-based self-attentive encoder to learn the complex user-POI relations. To capture the drift of user preference in online recommendation, we propose a meta-learning based transfer network to capture the knowledge transfer from both historical and current data. We conduct extensive experiments on two real-world datasets and the results show the superiority of our approaches in online POI recommendation.
      PubDate: 2022-01-13
      DOI: 10.1007/s10707-021-00459-6
       
  • On discovering motifs and frequent patterns in spatial trajectories with
           discrete Fr├ęchet distance

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      Abstract: Abstract The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of novel lower bound functions and a grouping-based solution. Our techniques apply directly when the analysis tasks are defined within the same or across multiple trajectories. An extensive empirical study on real trajectory datasets reveals that our approaches are 3 orders of magnitude faster than baseline solutions.
      PubDate: 2022-01-01
      DOI: 10.1007/s10707-021-00438-x
       
  • Efficient computation of map algebra over raster data stored in the k2-acc
           compact data structure

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      Abstract: Abstract We present efficient algorithms to compute simple and complex map algebra operations over raster data stored in main memory, using the k2-acc compact data structure. Raster data correspond to numerical data that represent attributes of spatial objects, such as temperature or elevation measures. Compact data structures allow efficient data storage in main memory and query them in their compressed form. A k2-acc is a set of k2-trees, one for every distinct numeric value in the raster matrix. We demonstrate that map algebra operations can be computed efficiently using this compact data structure. In fact, some map algebra operations perform over five orders of magnitude faster compared with algorithms working over uncompressed datasets.
      PubDate: 2022-01-01
      DOI: 10.1007/s10707-021-00445-y
       
  • Temporal aggregation bias and Gerrymandering urban time series

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      Abstract: Abstract The Modifiable Aerial Unit Problem (MAUP) influences the interpretation of spatial data in that forms of spatial aggregation creates scale and segmentation ecological fallacies. This paper explores the extent to which similar scalar and segmentation issues affect the analysis of temporal data. The analogy of gerrymandering in spatial data, which is the purposeful segmentation of space such that the underlying aggregations prove a specific point, is used to demonstrate segmentation and aggregation effects on time series data. To do so, the paper evaluates real-time sound monitoring data for Dublin, Ireland at multiple aggregation scales and segmentations to determine their effects with respect to compliance with European Union regulations concerning acceptable decibel levels. Like the MAUP, increasing scales of temporal aggregation remove extremes at more local scales, which has the effect of reducing measurements of non-compliance. Similarly, and unlike the spatial equivalent, because of circadian human social patterns, segmentation of temporal measurements also has a predictable, and gerrymander-able, effect on the measurement of compliance with ambient sound limits. The effect is computed as the Temporal Aggregation Bias and strategies which could justify gerrymandering of sound monitoring data are presented.
      PubDate: 2021-11-13
      DOI: 10.1007/s10707-021-00452-z
       
  • Reverse keyword-based location search on road networks

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      Abstract: Abstract Reverse top-k keyword-based location query (RTkKL), aims to find the maximum spatial region such that the query object is contained in the result of any top-k spatial keyword query with users’ queried keywords and any location in the region as arguments. Existing efforts on RTkKL find the objects in the Euclidean space. In this paper, we study the problem of reverse top-k keyword-based location query on road networks. We propose two methods. One is based on mark vertex, and the other is based on bisector. For the mark vertex based method, we identify the mark vertex according to the definition of RTkKL on road networks. Based on the mark vertex, we will get the mark segments in the result. For the bisector-based method, we find the border points for the query q and some objects. With Dijkstra algorithm, we start from the query point q. For each closed edge, whose two adjacent vertices have been extracted from the min heap, we would search the border points on the edge, and count the border points for the adjacent vertex. For each method, we propose effective pruning strategy to reduce the search range and computation cost. Finally, experiments demonstrate the efficiency of the proposed algorithm.
      PubDate: 2021-11-12
      DOI: 10.1007/s10707-021-00440-3
       
  • A survey of location-based social networks: problems, methods, and future
           research directions

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      Abstract: Abstract The development of mobile devices and positioning technology has facilitated the rapid growth of location-based social networks (LBSNs). Users in these networks can share geo-related information in real-time, including locations, trajectories, geo-tagged pictures, and tweets. LBSNs record massive amounts of spatiotemporal data and offer a great opportunity to analyze human and location-specific spatiotemporal characteristics. It plays an important role in various applications, such as marketing, recommendations, and urban planning. In this study, we collect relevant literature about LBSNs research in the past 10 years and use a topic model, latent Dirichlet allocation (LDA), to uncover the highly heterogeneous area of research related to LBSNs. Then, we conduct a systematic review of those works. In doing so, we organize identified literature into eight fine-grained directions. For each direction, we sum up the major research focus and contributions. We also systematize future research into four main themes concerning data simulation and fusion, privacy-aware methods, new applications and services, and technological innovations.
      PubDate: 2021-09-24
      DOI: 10.1007/s10707-021-00450-1
       
 
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