A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 | Last   [Sort alphabetically]   [Restore default list]

  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
Geophysical Research Letters     Full-text available via subscription   (Followers: 178)
Journal of Geophysical Research : Space Physics     Full-text available via subscription   (Followers: 157)
Journal of Geophysical Research : Atmospheres     Partially Free   (Followers: 144)
Journal of Geophysical Research : Planets     Full-text available via subscription   (Followers: 136)
Remote Sensing of Environment     Hybrid Journal   (Followers: 96)
Antipode     Hybrid Journal   (Followers: 65)
Journal of Geophysical Research : Earth Surface     Partially Free   (Followers: 60)
Journal of Geophysical Research : Oceans     Partially Free   (Followers: 60)
Progress in Human Geography     Hybrid Journal   (Followers: 59)
Journal of Geophysical Research : Solid Earth     Full-text available via subscription   (Followers: 58)
International Journal of Geographical Information Science     Hybrid Journal   (Followers: 55)
GIScience & Remote Sensing     Open Access   (Followers: 54)
Journal of Water and Climate Change     Open Access   (Followers: 53)
Climate Change Economics     Hybrid Journal   (Followers: 50)
Reviews of Geophysics     Full-text available via subscription   (Followers: 49)
Remote Sensing Letters     Hybrid Journal   (Followers: 46)
Annals of the American Association of Geographers     Hybrid Journal   (Followers: 43)
Economic Geography     Hybrid Journal   (Followers: 40)
Applied Geography     Hybrid Journal   (Followers: 38)
Geochemistry, Geophysics, Geosystems     Full-text available via subscription   (Followers: 35)
Urban Geography     Hybrid Journal   (Followers: 34)
Journal of Geophysical Research : Biogeosciences     Full-text available via subscription   (Followers: 34)
Climate and Development     Hybrid Journal   (Followers: 33)
Journal of Coastal Research     Hybrid Journal   (Followers: 31)
Annals of GIS     Open Access   (Followers: 31)
Cartography and Geographic Information Science     Hybrid Journal   (Followers: 31)
GPS Solutions     Hybrid Journal   (Followers: 28)
Transactions of the Institute of British Geographers     Hybrid Journal   (Followers: 27)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of the Middle East and Africa     Hybrid Journal   (Followers: 22)
China : An International Journal     Full-text available via subscription   (Followers: 20)
Urban Research & Practice     Hybrid Journal   (Followers: 20)
Dialogues in Human Geography     Hybrid Journal   (Followers: 20)
Imago Mundi: The International Journal for the History of Cartography     Hybrid Journal   (Followers: 20)
Atmospheric Measurement Techniques (AMT)     Open Access   (Followers: 19)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Water International     Hybrid Journal   (Followers: 19)
Journal of the American Planning Association     Hybrid Journal   (Followers: 19)
Journal of Cultural Geography     Hybrid Journal   (Followers: 18)
Geography Compass     Hybrid Journal   (Followers: 18)
Professional Geographer     Hybrid Journal   (Followers: 17)
Cartographica : The International Journal for Geographic Information and Geovisualization     Full-text available via subscription   (Followers: 17)
Africa Insight     Full-text available via subscription   (Followers: 16)
The Geographical Journal     Hybrid Journal   (Followers: 16)
International Geology Review     Hybrid Journal   (Followers: 16)
Crossings : Journal of Migration & Culture     Hybrid Journal   (Followers: 16)
Computational Geosciences     Hybrid Journal   (Followers: 16)
Tectonics     Full-text available via subscription   (Followers: 15)
American Journal of Geographic Information System     Open Access   (Followers: 14)
Geographical Review     Hybrid Journal   (Followers: 13)
Buildings & Landscapes: Journal of the Vernacular Architecture Forum     Full-text available via subscription   (Followers: 13)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 13)
International Indigenous Policy Journal     Open Access   (Followers: 13)
Annual Review of Marine Science     Full-text available via subscription   (Followers: 13)
Progress in Physical Geography     Hybrid Journal   (Followers: 13)
Bulletin of Geosciences     Open Access   (Followers: 12)
American Journal of Human Ecology     Open Access   (Followers: 11)
Geographical Research     Hybrid Journal   (Followers: 11)
Geographical Analysis     Hybrid Journal   (Followers: 11)
Canadian Journal of Soil Science     Full-text available via subscription   (Followers: 11)
Geosciences Journal     Hybrid Journal   (Followers: 10)
GeoJournal     Hybrid Journal   (Followers: 10)
Journal of Geography     Hybrid Journal   (Followers: 10)
European Spatial Research and Policy     Open Access   (Followers: 9)
Cartographic Journal     Hybrid Journal   (Followers: 9)
Atmospheric Measurement Techniques Discussions (AMTD)     Open Access   (Followers: 9)
Journal of Borderlands Studies     Hybrid Journal   (Followers: 8)
Geography and Natural Resources     Hybrid Journal   (Followers: 8)
Physical Geography     Hybrid Journal   (Followers: 8)
Natural Science     Open Access   (Followers: 8)
Journal of Iberian and Latin American Research     Hybrid Journal   (Followers: 8)
Middle East Development Journal     Hybrid Journal   (Followers: 8)
Geo-spatial Information Science     Open Access   (Followers: 7)
Nordic Journal of Migration Research     Open Access   (Followers: 7)
Social Geography Discussions (SGD)     Open Access   (Followers: 7)
International Journal of Applied Geospatial Research     Hybrid Journal   (Followers: 7)
California Italian Studies Journal     Full-text available via subscription   (Followers: 7)
Journal of Latin American Geography     Full-text available via subscription   (Followers: 7)
Urban History Review / Revue d'histoire urbaine     Full-text available via subscription   (Followers: 7)
GeoInformatica     Hybrid Journal   (Followers: 7)
International Journal of Health Geographics     Open Access   (Followers: 7)
Northern Scotland     Hybrid Journal   (Followers: 6)
Journal of Geographical Systems     Hybrid Journal   (Followers: 6)
Asia Policy     Full-text available via subscription   (Followers: 6)
Singapore Journal of Tropical Geography     Hybrid Journal   (Followers: 6)
Australian Geographer     Hybrid Journal   (Followers: 6)
Ocean Science Journal     Hybrid Journal   (Followers: 6)
Journal of Maps     Open Access   (Followers: 6)
The Canadian Geographer/le Geographe Canadien     Hybrid Journal   (Followers: 6)
Journal of Map & Geography Libraries     Hybrid Journal   (Followers: 5)
ISPRS International Journal of Geo-Information     Open Access   (Followers: 5)
Journal of Developmental Entrepreneurship     Hybrid Journal   (Followers: 5)
Current Research in Geoscience     Open Access   (Followers: 5)
Creativity Studies     Open Access   (Followers: 5)
Australian Antarctic Magazine     Free   (Followers: 5)
Focus on Geography     Partially Free   (Followers: 5)
Asian Geographer     Hybrid Journal   (Followers: 5)
Journal of Australian Studies     Hybrid Journal   (Followers: 5)
Bulletin of the Ecological Society of America     Open Access   (Followers: 4)
Geografiska Annaler, Series A : Physical Geography     Hybrid Journal   (Followers: 4)
Journal of Sedimentary Research     Hybrid Journal   (Followers: 4)
Genre & histoire     Open Access   (Followers: 4)
Globe, The     Full-text available via subscription   (Followers: 4)
Latinoamérica. Revista de estudios Latinoamericanos     Open Access   (Followers: 4)
Applied Geomatics     Hybrid Journal   (Followers: 4)
Transmodernity : Journal of Peripheral Cultural Production of the Luso-Hispanic World     Open Access   (Followers: 4)
Interaction     Full-text available via subscription   (Followers: 3)
Journal of Western Archives     Open Access   (Followers: 3)
Limnological Review     Open Access   (Followers: 3)
Lithosphere     Open Access   (Followers: 3)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 3)
Southeastern Europe     Hybrid Journal   (Followers: 3)
New Zealand Journal of Geography     Hybrid Journal   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Économie rurale     Open Access   (Followers: 3)
Polar Research     Open Access   (Followers: 3)
South Asian Diaspora     Hybrid Journal   (Followers: 3)
Journal of Burma Studies     Full-text available via subscription   (Followers: 3)
Social Dynamics: A journal of African studies     Hybrid Journal   (Followers: 3)
All Earth     Open Access   (Followers: 3)
Pastoralism : Research, Policy and Practice     Open Access   (Followers: 2)
Maine Policy Review     Open Access   (Followers: 2)
The South Asianist     Open Access   (Followers: 2)
Provincial China     Hybrid Journal   (Followers: 2)
Geodesy and Cartography     Open Access   (Followers: 2)
Polar Journal     Hybrid Journal   (Followers: 2)
Regional Science Policy & Practice     Hybrid Journal   (Followers: 2)
History of Geo- and Space Sciences     Open Access   (Followers: 2)
Mineralogia     Open Access   (Followers: 2)
Eastern European Countryside     Open Access   (Followers: 2)
Regions and Cohesion     Open Access   (Followers: 2)
Geosphere     Open Access   (Followers: 2)
Journal of Earthquake and Tsunami     Hybrid Journal   (Followers: 2)
Cahiers franco-canadiens de l'Ouest     Full-text available via subscription   (Followers: 2)
Norois     Open Access   (Followers: 2)
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 2)
Études rurales     Open Access   (Followers: 2)
Polar Geography     Hybrid Journal   (Followers: 2)
Scottish Geographical Journal     Hybrid Journal   (Followers: 2)
Norsk Geografisk Tidsskrift - Norwegian Journal of Geography     Hybrid Journal   (Followers: 2)
Southeastern Geographer     Full-text available via subscription   (Followers: 2)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
BioRisk     Open Access   (Followers: 2)
Geographical Education     Full-text available via subscription   (Followers: 2)
Cahiers Balkaniques     Open Access   (Followers: 2)
Reflets : revue d'intervention sociale et communautaire     Full-text available via subscription   (Followers: 2)
Études internationales     Full-text available via subscription   (Followers: 1)
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
Recherches sociographiques     Full-text available via subscription   (Followers: 1)
Estudios Geográficos     Open Access   (Followers: 1)
Terrae Incognitae     Hybrid Journal   (Followers: 1)
Geoforum Perspektiv     Open Access   (Followers: 1)
Newfoundland and Labrador Studies     Full-text available via subscription   (Followers: 1)
South African Geographical Journal     Hybrid Journal   (Followers: 1)
Geochronometria     Open Access   (Followers: 1)
Amerika     Open Access   (Followers: 1)
Journal de la Société des Océanistes     Open Access   (Followers: 1)
Les Cahiers d'Outre-Mer     Open Access   (Followers: 1)
Revue archéologique du Centre de la France     Open Access   (Followers: 1)
Journal of Terrestrial Observation     Open Access   (Followers: 1)
PRISM : A Journal of Regional Engagement     Open Access   (Followers: 1)
Physio-Géo     Open Access   (Followers: 1)
Méditerranée     Open Access   (Followers: 1)
Indiana     Open Access   (Followers: 1)
Revista de Geografía Norte Grande     Open Access   (Followers: 1)
L'Année du Maghreb     Open Access   (Followers: 1)
European Countryside     Open Access   (Followers: 1)
Norteamérica     Open Access   (Followers: 1)
International Journal of Bahamian Studies     Open Access   (Followers: 1)
Journal of the Southwest     Full-text available via subscription   (Followers: 1)
PSC Discussion Papers Series     Open Access  
Anales de Geografía de la Universidad Complutense     Open Access  
International Journal of River Basin Management     Hybrid Journal  
Revista Geográfica de América Central     Open Access  
Multiciencias     Open Access  
Investigaciones Geográficas (Esp)     Open Access  
Sociedade & Natureza     Open Access  
Región y Sociedad     Open Access  
Migración y Desarrollo     Open Access  
Migraciones Internacionales     Open Access  
Investigaciones Geográficas     Open Access  
Frontera Norte     Open Access  
Cuadernos de Desarrollo Rural     Open Access  
Territoire en Mouvement     Open Access  
Quaestiones Geographicae     Open Access  
Limes. Cultural Regionalistics     Open Access  
GEOMATICA     Hybrid Journal  
Preview     Hybrid Journal  
Cuadernos de Geografía : Revista Colombiana de Geografía     Open Access  
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Recherches amérindiennes au Québec     Full-text available via subscription  
Rabaska : revue d'ethnologie de l'Amérique française     Full-text available via subscription  
Port Acadie : revue interdisciplinaire en études acadiennes / Port Acadie: An Interdisciplinary Review in Acadian Studies     Full-text available via subscription  
Études/Inuit/Studies     Full-text available via subscription  
Aurora Journal     Full-text available via subscription  
Revista de la Asociacion Geologica Argentina     Open Access  
San Francisco Estuary and Watershed Science     Open Access  
Journal of Alpine Research : Revue de géographie alpine     Open Access  
Géocarrefour     Open Access  
Confins     Open Access  

        1 2 3 | Last   [Sort alphabetically]   [Restore default list]

Similar Journals
Journal Cover
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]
  • joinTree: A novel join-oriented multivariate operator for spatio-temporal
           data management in Flink

    • Free pre-print version: Loading...

      Abstract: Abstract In the era of intelligent Internet, the management and analysis of massive spatio-temporal data is one of the important links to realize intelligent applications and build smart cities, in which the interaction of multi-source data is the basis of realizing spatio-temporal data management and analysis. As an important carrier to achieve the interactive calculation of massive data, Flink provides the advanced Operator Join to facilitate user program development. In a Flink job with multi-source data connection operations, the selection of join sequences and the data communication in the repartition phase are both key factors that affect the efficiency of the job. However, Flink does not provide any optimization mechanism for the two factors, which in turn leads to low job efficiency. If the enumeration method is used to find the optimal join sequence, the result will not be obtained in polynomial time, so the optimization effect cannot be achieved. We investigate the above problems, design and implement a more advanced Operator joinTree that can support multi-source data connection in Flink, and introduce two optimization strategies into the Operator. In summary, the advantages of our work are highlighted as follows: (1) the Operator enables Flink to support multi-source data connection operation, and reduces the amount of calculation and data communication by introducing lightweight optimization strategies to improve job efficiency; (2) with the optimization strategy for join sequence, the total running time can be reduced by 29% and the data communication can be reduced by 34% compared with traditional sequential execution; (3) the optimization strategy for data repartition can further enable the job to bring 35% performance improvement, and in the average case can reduce the data communication by 43%.
      PubDate: 2022-08-04
       
  • Editor’s note

    • Free pre-print version: Loading...

      PubDate: 2022-07-18
       
  • Personalized route recommendation through historical travel behavior
           analysis

    • Free pre-print version: Loading...

      Abstract: Abstract Popular navigation applications and services optimize routes based on either distance or time, disregarding drivers’ preferences when suggesting routes. Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, it is believed that users’ travel behaviors are implicitly reflected and can be learned from their historical Global Positioning System (GPS) trajectories. The Behavior-based Route Recommendation (BR2) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are defined to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Furthermore, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. A behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. An extension is proposed, named BR2+, to better consider the temporal dependency and incorporate distance in the recommendation process. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method. In addition, a web-based geographic information system (GIS) called MPR is implemented to demonstrate differences in route recommendation when time, distance, or users’ preferences are considered, besides providing insight about users’ movement through data visualization of their spatial and temporal coverage.
      PubDate: 2022-07-01
       
  • Understanding evolution of maritime networks from automatic identification
           system data

    • Free pre-print version: Loading...

      Abstract: Abstract Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a conceptual framework for the fine-grained systematic study of maritime graphs evolution. To this end, this paper presents the month-by-month analysis of world-wide graphs built using a 3-years AIS dataset. The analysis focuses on the evolution of several topological graph features, as well as their stationarity and statistical correlation. Results have revealed some interesting seasonal and trending patterns that can provide insights in the world-wide maritime context and be used as building blocks toward the prediction of graphs topology.
      PubDate: 2022-07-01
       
  • Individual and collective stop-based adaptive trajectory segmentation

    • Free pre-print version: Loading...

      Abstract: Abstract Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.
      PubDate: 2022-07-01
       
  • Creating contiguous service areas around points of dispensing for resource
           distribution during bio-emergencies

    • Free pre-print version: Loading...

      Abstract: Abstract Response plans in preparation for public health emergencies often involve the setup of facilities like shelters, ad-hoc clinics, etc. to serve the affected population. While public health authorities frequently have prospective facility locations, balancing the demand or population at these facilities can be challenging. Assigning populations to their closest facilities may lead to uneven distribution of demand. This research proposes a novel greedy heuristic algorithm to create service areas around given facilities such that the population to be served by each facility is uniform or proportional to available resources. This algorithm has been implemented in the context of response plans for bio-emergencies in Denton County, Texas, USA. Given the location of Points of Dispensing (PODs), the objective is to create contiguous catchment areas, each served by one POD such that demand distribution constraints are satisfied. While the demand distribution constraints are hard constraints, it is also preferred that populations are mapped to PODs as close to them as possible. A response plan defines a mapping of populations to facilities and presents a combinatorial optimization problem in which the average distance between population locations and PODs is the cost function value, and demand equity and contiguity of catchment areas are hard constraints. We present a decision support system for planners to select solutions based on the compactness of catchment areas, the average distance between populations and PODs, and execution time, given that all solutions have contiguous catchment areas and balanced demand.
      PubDate: 2022-07-01
       
  • ConvGCN-RF: A hybrid learning model for commuting flow prediction
           considering geographical semantics and neighborhood effects

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-022-00467-0
       
  • Online fleet monitoring with scalable event recognition and forecasting

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-022-00465-2
       
  • MTMGNN: Multi-time multi-graph neural network for metro passenger flow
           prediction

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-022-00466-1
       
  • Multi-type clustering using regularized tensor decomposition

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-020-00418-7
       
  • Dynamic top-k influence maximization in social networks

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-020-00419-6
       
  • From reanalysis to satellite observations: gap-filling with imbalanced
           learning

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-020-00426-7
       
  • Graph neural network based model for multi-behavior session-based
           recommendation

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-021-00439-w
       
  • MTLM: a multi-task learning model for travel time estimation

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-020-00422-x
       
  • Parallel discriminative subspace for city target detection from high
           dimension images

    • Free pre-print version: Loading...

      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
      DOI: 10.1007/s10707-020-00399-7
       
  • From multiple aspect trajectories to predictive analysis: a case study on
           fishing vessels in the Northern Adriatic sea

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

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

    • Free pre-print version: Loading...

      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
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.239.4.127
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-