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 by number of followers]   [Restore default list]

  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
40 [degrees] South     Full-text available via subscription   (Followers: 1)
AAG Review of Books     Hybrid Journal   (Followers: 2)
AbeÁfrica : Revista da Associação Brasileira de Estudos Africanos     Open Access  
ACME : An International Journal for Critical Geographies     Open Access   (Followers: 1)
Acta Universitatis Lodziensis : Folia Geographica Socio-Oeconomica     Open Access   (Followers: 1)
Adam Academy : Journal of Social Sciences / Adam Akademi : Sosyal Bilimler Dergisi     Open Access   (Followers: 3)
Advances in Cartography and GIScience of the ICA     Open Access   (Followers: 3)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 20)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 11)
Africa Insight     Full-text available via subscription   (Followers: 16)
Africa Spectrum     Open Access   (Followers: 16)
African Geographical Review     Hybrid Journal   (Followers: 2)
Afrika Focus     Open Access   (Followers: 1)
AGORA Magazine     Open Access   (Followers: 2)
Agronomía & Ambiente     Open Access   (Followers: 1)
AGU Advances     Open Access   (Followers: 1)
All Earth     Open Access   (Followers: 3)
American Journal of Geographic Information System     Open Access   (Followers: 14)
American Journal of Human Ecology     Open Access   (Followers: 11)
American Journal of Rural Development     Open Access   (Followers: 6)
Amerika     Open Access   (Followers: 1)
Anales de Geografía de la Universidad Complutense     Open Access  
Anatoli     Open Access  
Annales Universitatis Paedagogicae Cracoviensis / Studia de Cultura     Open Access  
Annals of GIS     Open Access   (Followers: 31)
Annals of the American Association of Geographers     Hybrid Journal   (Followers: 43)
Annual Review of Marine Science     Full-text available via subscription   (Followers: 13)
Antipode     Hybrid Journal   (Followers: 65)
Anuario     Open Access  
Applied Geography     Hybrid Journal   (Followers: 40)
Applied Geomatics     Hybrid Journal   (Followers: 4)
Ar@cne     Open Access  
Arctic     Open Access   (Followers: 6)
Arctic Science     Open Access   (Followers: 7)
Area Development and Policy     Hybrid Journal   (Followers: 1)
Asia Policy     Full-text available via subscription   (Followers: 6)
Asian Geographer     Hybrid Journal   (Followers: 5)
Asian Journal of Geographical Research     Open Access   (Followers: 2)
Ateneo Korean Studies Conference Proceedings     Open Access  
Atmospheric Measurement Techniques (AMT)     Open Access   (Followers: 19)
Atmospheric Measurement Techniques Discussions (AMTD)     Open Access   (Followers: 10)
Aurora Journal     Full-text available via subscription  
Australian Antarctic Magazine     Free   (Followers: 5)
Australian Geographer     Hybrid Journal   (Followers: 7)
Bandung : Journal of the Global South     Open Access   (Followers: 1)
Barn : Forskning om barn og barndom i Norden     Open Access  
Baru : Revista Brasileira de Assuntos Regionais e Urbanos     Open Access  
Belgeo     Open Access   (Followers: 2)
Biblio3W : Revista Bibliográfica de Geografía y Ciencias Sociales     Open Access  
Biogeographia : The Journal of Integrative Biogeography     Open Access   (Followers: 2)
BioRisk     Open Access   (Followers: 2)
Boletim Campineiro de Geografia     Open Access  
Boletim de Ciências Geodésicas     Open Access  
Boletim Gaúcho de Geografia     Open Access  
Boletim Goiano de Geografia     Open Access  
Boletín de Estudios Geográficos     Open Access  
Boletín de la Asociación de Geógrafos Españoles     Open Access  
Brill Research Perspectives in Map History     Full-text available via subscription   (Followers: 1)
Buildings & Landscapes: Journal of the Vernacular Architecture Forum     Full-text available via subscription   (Followers: 13)
Bulletin de la Société Géographique de Liège     Open Access  
Bulletin de l’association de géographes français     Open Access   (Followers: 1)
Bulletin of Geography. Physical Geography Series     Open Access   (Followers: 4)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 3)
Bulletin of Geosciences     Open Access   (Followers: 12)
Bulletin of the Ecological Society of America     Open Access   (Followers: 4)
Bulletin of the Serbian Geographical Society     Open Access  
Caderno de Geografia     Open Access  
Cahiers Balkaniques     Open Access   (Followers: 2)
Cahiers Charlevoix : Études franco-ontariennes     Full-text available via subscription  
Cahiers franco-canadiens de l'Ouest     Full-text available via subscription   (Followers: 2)
California Italian Studies Journal     Full-text available via subscription   (Followers: 7)
Canadian Journal of Latin American and Caribbean Studies     Hybrid Journal   (Followers: 10)
Canadian Journal of Soil Science     Full-text available via subscription   (Followers: 11)
Cardinalis     Open Access  
Carnets de géographes     Open Access  
Cartographic Journal     Hybrid Journal   (Followers: 9)
Cartographic Perspectives     Open Access   (Followers: 2)
Cartographica : The International Journal for Geographic Information and Geovisualization     Full-text available via subscription   (Followers: 17)
Cartography and Geographic Information Science     Hybrid Journal   (Followers: 32)
Check List : The Journal of Biodiversity Data     Open Access   (Followers: 2)
China : An International Journal     Full-text available via subscription   (Followers: 20)
Climate and Development     Hybrid Journal   (Followers: 35)
Climate Change Economics     Hybrid Journal   (Followers: 52)
Comparative Cultural Studies : European and Latin American Perspectives     Open Access   (Followers: 5)
Computational Geosciences     Hybrid Journal   (Followers: 16)
Computational Urban Science     Open Access   (Followers: 1)
Confins     Open Access  
Conjuntura Austral : Journal of the Global South     Open Access   (Followers: 2)
Coolabah     Open Access  
Creativity Studies     Open Access   (Followers: 5)
Critical Romani Studies     Open Access  
Crossings : Journal of Migration & Culture     Hybrid Journal   (Followers: 16)
Cuadernos de Desarrollo Rural     Open Access  
Cuadernos de Geografía : Revista Colombiana de Geografía     Open Access  
Cuadernos de Geografía de la Universitat de València     Open Access  
Cuadernos de Investigación Geográfica / Geographical Research Letters     Open Access  
Cuadernos Inter.c.a.mbio sobre Centroamérica y el Caribe     Open Access   (Followers: 1)
Current Research in Geoscience     Open Access   (Followers: 5)
Dela     Open Access  
Dialogues in Human Geography     Hybrid Journal   (Followers: 20)
Didáctica Geográfica     Open Access  
DIE ERDE : Journal of the Geographical Society of Berlin     Open Access   (Followers: 1)
Documenti Geografici     Open Access  
Documents d'Anàlisi Geogràfica     Open Access  
Doğu Coğrafya Dergisi : Eastern Geographical Review     Open Access  
DRd - Desenvolvimento Regional em debate     Open Access  
Earth System Governance     Open Access   (Followers: 1)
Earth Systems and Environment     Hybrid Journal   (Followers: 3)
East/West : Journal of Ukrainian Studies     Open Access  
Eastern European Countryside     Open Access   (Followers: 2)
Economic and Regional Studies / Studia Ekonomiczne i Regionalne     Open Access  
Economic Geography     Hybrid Journal   (Followers: 42)
Économie rurale     Open Access   (Followers: 3)
Ecosystems and People     Open Access   (Followers: 4)
Entorno Geográfico     Open Access  
Environment & Ecosystem Science     Open Access   (Followers: 3)
Environmental and Sustainability Indicators     Open Access   (Followers: 7)
Environmental Science : Atmospheres     Open Access  
Environmental Science and Sustainable Development : International Journal Of Environmental Science & Sustainable Development     Open Access   (Followers: 13)
Environmental Smoke     Open Access  
Ería : Revista Cuatrimestral de Geografía     Open Access  
Espacio y Desarrollo     Open Access  
Espacios : Revista de |Geografía     Open Access  
Espaço & Economia : Revista Brasileira de Geografia Econômica     Open Access  
Espaço Aberto     Open Access  
Espaço e Cultura     Open Access  
Espaço e Tempo Midiáticos     Open Access  
Estudios Geográficos     Open Access   (Followers: 1)
Estudios Socioterritoriales : Revista de Geografía     Open Access  
Ethnobiology Letters     Open Access  
Ethnoscientia : Brazilian Journal of Ethnobiology and Ethnoecology     Open Access  
eTropic : electronic journal of studies in the tropics     Open Access  
Études internationales     Full-text available via subscription   (Followers: 1)
Études rurales     Open Access   (Followers: 2)
Études/Inuit/Studies     Full-text available via subscription  
European Bulletin of Himalayan Research     Open Access   (Followers: 10)
European Countryside     Open Access   (Followers: 1)
European Spatial Research and Policy     Open Access   (Followers: 9)
Evolutionary Human Sciences     Open Access   (Followers: 5)
Fennia : International Journal of Geography     Open Access   (Followers: 1)
Finisterra : Revista Portuguesa de Geografia     Open Access  
Fire Ecology     Open Access   (Followers: 3)
Florida Geographer     Open Access   (Followers: 1)
Focus on Geography     Partially Free   (Followers: 5)
Forum Geografi     Open Access  
Frontera Norte     Open Access  
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
Genre & histoire     Open Access   (Followers: 4)
Geo : Geography and Environment     Open Access   (Followers: 9)
Geo UERJ     Open Access  
Geo-Image     Open Access   (Followers: 1)
Geo-spatial Information Science     Open Access   (Followers: 8)
GeoArabia     Hybrid Journal  
Géocarrefour     Open Access  
Geochemistry, Geophysics, Geosystems     Full-text available via subscription   (Followers: 34)
Geochronometria     Open Access   (Followers: 1)
Geoderma Regional : The International Journal for Regional Soil Research     Full-text available via subscription   (Followers: 4)
Geodesy and Cartography     Open Access   (Followers: 2)
Geoforum Perspektiv     Open Access   (Followers: 1)
Geofronter     Open Access  
Geografares     Open Access  
Geografisk Tidsskrift-Danish Journal of Geography     Hybrid Journal   (Followers: 3)
Geografiska Annaler, Series A : Physical Geography     Hybrid Journal   (Followers: 4)
Geographia     Open Access   (Followers: 3)
Geographica Helvetica     Open Access   (Followers: 13)
Geographical Analysis     Hybrid Journal   (Followers: 11)
Geographical Education     Full-text available via subscription   (Followers: 2)
Geographical Journal of Nepal     Open Access  
Geographical Research     Hybrid Journal   (Followers: 11)
Geographical Review     Hybrid Journal   (Followers: 13)
Geographicalia     Open Access  
Géographie et cultures     Open Access   (Followers: 3)
Geography and Natural Resources     Hybrid Journal   (Followers: 10)
Geography and Sustainability     Open Access   (Followers: 3)
Geography Compass     Hybrid Journal   (Followers: 18)
GeoHumanities     Hybrid Journal   (Followers: 2)
GeoInformatica     Hybrid Journal   (Followers: 7)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 13)
Geoinformatics FCE CTU     Open Access   (Followers: 7)
Geoingá : Revista do Programa de Pós-Graduação em Geografia     Open Access  
GeoJournal     Hybrid Journal   (Followers: 11)
GEOMATICA     Hybrid Journal   (Followers: 1)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 14)
GEOmedia     Open Access   (Followers: 1)
Geopauta : Revista de Geografia da Universidade Estadual do Sudoeste da Bahia     Open Access  
Geophysical Research Letters     Full-text available via subscription   (Followers: 199)
Geoplanning : Journal of Geomatics and Planning     Open Access   (Followers: 5)
GeoScape     Open Access  
Geosciences Journal     Hybrid Journal   (Followers: 11)
Geosphere     Open Access   (Followers: 2)
GEOUSP : Espaço e Tempo     Open Access  
Ghana Journal of Geography     Open Access   (Followers: 11)
Ghana Studies     Full-text available via subscription   (Followers: 15)
GIScience & Remote Sensing     Open Access   (Followers: 55)
Global Challenges     Open Access   (Followers: 2)
Global Sustainability     Open Access   (Followers: 5)
Globe, The     Full-text available via subscription   (Followers: 4)
GPS Solutions     Hybrid Journal   (Followers: 28)
Grafo Working Papers     Open Access  
HiN : Alexander von Humboldt im Netz. Internationale Zeitschrift für Humboldt-Studien     Open Access  

        1 2 3 | Last   [Sort by number of followers]   [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  [2467 journals]
  • Big mobility data analytics: recent advances and open problems

    • Free pre-print version: Loading...

      PubDate: 2022-11-18
       
  • A spatially-aware algorithm for location extraction from structured
           documents

    • Free pre-print version: Loading...

      Abstract: Abstract Place names facilitate locating and distinguishing geographic space where human activities and natural phenomena occur. Extracting place names at multiple spatial resolutions from text is beneficial in several tasks such as identifying the location of events, enriching gazetteers, discovering connections between events and places, etc. Most modern place name extraction approaches generalize the linguistic rules and lexical features as a universal rule and ignore patterns inherent in place names in the geographic contexts. As a result, they lack spatial awareness to effectively identify place names from different geographic contexts, especially the lesser-known place names. In this research, we develop a novel Spatially-Aware Location Extraction (SALE) algorithm for place name extraction from structured documents that uses a hybrid approach comprising of knowledge-driven and data-driven methods. We build a custom named entity recognition (NER) system based on the conditional random field (CRF) and train/ fine-tune it using spatial features extracted from a dataset based on a given geographic region. SALE uses multiple pathways, including the use of the spatially tuned NER to enhance the efficacy in our place names extraction. The experimental results using a large geographic region show that our algorithm outperforms well-known state-of-the-art place name recognizers.
      PubDate: 2022-11-04
       
  • Towards general-purpose representation learning of polygonal geometries

    • Free pre-print version: Loading...

      Abstract: Abstract Neural network representation learning for spatial data (e.g., points, polylines, polygons, and networks) is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made in representation learning for points, polylines, and networks, whereas little progress has been made for polygons, especially complex polygonal geometries. In this work, we focus on developing a general-purpose polygon encoding model, which can encode a polygonal geometry (with or without holes, single or multipolygons) into an embedding space. The result embeddings can be leveraged directly (or finetuned) for downstream tasks such as shape classification, spatial relation prediction, building pattern classification, cartographic building generalization, and so on. To achieve model generalizability guarantees, we identify a few desirable properties that the encoder should satisfy: loop origin invariance, trivial vertex invariance, part permutation invariance, and topology awareness. We explore two different designs for the encoder: one derives all representations in the spatial domain and can naturally capture local structures of polygons; the other leverages spectral domain representations and can easily capture global structures of polygons. For the spatial domain approach we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons. For the spectral domain approach we develop NUFTspec based on Non-Uniform Fourier Transformation (NUFT), which naturally satisfies all the desired properties. We conduct experiments on two different tasks: 1) polygon shape classification based on the commonly used MNIST dataset; 2) polygon-based spatial relation prediction based on two new datasets (DBSR-46K and DBSR-cplx46K) constructed from OpenStreetMap and DBpedia. Our results show that NUFTspec and ResNet1D outperform multiple existing baselines with significant margins. While ResNet1D suffers from model performance degradation after shape-invariance geometry modifications, NUFTspec is very robust to these modifications due to the nature of the NUFT representation. NUFTspec is able to jointly consider all parts of a multipolygon and their spatial relations during prediction while ResNet1D can recognize the shape details which are sometimes important for classification. This result points to a promising research direction of combining spatial and spectral representations.
      PubDate: 2022-10-22
       
  • Semi-supervised geological disasters named entity recognition using few
           labeled data

    • Free pre-print version: Loading...

      Abstract: Abstract The geological disasters Named Entity Recognition (NER) method aims to recognize entities reflecting disaster event information in unstructured texts to construct a geohazard knowledge graph that can provide a reference for disaster emergency response. Without training on large-scale labeled data, current NER methods based on deep learning models cannot identify specific geological disaster entities from geological disaster situation reports. However, manually labeling geohazard situation reports is tedious and time-consuming. As a result, we present Semi-GDNER, a semi-supervised geological disasters NER approach that can effectively extract six kinds of geological disaster entities when a few manually labeled and unlabeled in-domain data are available. It is divided into two stages: (1) transferring the parameters of the pre-trained BERT-base model to the BERT layer of the backbone model BERT-BiLSTM-CRF and training the backbone model with a few labeled data; (2) continuing training the backbone model by expanding the training set with unlabeled data using a self-training (ST) strategy. To reduce noise in the second stage, we select the pseudo-labeled samples with high confidence to join the training set in each ST iteration. Experiments on our constructed Geological Disaster NER data show that our approach achieves a higher F1 (0.88) than other NER approaches (including five supervised NER approaches and a semi-supervised NER approach using the ST strategy of expanding the training set with all pseudo-labeled data), demonstrating the effectiveness of our approach. Furthermore, experiments on four general Chinese NER datasets show that the framework of our approach is transferable.
      PubDate: 2022-10-18
       
  • Knowledge distillation based lightweight building damage assessment using
           satellite imagery of natural disasters

    • Free pre-print version: Loading...

      Abstract: Abstract Accurate and timely assessment of post-disaster building damage is of great significance for national development and social security concerns. However, due to the high timeliness requirements of disaster emergency response and the conflict that sufficient computing resources are not easily available in harsh environments, and therefore the lightweight AI-driven post-disaster building damage assessment model is highly needed. In this paper, we introduced a knowledge distillation-based lightweight approach for assessing building damage from xBD high-resolution satellite images with the purpose of reducing the dependence on computing resources in disaster emergency response scenarios. Specifically, an ensemble Teacher-Student knowledge distillation method was designed and compared with the xBD baseline model. The result has shown that, the knowledge distillation reduces the parameter number of the original model by 30%, and the inference speed is increased by 30%-40%. In the building localization task, the accuracy of teacher and student model are 0.879 and 0.832 (IOU) respectively. In the damage classification task, the accuracy of teacher and student are 0.798 and 0.775 respectively. In addition, we proposed a dual-teacher-student knowledge distillation strategy, which cannot use the pre-training skills of curriculum learning in student model training, but achieve the same effect through more direct knowledge transfer. In the experiment, our dual-teacher-student method improves the knowledge distillation baseline by 3.7% with 30 epoch training. With only 70% parameters, our student model performs close to the teacher model at a degradation within 5%.This study verifies the effectiveness and prospect of knowledge distillation method in building damage assessment for disaster emergency.
      PubDate: 2022-10-17
       
  • Probabilistic air quality forecasting using deep learning
           spatial–temporal neural network

    • Free pre-print version: Loading...

      Abstract: Regional air quality monitoring, a critical component of sustainable development is realized through various air quality observation stations established across a region. Accurate forecasting of air quality data collected from these observation stations requires the modelling of spatial–temporal patterns in the data. Deep learning algorithms, known for their ability to capture layers of abstraction, can proficiently achieve spatial–temporal modeling. However, deterministic models that produces point forecast does not consider the underlying model uncertainty during prediction and are therefore less reliable for real-time applications. Probabilistic forecasting models that forecast prediction intervals rather than point estimates can overcome this through uncertainty quantification. The objective of the proposed study is three-fold: i) develop an efficient deterministic deep learning spatial–temporal neural network named DL-STNN for spatial–temporal air quality forecasting; ii) investigate different approaches to uncertainty quantification in deep learning models and integrate some of them, such as Monte-Carlo Dropout, Ensemble Averaging, Gaussian Process Regression, Quantile Regression, and Bayesian Inference, in tandem with DL-STNN to facilitate probabilistic forecasting; and iii) evaluate the developed deterministic and probabilistic models, using a real-world Delhi air quality dataset. The evaluation results show that, among the deterministic models, DL-STNN outperforms the baselines with 39.8% more accurate predictions and performs consistently across all seasons in Delhi. Furthermore, among the DL-STNN-based tandem models that performed probabilistic forecasting, Bayesian DL-STNN proved efficient. It does 13% more accurate point forecasting and has 20% higher suitability score than the other tandem models, indicating that Bayesian inference adapts DL-STNN more reliable for real-time applications.
      PubDate: 2022-09-22
       
  • Predicting Co-movement patterns in mobility data

    • Free pre-print version: Loading...

      Abstract: Abstract Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.
      PubDate: 2022-09-22
       
  • Learning the micro-environment from rich trajectories in the context of
           mobile crowd sensing

    • Free pre-print version: Loading...

      Abstract: Abstract  With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant’s exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human’s micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models.
      PubDate: 2022-09-20
       
  • HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and
           temporal reasoning

    • Free pre-print version: Loading...

      Abstract: Abstract Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning techniques such as embedding have been applied to reasoning and inference tasks such as query answering and knowledge base completion. These subsymbolic and learnable representations are well suited for handling noise and efficiency problems that plagued prior work. However, applying embedding techniques to spatial and temporal reasoning has received little attention to date. In this paper, we explore two research questions: (1) How do embedding-based methods perform empirically compared to traditional reasoning methods on QSR/QTR problems' (2) If the embedding-based methods are better, what causes this superiority' In order to answer these questions, we first propose a hyperbolic embedding model, called HyperQuaternionE, to capture varying properties of relations (such as symmetry and anti-symmetry), to learn inversion relations and relation compositions (i.e., composition tables), and to model hierarchical structures over entities induced by transitive relations. We conduct various experiments on two synthetic datasets to demonstrate the advantages of our proposed embedding-based method against existing embedding models as well as traditional reasoners with respect to entity inference and relation inference. Additionally, our qualitative analysis reveals that our method is able to learn conceptual neighborhoods implicitly. We conclude that the success of our method is attributed to its ability to model composition tables and learn conceptual neighbors, which are among the core building blocks of QSR/QTR.
      PubDate: 2022-09-05
      DOI: 10.1007/s10707-022-00469-y
       
  • GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI
           and supervised machine learning

    • Free pre-print version: Loading...

      Abstract: Abstract The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.
      PubDate: 2022-09-03
      DOI: 10.1007/s10707-022-00476-z
       
  • Geographical information system for air traffic optimization using genetic
           algorithm

    • Free pre-print version: Loading...

      Abstract: Abstract The primary concern of an air traffic controller is to ensure the safety and fluidity of ever-increasing air traffic. This requires effective training through practical work supervised by instructors. Based on certain rules called separation rules, the trainee must find a solution to a traffic configuration defined by flight plans (FPL) initially containing a number of conflicts. This solution will then be compared to the one proposed by the instructor. The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made. The application will use the GAMA platform, very suitable for this and a set of tests composed of actual exercises will be performed to validate the work.
      PubDate: 2022-08-30
      DOI: 10.1007/s10707-022-00477-y
       
  • Optimizing vessel trajectory compression for maritime situational
           awareness

    • Free pre-print version: Loading...

      Abstract: Abstract We present an open-source system that can optimize compressed trajectory representations for large fleets of vessels. We take into account the type of each vessel in order to choose a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. We employ a genetic algorithm that converges to a fine-tuned configuration per vessel type without any hyper-parameter tuning. These configurations can provide synopses that retain less than 10% of the original points with less than 20m approximation error in a real world dataset; in another dataset with 90% less samples than the previous one, the synopses retain 20% of the points and achieve less than 80m error. Additionally the level of compression can be chosen by the user, by setting the desired approximation error. Our system also supports incremental optimization by training in data batches, and therefore continuously improves performance. Furthermore, we employ a composite event recognition engine to efficiently detect complex maritime activities, such as ship-to-ship transfer and loitering; thanks to the synopses generated by the genetic algorithm instead of the raw trajectories, we make the recognition process faster while also maintaining the same level of recognition accuracy. Our extensive empirical study demonstrates the effectiveness of our system over large, real-world datasets.
      PubDate: 2022-08-29
      DOI: 10.1007/s10707-022-00475-0
       
  • Terrain trees: a framework for representing, analyzing and visualizing
           triangulated terrains

    • Free pre-print version: Loading...

      Abstract: Abstract We propose a family of spatial data structures for the representation and processing of Triangulated Irregular Networks (TINs). We call such data structures Terrain trees. A Terrain tree combines a minimal encoding of the connectivity of the TIN with a hierarchical spatial index. Connectivity relations are extracted locally at run-time, within each leaf block of the hierarchy, based on specific application needs. Spatial queries are performed by exploring the hierarchical data structure. We present a new framework for terrain analysis based on Terrain trees. The framework, implemented in the Terrain trees library (TTL), contains algorithms for morphological features extraction, such as roughness and curvature, and for topology-based analysis of terrains. Moreover, it includes a technique for multivariate visualization, which enables the analysis of multiple scalar fields defined on the same terrain. To prove the effectiveness and scalability of such framework, we have compared the different Terrain trees against each other and also against the most compact state-of-the-art data structure for TINs. Comparisons are performed on storage and generation costs and on the efficiency in performing terrain analysis operations.
      PubDate: 2022-08-27
      DOI: 10.1007/s10707-022-00472-3
       
  • Real-time road safety optimization through network-level data management

    • Free pre-print version: Loading...

      Abstract: Abstract With the increasing connectedness of vehicles, real-time spatio-temporal data can be collected from citywide road networks. Innovative data management solutions can process the collected data for the purpose of reducing travel time. However, a majority of the existing solutions have missed the opportunity to better manage the collected data for improving road safety at the network level. We propose an efficient data management framework that uses network-level data to improve road safety for citywide applications. Our framework uses a graph-based data structure to maintain real-time network-level traffic data. Based on the graph, the framework uses a novel technique to generate driving instructions for individual vehicles. By following the instructions, inter-vehicular spacing can be increased, leading to an improvement of road safety. Experimental results show that our framework improves road safety, measured based on the time to collision between vehicles, from the state-of-the-art traffic data management solutions by a large margin while achieving lower travel times compared with the solutions. The framework is also readily deployable for large-scale real-time applications due to its low computation costs.
      PubDate: 2022-08-22
      DOI: 10.1007/s10707-022-00473-2
       
  • 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
      DOI: 10.1007/s10707-022-00470-5
       
  • Editor’s note

    • Free pre-print version: Loading...

      PubDate: 2022-07-18
      DOI: 10.1007/s10707-022-00468-z
       
  • 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
      DOI: 10.1007/s10707-021-00453-y
       
  • 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
      DOI: 10.1007/s10707-021-00451-0
       
  • 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
      DOI: 10.1007/s10707-021-00449-8
       
  • 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
      DOI: 10.1007/s10707-022-00462-5
       
 
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: 3.239.112.140
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-