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Predicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)
[article]
Titre : Predicting user activity intensity using geographic interactions based on social media check-in data Type de document : Article/Communication Auteurs : Jing Li, Auteur ; Wenyue Guo, Auteur ; Haiyan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] interaction spatiale
[Termes IGN] mobilité humaine
[Termes IGN] modèle non linéaire
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau neuronal récurrent
[Termes IGN] utilisateurRésumé : (auteur) Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows. Numéro de notice : A2021-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080555 Date de publication en ligne : 17/08/2021 En ligne : https://doi.org/10.3390/ijgi10080555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98206
in ISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 555[article]Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility / Qingqing Chen in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
[article]
Titre : Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility Type de document : Article/Communication Auteurs : Qingqing Chen, Auteur ; Ate Poorthuis, Auteur Année de publication : 2021 Article en page(s) : pp 1425 - 1448 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] géopositionnement
[Termes IGN] logement
[Termes IGN] mobilité urbaine
[Termes IGN] R (langage)
[Termes IGN] service fondé sur la position
[Termes IGN] SingapourRésumé : (auteur) Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which – compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research. Numéro de notice : A2021-449 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887489 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97861
in International journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1425 - 1448[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021071 SL Revue Centre de documentation Revues en salle Disponible Improving human mobility identification with trajectory augmentation / Fan Zhou in Geoinformatica, vol 25 n° 3 (July 2021)
[article]
Titre : Improving human mobility identification with trajectory augmentation Type de document : Article/Communication Auteurs : Fan Zhou, Auteur ; Ruiyang Yin, Auteur ; Goce Trajcevski, Auteur ; Kunpeng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 453 - 483 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] itinéraire
[Termes IGN] mobilité humaine
[Termes IGN] modèle numérique de déplacement
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal récurrent
[Termes IGN] utilisateurRésumé : (auteur) Many location-based social networks (LBSNs) applications such as customized Point-Of-Interest (POI) recommendation, preference-based trip planning, travel time estimation, etc., involve an important task of understanding human trajectory patterns. In particular, identifying and linking trajectories to users who generate them – a problem called Trajectory-User Linking (TUL) – has become a focus of many recent works. TUL is usually studied as a multi-class classification problem and has gained recent attention because: (1) the number of labels/classes (i.e., users) is way larger than the number of motion patterns among various trajectories; and (2) the location-based trajectory data, especially the check-ins – i.e., events of reporting a location at particular Point of Interest (POI) with known semantics – are often extremely sparse. Towards addressing these challenges, we introduce a Trajectory Generative Adversarial Network (TGAN) as an approach to enable learning users motion patterns and location distribution, and to eventually identify human mobility. TGAN consists of two jointly trained neural networks, playing a Minimax game to (iteratively) optimize both components. The first one is the generator, learning trajectory representation by a Recurrent Neural Network (RNN) based model, aiming at fitting the underlying trajectory distribution of a particular individual and generate synthetic trajectories with intrinsic invariance and global coherence. The second one is the discriminator – a Convolutional Neural Network (CNN) based model that discriminates the generated trajectory from the real ones and provides guidance to train the generator model. We demonstrate that the above two models can be well tuned together to improve the TUL performance, while achieving superior accuracy when compared to existing approaches. Numéro de notice : A2021-972 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00378-7 Date de publication en ligne : 29/08/2019 En ligne : https://doi.org/10.1007/s10707-019-00378-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100390
in Geoinformatica > vol 25 n° 3 (July 2021) . - pp 453 - 483[article]Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics / Somayeh Dodge in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)
[article]
Titre : Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics Type de document : Article/Communication Auteurs : Somayeh Dodge, Auteur ; Evgeny Noi, Auteur Année de publication : 2021 Article en page(s) : pp 353 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] données de flux
[Termes IGN] interaction humain-espace
[Termes IGN] origine - destination
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] trajet (mobilité)
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This paper argues for a “human-centered” approach to knowledge discovery from movement data through the use of visualization and mapping. As movement data becomes more available and diverse in dimension and resolution, mapping becomes particularly important in the exploratory analysis of movement trajectories and for capturing patterns and structures in large origin-destination flow data sets. Movement phenomena (e.g. ranging from micro-movements of humans and animals to macro-level mobility, to migration flows, to spread of viruses) are complex dynamic processes which are realized in a multidimensional location-time-context space. This paper provides a comprehensive overview of various visualization techniques for mapping movement through the lens of cartography and with a special focus on the “human user” (e.g. data scientist, analyst, domain expert, etc.). We systematically characterize and categorize available techniques based on their visual specifications and functional capacities for human control, map-interaction, and design flexibility. These elements are beneficial to enhance the user’s capacities for map reasoning and knowledge discovery. Trends and gaps in the literature on movement visualization over the past 10 years are discussed. Our review suggests that future research should focus more on the role of the “human” in the development of human-centered visual analytic and exploratory tools, while providing functionalities for mapping uncertainty and protecting individual privacy in knowledge discovery of movement. These tools should be guided by a cartographic framework and visual principles specifically pertinent to movement. Numéro de notice : A2021-446 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1913763 Date de publication en ligne : 21/05/2021 En ligne : https://doi.org/10.1080/15230406.2021.1913763 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97853
in Cartography and Geographic Information Science > vol 48 n° 4 (July 2021) . - pp 353 - 375[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2021041 RAB Revue Centre de documentation En réserve L003 Disponible Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
[article]
Titre : Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes Type de document : Article/Communication Auteurs : Qingying Yu, Auteur ; Yonglong Luo, Auteur ; Dongxia Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1346 - 1373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] direction
[Termes IGN] données spatiotemporelles
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] mobilité urbaine
[Termes IGN] Pékin (Chine)
[Termes IGN] plan de déplacement urbain
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] segmentation
[Termes IGN] trajet (mobilité)
[Termes IGN] vitesse de déplacementRésumé : (auteur) Residents’ trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability. Numéro de notice : A2021-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1901904 Date de publication en ligne : 15/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1901904 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97860
in International journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1346 - 1373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021071 SL Revue Centre de documentation Revues en salle Disponible Reconsidering movement and exposure: Towards a more dynamic health geography / Malcolm Campbell in Geography compass, vol 15 n° 6 (June 2021)PermalinkSimulating multi-exit evacuation using deep reinforcement learning / Dong Xu in Transactions in GIS, Vol 25 n° 3 (June 2021)PermalinkUnderstanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)PermalinkWhat is the difference between augmented reality and 2D navigation electronic maps in pedestrian wayfinding? / Weihua Dong in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)PermalinkEvaluating the effectiveness of different cartographic design variants for influencing route choice / Stefan Fuest in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)PermalinkIntegration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkModelling the effect of landmarks on pedestrian dynamics in urban environments / Gabriele Filomena in Computers, Environment and Urban Systems, vol 86 (March 2021)PermalinkFinding the most navigable path in road networks / Ramneek Kaur in Geoinformatica, vol 25 n° 1 (January 2021)PermalinkIncorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkPermalinkPermalinkPermalinkRecueil des contributions, Colloque international Tous (im)mobiles, tous cartographes ? Approches cartographiques des mobilités, des circulations, des flux et des déplacements : Méthodes, outils, représentations, pratiques et usages / Françoise Bahoken (2021)PermalinkStability of urban forms: modelling the emergence of collective behaviour in residential trajectories / Arthur Benichou (2021)PermalinkPermalinkExploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)PermalinkGroup diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)PermalinkSTME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkUsing multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkContext-aware similarity of GPS trajectories / Radu Mariescu-Istodor in Journal of location-based services, vol 14 n° 4 ([01/11/2020])Permalink