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scientifique

Behavior-based location recommendation on location-based social networks / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 3 (July 2020) . - pp 477 – 504 Titre : Behavior-based location recommendation on location-based social networks Type de document : Article/Communication Auteurs : Seyyed Mohammadreza Rahimi, Auteur ; Behrouz Far, Auteur ; Xin Wang, Auteur Année de publication : 2020 Article en page(s) : pp 477 – 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] interface web [Termes descripteurs IGN] modèle conceptuel de données localisées [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] système de recommandation Résumé : (auteur) Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However, the existing methods suffer from one or more deficiencies such as data sparsity, cold-start users, ignoring users’ specific spatial and temporal behaviors, not utilizing the shared behaviors of the users. In this paper, we propose a novel location recommendation method, namely Behavior-based Location Recommendation (BLR). BLR recommends a location to a user based on the users’ repetitive behaviors and behaviors of similar users. Additionally, to better integrate the spatial information, BLR has two spatial components, a user-based spatial component to find the spatial preferences of the user, and a behavior-based spatial component to find locations of interest for different behaviors. Experimental studies on three real-world datasets show that BLR produces better location recommendations and can effectively address data sparsity and cold-start problems. Numéro de notice : A2020-370 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00360-3 date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1007/s10707-019-00360-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95265 [article]

NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735 Titre : NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages Type de document : Article/Communication Auteurs : Jimin Wang, Auteur ; Yingjie Hu, Auteur ; Kenneth Joseph, Auteur Année de publication : 2020 Article en page(s) : pp 719 - 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] flux de travaux [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] précision sémantique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] toponyme Résumé : (auteur) Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models. Numéro de notice : A2020-445 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12627 date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1111/tgis.12627 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95508 [article]

Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)  

Public [article]  inInternational Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119 Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher , Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière , Auteur ; Olivier Orfila, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] analyse fonctionnelle (mathématiques) [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte routière [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données routières [Termes descripteurs IGN] feu de circulation [Termes descripteurs IGN] inférence [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] transformation en ondelettes [Termes descripteurs IGN] vitesse Résumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2019-497 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755 [article]

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 759 - 776 Titre : A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation Type de document : Article/Communication Auteurs : Kevin Sparks, Auteur ; Gautam Thakur, Auteur ; Amol Pasarkar, Auteur ; Marie Urban, Auteur Année de publication : 2020 Article en page(s) : pp 759 - 776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] coutume [Termes descripteurs IGN] démographie [Termes descripteurs IGN] données géophysiques [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] estimation quantitative [Termes descripteurs IGN] ethnologie [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] trace numérique Résumé : (auteur) The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish. Numéro de notice : A2020-294 Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615069 date de publication en ligne : 04/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615069 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95126 [article]

Uber movement data: a proxy for average one-way commuting times by car / Yeran Sun in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 16 p. Titre : Uber movement data: a proxy for average one-way commuting times by car Type de document : Article/Communication Auteurs : Yeran Sun, Auteur ; Yinming Ren, Auteur ; Xuan Sun, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Boston (Massachusetts) [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] flux [Termes descripteurs IGN] migration pendulaire [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] origine - destination [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] taxi [Termes descripteurs IGN] temps de trajet [Termes descripteurs IGN] trace GPS Résumé : (auteur) Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston. Numéro de notice : A2020-255 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9030184 date de publication en ligne : 24/03/2020 En ligne : https://doi.org/10.3390/ijgi9030184 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95010 [article]

Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) / Chanwoo Jin in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Modelling perceived risks to personal privacy from location disclosure on online social networks / Fatma S. Alrayes in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)  

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Potential of crowdsourced traces for detecting updates in authoritative geographic data / Stefan Ivanovic (2020)  

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Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes / Antonio Tomás Mozas-Calvache in International journal of geographical information science IJGIS, vol 33 n°11 (November 2019)  

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A filtering-based approach for improving crowdsourced GNSS traces in a data update context / Stefan Ivanovic in ISPRS International journal of geo-information, vol 8 n° 9 (September 2019)  

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Accuracy assessment of speed values calculated from GNSS tracks of roads obtained from VGI / Antonio Tomás Mozas-Calvache in Survey review, vol 51 n° 367 (July 2019)  

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Crowdsourcing geographic information with a gamification approach / Roberta Martella in Geodetski vestnik, vol 63 n° 2 (June - August 2019)  

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Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

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A fuzzy formal concept analysis-based approach to uncovering spatial hierarchies among vague places extracted from user-generated data / Xiaoyu Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

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Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)  

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