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scientifique

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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1425 - 1448 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 descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] logement [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] R (langage) [Termes descripteurs IGN] service fondé sur la position [Termes descripteurs IGN] Singapour Résumé : (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 [article]

JUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)  

Public [article]  inGPS solutions > vol 25 n° 3 (July 2021) . - Article 85 Titre : JUST: MATLAB and python software for change detection and time series analysis Type de document : Article/Communication Auteurs : Ebrahim Ghaderpour, Auteur Année de publication : 2021 Article en page(s) : Article 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] série temporelle Résumé : (Auteur) Change detection within unequally spaced and non-stationary time series is crucial in various applications, such as environmental monitoring and satellite navigation. The jumps upon spectrum and trend (JUST) is developed to detect potential jumps within the trend component of time series segments. JUST can simultaneously estimate the trend and seasonal components of any equally or unequally spaced time series by considering the observational uncertainties or measurement errors. JUST and its modules can also be applied to monitor vegetation time series in near-real-time. Herein, the details of the open-source software package for JUST, developed in both MATLAB and Python, are presented. Numéro de notice : A2021-330 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01118-x date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01118-x Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97500 [article]

Stop-and-move sequence expressions over semantic trajectories / Yenier Torres Izquierdo in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 793 - 818 Titre : Stop-and-move sequence expressions over semantic trajectories Type de document : Article/Communication Auteurs : Yenier Torres Izquierdo, Auteur ; Grettel Monteagudo Garcia, Auteur ; Marco A. Casanova, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 793 - 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] algorithme de recherche [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] langage de requête [Termes descripteurs IGN] RDF [Termes descripteurs IGN] SPARQL Résumé : (auteur) Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data. Numéro de notice : A2021-270 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1793157 date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1793157 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97328 [article]

Spatial analysis of subway passenger traffic in Saint-Petersburg / Tatiana Baltyzhakova in Geodesy and cartography, vol 47 n° 1 (January 2021)  

Public [article]  inGeodesy and cartography > vol 47 n° 1 (January 2021) . - pp 10 - 20 Titre : Spatial analysis of subway passenger traffic in Saint-Petersburg Type de document : Article/Communication Auteurs : Tatiana Baltyzhakova, Auteur ; Aleksei Romanchicov, Auteur Année de publication : 2021 Article en page(s) : pp 10 - 20 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Blender [Termes descripteurs IGN] diagramme de Voronoï [Termes descripteurs IGN] flux [Termes descripteurs IGN] Mapbox [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] QGIS [Termes descripteurs IGN] R (langage) [Termes descripteurs IGN] Saint-Petersbourg [Termes descripteurs IGN] trafic [Termes descripteurs IGN] transport public Résumé : (auteur) The purpose of the paper is to create clear visualization of passenger traffic for Saint Petersburg subway system. This visualization can be used to better understand the passenger flow and to make more informed decisions in future planning. Research was based on officially published information about passenger traffic on subway station for years 2016 and 2018. Visualization was created with the variety of methods and software: Voronoi diagrams (QGIS software), social gravitation potential (R programming language), presentation of gravitation potential as a relief (Blender software), service zones of ground transport accessibility (2GIS, QGIS and Mapbox mapping platform). In this research, authors propose the use of intersection between the service zones and social gravitation potential isolines as an instrument for spatial analysis of traffic data. Analysis shown that current development of subway system does not correspond to passenger distribution. All stations were classified according to their accessibility and propositions about future directions of development were made. Numéro de notice : A2021-451 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3846/gac.2021.11980 date de publication en ligne : 12/03/2021 En ligne : https://doi.org/10.3846/gac.2021.11980 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97869 [article]

Data mining / Derya Birant (2021)  

Public Titre : Data mining : methods, applications and systems Type de document : Monographie Auteurs : Derya Birant, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 200 p. ISBN/ISSN/EAN : 978-1-83968-319-0 Note générale : Print ISBN 978-1-83968-318-3 eBook (PDF) ISBN 978-1-83968-320-6 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] acquisition de données [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre de décision [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] série temporelle Index. décimale : 26.40 Intelligence artificielle Résumé : (Editeur) Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment. Note de contenu : 1. Deep Learning: Exemplar Studies in Natural Language Processing and Computer Vision / Selma Tekir and Yalin Bastanlar 2. Contribution to Decision Tree Induction with Python: A Review / Bouchra Lamrini 3. Association Rule Mining on Big Data Sets / Oguz Celik, Muruvvet Hasanbasoglu, Mehmet S. Aktas and Oya Kalipsiz 4. Data Mining in Banking Sector Using Weighted Decision Jungle Method / Derya Birant 5. Analytical Statistics Techniques of Classification and Regression in Machine Learning / Pramod Kumar, Sameer Ambekar, Manish Kumar and Subarna Roy 6. Clustering of Time-Series Data / Esma Ergüner Özkoç 7. Weather Nowcasting Using Deep Learning Techniques / Makhamisa Senekane, Mhlambululi Mafu and Molibeli Benedict Taele 8. Data Mining and Machine Learning for Software Engineering / Elife Ozturk Kiyak 9. Data Mining for Student Performance Prediction in Education / Ferda Ünal 10. Tracer Transport in a Homogeneous Porous Medium: Experimental Study and Acquisition Data with LabVIEW / Sana Dardouri and Jalila Sghaier 11. Data Mining and Fuzzy Data Mining Using MapReduce Algorithms / Poli Venkata Subba Reddy Numéro de notice : 26539 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87784 En ligne : http://doi.org/10.5772/intechopen.87784 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97753

FOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)  

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A new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello in Journal of the Royal Statistical Society: Series C Applied Statistics, vol inconnu ([01/01/2021])  

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fusionImage: An R package for pan‐sharpening images in open source software / Fulgencio Cánovas‐García in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Python software tools for GNSS interferometric reflectometry (GNSS-IR) / Angel Martín in GPS solutions, Vol 24 n° 4 (October 2020)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Creating a web mapping portal to manage Malta’s underwater cultural heritage / Mélissa Dupuis (2020) 

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Creation of inspirational Web Apps that demonstrate the functionalities offered by the ArcGIS API for JavaScript / Arthur Genet (2020) 

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Création d’un outil d’interrogation du référentiel régional pédologique de Bretagne pour estimation du stock de carbone organique du sol / Louise Grall (2020) 

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Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability / Annarosa Quarello (2020)  

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