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scientifique

Designing geovisual analytics environments and displays with humans in mind / Arzu Çöltekin in ISPRS International journal of geo-information, vol 8 n° 12 (December 2019)  

Public [article]  inISPRS International journal of geo-information > vol 8 n° 12 (December 2019) . - 6 p. Titre : Designing geovisual analytics environments and displays with humans in mind Type de document : Article/Communication Auteurs : Arzu Çöltekin, Auteur ; Sidonie Christophe , Auteur ; Anthony Robinson, Auteur ; Urška Demšar, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : 6 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] interface homme-machine [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] réalité virtuelle [Termes descripteurs IGN] représentation cartographique 3D [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) [Introduction] In this open-access Special Issue, we feature a set of publications under the theme “Human-Centered Geovisual Analytics and Visuospatial Display Design”. As the title suggests, the scope of this collection is on human-centered questions regarding visual analytics software environments; and the design of visuospatial displays within and beyond these environments. The essential building blocks of visual analytics (VA) are computers and humans [1]. Without computers (i.e., technology and quantitative methods such as those used in statistics and data science) VA simply would not exist. For decades now, it has been clear that computers are better than humans in processing large amounts of data, being capable of storing and quickly retrieving what is needed. Mechanisms such as parsing and filtering, automated pattern detection and machine learning, manual queries, and coordinated-view visualizations make visual analytics environments amazingly versatile and powerful [2]. The tools contained in VA environments assist us in spatial learning, discovery, and decision making [3,4]. It is important to remember that they can really only play an assistive role however, because tasks such as learning, interpreting patterns to make discoveries, and decision making are inherently qualitative. Often the goal is to make decisions based on observed patterns and anomalies. Such patterns and anomalies are much more likely to emerge (and if they are known to exist, they are better expressed) with visualizations than via numbers or tables alone [5]. Numéro de notice : A2019-614 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi8120572 date de publication en ligne : 11/12/2019 En ligne : https://doi.org/10.3390/ijgi8120572 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95213 [article]

Mapping urban fingerprints of odonyms automatically extracted from French novels / Ludovic Moncla in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2477 - 2497 Titre : Mapping urban fingerprints of odonyms automatically extracted from French novels Type de document : Article/Communication Auteurs : Ludovic Moncla, Auteur ; Mauro Gaio, Auteur ; Thierry Joliveau, Auteur ; Yves-François Le Lay, Auteur ; Pierre-Olivier Mazagol, Auteur Année de publication : 2019 Article en page(s) : pp 2477 - 2497 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] dix-neuvième siècle [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] Geoparsing [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] littérature [Termes descripteurs IGN] odonymie [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] route [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) In this paper, we propose and discuss a methodology to map the spatial fingerprints of novels and authors based on all of the named urban roads (i.e., odonyms) extracted from novels. We present several ways to explore Parisian space and fictional landscapes by interactively and simultaneously browsing geographical space and literary text. Our project involves building a platform capable of retrieving, mapping and analyzing the occurrences of named urban roads in novels in which the action occurs wholly or partly in Paris. This platform will be used in several areas, such as cultural tourism, urban research, and literary analysis. The paper focuses on extracting named urban roads and mapping the results for a sample of 31 novels published between 1800 and 1914. Two approaches to the annotation of odonyms are compared. First, we describe a proof of concept using queries made via the TXM textual analysis platform. Then, we describe an automatic process using a natural language processing (NLP) method. Additionally, we mention how the geosemantic information annotated from the text (e.g., a structure combining verbs, spatial relations, named entities, adjectives and adverbs) can be used to automatically characterize the semantic content associated with named urban roads. Numéro de notice : A2019-427 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1584804 date de publication en ligne : 17/03/2019 En ligne : https://doi.org/10.1080/13658816.2019.1584804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93560 [article]

DataPink, l'IA au service de l'information géographique / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)

Public [article]  inGéomatique expert > n° 126 (janvier - février 2019) . - pp 38 - 46 Titre : DataPink, l'IA au service de l'information géographique Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2019 Article en page(s) : pp 38 - 46 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] photo-interprétation assistée par ordinateur [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] télédétection électromagnétique Résumé : (Auteur) Récemment créée par Olivier Courtin, DataPink est une jeune entreprise innovante spécialisée dans la mise en œuvre de l’intelligence artificielle à l’information géographique. L’occasion de faire le point sur des techniques de pointe. Numéro de notice : A2019-298 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93264 [article]

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
IFN-001-P002120	PER	Revue	Nogent-sur-Vernisson	Salle périodiques	Exclu du prêt

A spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 4 (August 2018) . - pp 913 - 929 Titre : A spatial analysis of non‐English Twitter activity in Houston, TX Type de document : Article/Communication Auteurs : Matthew Haffner, Auteur Année de publication : 2018 Article en page(s) : pp 913 - 929 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] Houston (Texas) [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] régression [Termes descripteurs IGN] Twitter Résumé : (Auteur) The use of social media data in geographic studies has become common, yet the question of social media's validity in such contexts is often overlooked. Social media data suffers from a variety of biases and limitations; nevertheless, with a proper understanding of the drawbacks, these data can be powerful. As cities seek to become “smarter,” they can potentially use social media data to creatively address the needs of their most vulnerable groups, such as ethnic minorities. However, questions remain unanswered regarding who uses these social networking platforms, how people use these platforms, and how representative social media data is of users' everyday lives. Using several forms of regression, I explore the relationships between a conventional data source (the U.S. Census) and a subset of Twitter data potentially representative of minority groups: tweets created by users with an account language other than English. A considerable amount of non‐stationarity is uncovered, which should serve as a warning against sweeping statements regarding the demographics of users and where people prefer to post. Further, I find that precisely located Twitter data informs us more about the digital status of places and less about users' day‐to‐day travel patterns. Numéro de notice : A2018-574 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12335 date de publication en ligne : 11/04/2018 En ligne : https://doi.org/10.1111/tgis.12335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92320 [article]

Aggregate keyword nearest neighbor queries on road networks / Pengfei Zhang in Geoinformatica [en ligne], vol 22 n° 2 (April 2018)  

Public [article]  inGeoinformatica [en ligne] > vol 22 n° 2 (April 2018) . - pp 237 - 268 Titre : Aggregate keyword nearest neighbor queries on road networks Type de document : Article/Communication Auteurs : Pengfei Zhang, Auteur ; Huaizhong Lin, Auteur ; Yunjun Gao, Auteur ; Dongming Lu, Auteur Année de publication : 2018 Article en page(s) : pp 237 - 268 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] requête spatiale [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] système d'information géographique Résumé : (Auteur) Given a group Q of query points and a set P of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from P that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in Q$\mathcal {Q}$. Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies. Numéro de notice : A2018-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0315-0 date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1007/s10707-017-0315-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90726 [article]

Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

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Popularity-aware collective keyword queries in road networks / Sen Zhao in Geoinformatica [en ligne], vol 21 n° 3 (July - September 2017)  

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Classifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

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Reconstruction of itineraries from annotated text with an informed spanning tree algorithm / Ludovic Moncla in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

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Reconstruction automatique d'itinéraires à partir de textes descriptifs / Ludovic Moncla in Cartes & Géomatique, n° 227 (mars - mai 2016)    

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European handbook of crowdsourced geographic information, ch. 14. Querying VGI by semantic enrichment / Robert Lemmens (2016)    

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Metadata topic harmonization and semantic search for linked-data-driven geoportals: A case study using ArcGIS online / Yingjie Hu in Transactions in GIS, vol 19 n° 3 (June 2015)  

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SENTERRITOIRE pour la détection d’opinions liées à l’aménagement d’un territoire / Eric Kergosien in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)  

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Exploring and visualizing differences in geographic and linguistic web coverage / Ramya Venkateswaran in Transactions in GIS, vol 18 n° 6 (December 2014)  

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Publishing deep web geographic data / Helena Piccinini in Geoinformatica, vol 18 n° 4 (October 2014)  

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