Descripteur
Termes IGN > informatique > intelligence artificielle > ingénierie des connaissances > découverte de connaissances > exploration de données > exploration de données géographiques
exploration de données géographiquesSynonyme(s)fouille exploratoire de données géographiques |
Documents disponibles dans cette catégorie (126)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
PerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)
[article]
Titre : PerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis Type de document : Article/Communication Auteurs : Brian Swedberg, Auteur ; Donna J. Peuquet, Auteur Année de publication : 2017 Article en page(s) : pp 577 - 597 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] application web
[Termes IGN] calendrier
[Termes IGN] démonstration de faisabilité
[Termes IGN] dimension temporelle
[Termes IGN] exploration de données géographiques
[Termes IGN] filtrage d'information
[Termes IGN] fréquence
[Termes IGN] interactivité
[Termes IGN] sciences humaines et sociales
[Termes IGN] utilisateur
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Periodicity is embedded in all societies. As most of us organize our lives based on temporal structures, it is hard to imagine what life would be like without it. We experience periodicity through naturally occurring rhythms that exist in nature, such as sunrise/sunset, seasonal changes in the weather, and the tides. We also experience it through abstract means via cultural, political, religious ties, such as the weekend, Independence Day, and Ramadan. Forms of periodicity, like the examples above, are foundational to making sense of human activity because they provide contextual rationale and frame normaility. However, disparate calendars (e.g. Islamic vs. Gregorian), localized idiosyncrasies, and other variables greatly complicate the analytical ability to uncover and understand human activity at a given time within a specified region. We have developed PerSE (Periodicity in Spatiotemporal Events): a web application designed to aid users in the detection and analysis of calendar related periodicity in spatiotemporal event data sets via exploratory user interaction. PerSE is composed of several crossfiltering views: the Map, Attribute View, Time-Wheel, Timeline, and Table. Users interactively set and release filters on one or more of the views to detect and analyze calendar related periodicity. This paper illustrates the utility of PerSE through an in-depth description of the tool and proof of concept usage example. Numéro de notice : A2017-382 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0280-z En ligne : https://doi.org/10.1007/s10707-016-0280-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85813
in Geoinformatica > vol 21 n° 3 (July - September 2017) . - pp 577 - 597[article]Robust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)
[article]
Titre : Robust point cloud classification based on multi-level semantic relationships for urban scenes Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Yuan Li, Auteur ; Han Hu, Auteur ; Bo Wu, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 102 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification
[Termes IGN] description multiniveau
[Termes IGN] exploration de données géographiques
[Termes IGN] relation sémantique
[Termes IGN] semis de points
[Termes IGN] voxel
[Termes IGN] zone urbaineRésumé : (Auteur) The semantic classification of point clouds is a fundamental part of three-dimensional urban reconstruction. For datasets with high spatial resolution but significantly more noises, a general trend is to exploit more contexture information to surmount the decrease of discrimination of features for classification. However, previous works on adoption of contexture information are either too restrictive or only in a small region and in this paper, we propose a point cloud classification method based on multi-level semantic relationships, including point–homogeneity, supervoxel–adjacency and class–knowledge constraints, which is more versatile and incrementally propagate the classification cues from individual points to the object level and formulate them as a graphical model. The point–homogeneity constraint clusters points with similar geometric and radiometric properties into regular-shaped supervoxels that correspond to the vertices in the graphical model. The supervoxel–adjacency constraint contributes to the pairwise interactions by providing explicit adjacent relationships between supervoxels. The class–knowledge constraint operates at the object level based on semantic rules, guaranteeing the classification correctness of supervoxel clusters at that level. International Society of Photogrammetry and Remote Sensing (ISPRS) benchmark tests have shown that the proposed method achieves state-of-the-art performance with an average per-area completeness and correctness of 93.88% and 95.78%, respectively. The evaluation of classification of photogrammetric point clouds and DSM generated from aerial imagery confirms the method’s reliability in several challenging urban scenes. Numéro de notice : A2017-347 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.022 En ligne : https://dx.doi.org/10.1016/j.isprsjprs.2017.04.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85611
in ISPRS Journal of photogrammetry and remote sensing > vol 129 (July 2017) . - pp 86 - 102[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Exploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : Exploring spatiotemporal clusters based on extended kernel estimation methods Type de document : Article/Communication Auteurs : Jay Lee, Auteur ; Junfang Gong, Auteur ; Shengwen Li, Auteur Année de publication : 2017 Article en page(s) : pp 1154 - 1177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] données spatiotemporelles
[Termes IGN] estimation par noyau
[Termes IGN] exploration de données géographiques
[Termes IGN] groupe
[Termes IGN] implémentation (informatique)
[Termes IGN] infraction
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] système d'information géographiqueRésumé : (auteur) We examined three different ways to integrate spatial and temporal data in kernel density estimation methods (KDE) to identify space–time clusters of geographic events. Spatial data and time data are typically measured in different units along respective dimensions. Therefore, spatial KDE methods require special extensions when incorporating temporal data to detect spatiotemporal clusters of geographical event. In addition to a real-world data set, we applied the proposed methods to simulated data that were generated through random and normal processes to compare results of different kernel functions. The comparison is based on hit rates and values of a compactness index with considerations of both spatial and temporal attributes of the data. The results show that the spatiotemporal KDE (STKDE) can reach higher hit rates while keeping identified hotspots compact. The implementation of these STKDE methods is tested using the 2012 crime event data in Akron, Ohio, as an example. The results show that STKDE methods reveal new perspectives from the data that go beyond what can be extracted by using the conventional spatial KDE. Numéro de notice : A2017-243 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287371 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287371 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85179
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1154 - 1177[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)
[article]
Titre : Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem Type de document : Article/Communication Auteurs : Jonathan K. Nelson, Auteur ; Cynthia A. Brewer, Auteur Année de publication : 2017 Article en page(s) : pp 35 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] exploration de données géographiques
[Termes IGN] maladie non infectieuse
[Termes IGN] mise à l'échelle
[Termes IGN] Pennsylvanie (Etats-Unis)
[Termes IGN] problème d'unité zonale modifiable
[Termes IGN] regroupement de donnéesRésumé : (auteur) Socioeconomic and health analysts commonly rely on areally aggregated data, in part because government regulations on confidentiality prohibit data release at the individual level. Analytical results from areally aggregated data, however, are sensitive to the modifiable areal unit problem (MAUP). Levels of aggregation as well as the arbitrary and modifiable sizes, shapes, and arrangements of zones affect the validity and reliability of findings from analyses of areally aggregated data. MAUP, long acknowledged, remains unresolved. We present an exploratory spatial data analytical approach (ESDA) to understand the scalar effects of MAUP. To characterize relationships between data aggregation structures and spatial scales, we develop a method for statistically and visually exploring the local indicators of spatial association (LISA) exhibited between a variable and itself across varying levels of aggregation. We demonstrate our approach by analyzing the across-scale relationships of aggregated 2010 median income for the State of Pennsylvania and 2005–2009 cancer diagnosis rates for the State of New York between county–tract, tract–block group, and county–block group level US census designated enumeration units. This method for understanding the relationship between MAUP and spatial scale provides guidance to researchers in selecting the most appropriate scales to aggregate, analyze, and represent data for problem-specific analyses. Numéro de notice : A2017-100 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1093431 En ligne : https://doi.org/10.1080/15230406.2015.1093431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84479
in Cartography and Geographic Information Science > Vol 44 n° 1 (January 2017) . - pp 35 - 50[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2017011 RAB Revue Centre de documentation En réserve L003 Disponible A review of volunteered geographic information quality assessment methods / Hansi Senaratne in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)
[article]
Titre : A review of volunteered geographic information quality assessment methods Type de document : Article/Communication Auteurs : Hansi Senaratne, Auteur ; Amin Mobasheri, Auteur ; Ahmed Loai Ali, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 139 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] acquisition de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de données géographiques
[Termes IGN] qualité des donnéesRésumé : (auteur) With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI. Numéro de notice : A2017-030 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1189556 En ligne : http://dx.doi.org/10.1080/13658816.2016.1189556 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84023
in International journal of geographical information science IJGIS > vol 31 n° 1-2 (January - February 2017) . - pp 139 - 167[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2017011 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017012 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkMining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkOn discovering co-location patterns in datasets : a case study of pollutants and child cancers / Jundong Li in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkModeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkUnsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkIntegrating geo web services for a user driven exploratory analysis / Simon Moncrieff in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkImage based geo-localization in the Alps / Olivier Saurer in International journal of computer vision, vol 116 n° 3 (February 2016)PermalinkPersonal mobility pattern mining and anomaly detection in the GPS era / Dong-He Shih in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkQGIS 2 cookbook / Alex Mandel (2016)Permalink