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Support vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)  

Public [article]  inTransactions in GIS > vol 17 n° 1 (February 2013) . - pp 40 - 61 Titre : Support vector machine for spatial variation Type de document : Article/Communication Auteurs : C. Andris, Auteur ; D. Cowen, Auteur ; J. Wittenbach, Auteur Année de publication : 2013 Article en page(s) : pp 40 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse discriminante [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] exploration de données géographiques [Termes IGN] seuillage Résumé : (Auteur) Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine-learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique ‘threshold line’ methodology for both discrete (regional) and continuous (k-neighbors) space. We then analyze the results of the regional and k-neighbor tests in order to respond to the methodological and geographic research questions. Numéro de notice : A2013-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01354.x Date de publication en ligne : 09/10/2012 En ligne : https://doi.org/10.1111/j.1467-9671.2012.01354.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32177 [article]

Discovering spatial patterns in origin-destination mobility data / D. Guo in Transactions in GIS, vol 16 n° 3 (June 2012)  

Public [article]  inTransactions in GIS > vol 16 n° 3 (June 2012) . - pp 411 - 429 Titre : Discovering spatial patterns in origin-destination mobility data Type de document : Article/Communication Auteurs : D. Guo, Auteur ; Hongxiao Jin, Auteur ; C. Andris, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 411 - 429 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatiale [Termes IGN] base de données d'objets mobiles [Termes IGN] cartographie des flux [Termes IGN] distribution spatiale [Termes IGN] données spatiotemporelles [Termes IGN] Kouangtoung (Chine) [Termes IGN] mobilité urbaine [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] origine - destination [Termes IGN] positionnement par GPS [Termes IGN] trajectographie par GPS [Termes IGN] transport urbain [Termes IGN] véhicule automobile Résumé : (Auteur) Mobility and spatial interaction data have become increasingly available due to the wide adoption of location-aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article, we focus on a special type of mobility data, i.e. origin-destination pairs, and present a new approach to the discovery and understanding of spatio-temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two-fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin-destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding. Numéro de notice : A2012-282 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01344.x Date de publication en ligne : 28/05/2012 En ligne : https://doi.org/10.1111/j.1467-9671.2012.01344.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31728 [article]