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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]

Local entropy map : a nonparametric approach to detecting spatially varying multivariate relationships / D. Guo in International journal of geographical information science IJGIS, vol 24 n° 9 (september 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n° 9 (september 2010) . - pp 1367 - 1389 Titre : Local entropy map : a nonparametric approach to detecting spatially varying multivariate relationships Type de document : Article/Communication Auteurs : D. Guo, Auteur Année de publication : 2010 Article en page(s) : pp 1367 - 1389 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multivariée [Termes IGN] entropie [Termes IGN] régression linéaire [Termes IGN] relation spatiale [Termes IGN] variable Résumé : (Auteur) The relationship between two or more variables may change over the geographic space. The change can be in parameter values (e.g., regression coefficients) or even in relation forms (e.g., linear, quadratic, or exponential). Existing local spatial analysis methods often assume a relationship form (e.g., a linear regression model) for all regions and focus only on the change in parameter values. Therefore, they may not be able to discover local relationships of different forms simultaneously. This research proposes a nonparametric approach, a local entropy map, which does not assume a prior relationship form and can detect the existence of multivariate relationships regardless of their forms. The local entropy map calculates an approximation of the Rényi entropy for the multivariate data in each local region (in the geographic space). Each local entropy value is then converted to a p-value by comparing to a distribution of permutation entropy values for the same region. All p-values (one for each local region) are processed by several statistical tests to control the multiple-testing problem. Finally, the testing results are mapped and allow analysts to locate and interactively examine significant local relationships. The method is evaluated with a series of synthetic data sets and a real data set. Numéro de notice : A2010-406 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658811003619143 En ligne : https://doi.org/10.1080/13658811003619143 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30599 [article]

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Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) / D. Guo in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n° 6-7 (june 2008) . - pp 801 - 823 Titre : Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) Type de document : Article/Communication Auteurs : D. Guo, Auteur Année de publication : 2008 Article en page(s) : pp 801 - 823 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] agrégation [Termes IGN] exploration de données géographiques [Termes IGN] partitionnement [Termes IGN] régionalisation (segmentation) [Termes IGN] regroupement de données Résumé : (Auteur) Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This research proposes and evaluates a family of six hierarchical regionalization methods. The six methods are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), and the complete linkage (CLK), each of which is constrained with spatial contiguity in two different ways (i.e. the first-order constraining and the full-order constraining). It is discovered that both the Full-Order-CLK and the Full-Order-ALK methods significantly outperform existing methods across four quality evaluations: the total heterogeneity, region size balance, internal variation, and the preservation of data distribution. Moreover, the proposed algorithms are efficient and can find the solution in O(n 2log n) time. With such data scalability, for the first time it is possible to effectively regionalize large data sets that have 10 000 or more spatial objects. A detailed comparison and evaluation of the six methods are carried out with the 2004 US presidential election data. Copyright Taylor & Francis Numéro de notice : A2008-233 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1080/13658810701674970 En ligne : https://doi.org/10.1080/13658810701674970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29228 [article]

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Supporting the process of exploring and interpreting space-time multivariate patterns: the visual inquiry toolkit / J. Chen in Cartography and Geographic Information Science, vol 35 n° 1 (January 2008)  

Public [article]  inCartography and Geographic Information Science > vol 35 n° 1 (January 2008) . - pp 33 - 50 Titre : Supporting the process of exploring and interpreting space-time multivariate patterns: the visual inquiry toolkit Type de document : Article/Communication Auteurs : J. Chen, Auteur ; Alan M. MacEachren, Auteur ; D. Guo, Auteur Année de publication : 2008 Article en page(s) : pp 33 - 50 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] analyse de groupement [Termes IGN] analyse visuelle [Termes IGN] données spatiotemporelles [Termes IGN] mise à l'échelle [Termes IGN] reconnaissance de formes [Termes IGN] visualisation Résumé : (Auteur) While many data sets carry geographic and temporal references, our ability to analyze these datasets lags behind our ability to collect them because of the challenges posed by both data complexity and tool scalability issues. This study develops a visual analytics approach that leverages human expertise with visual, computational, and cartographic methods to support the application of visual analytics to relatively large spatio-temporal, multivariate data sets. We develop and apply a variety of methods for data clustering, pattern searching, information visualization, and synthesis. By combining both human and machine strengths, this approach has a better chance to discover novel, relevant, and potentially useful information that is difficult to detect by any of the methods used in isolation. We demonstrate the effectiveness of the approach by applying the Visual Inquiry Toolkit we developed to analyze a data set containing geographically referenced, time-varying and multivariate data for U.S. technology industries. Copyright CaGISociety Numéro de notice : A2008-060 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1559/152304008783475689 En ligne : https://doi.org/10.1559/152304008783475689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29055 [article]

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Visual analytics of spatial interaction patterns for pandemic decision support / D. Guo in International journal of geographical information science IJGIS, vol 21 n° 8 (september 2007)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 21 n° 8 (september 2007) . - pp 859 - 877 Titre : Visual analytics of spatial interaction patterns for pandemic decision support Type de document : Article/Communication Auteurs : D. Guo, Auteur Année de publication : 2007 Article en page(s) : pp 859 - 877 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la décision [Termes IGN] analyse spatiale [Termes IGN] analyse visuelle [Termes IGN] distribution spatiale [Termes IGN] exploration de données géographiques [Termes IGN] graphe [Termes IGN] interaction humain-espace [Termes IGN] migration humaine [Termes IGN] mobilité humaine [Termes IGN] risque sanitaire [Termes IGN] santé [Termes IGN] surveillance sanitaire Résumé : (Auteur) Population mobility, i.e. the movement and contact of individuals across geographic space, is one of the essential factors that determine the course of a pandemic disease spread. This research views both individual-based daily activities and a pandemic spread as spatial interaction problems, where locations interact with each other via the visitors that they share or the virus that is transmitted from one place to another. The research proposes a general visual analytic approach to synthesize very large spatial interaction data and discover interesting (and unknown) patterns. The proposed approach involves a suite of visual and computational techniques, including (1) a new graph partitioning method to segment a very large interaction graph into a moderate number of spatially contiguous subgraphs (regions); (2) a reorderable matrix, with regions 'optimally' ordered on the diagonal, to effectively present a holistic view of major spatial interaction patterns; and (3) a modified flow map, interactively linked to the reorderable matrix, to enable pattern interpretation in a geographical context. The implemented system is able to visualize both people's daily movements and a disease spread over space in a similar way. The discovered spatial interaction patterns provide valuable insight for designing effective pandemic mitigation strategies and supporting decision-making in time-critical situations. Copyright Taylor & Francis Numéro de notice : A2007-321 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810701349037 En ligne : https://doi.org/10.1080/13658810701349037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28684 [article]

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Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach / D. Guo in Cartography and Geographic Information Science, vol 32 n° 2 (April 2005)  

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ICEAGE: interactive clustering and exploration of large and high-dimensional geodata / D. Guo in Geoinformatica, vol 7 n° 3 (September - November 2003)  

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