Centre dedocumentation
scientifique

Automating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)  

Public [article]  inInternational journal of cartography > vol 7 n° 1 (March 2021) . - pp 100 - 115 Titre : Automating and utilising equal-distribution data classification Type de document : Article/Communication Auteurs : Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur ; Ibad Kureshi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 100 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] attribut géomètrique [Termes descripteurs IGN] attribut sémantique [Termes descripteurs IGN] carte choroplèthe [Termes descripteurs IGN] classification [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] intervalle de classe Résumé : (Auteur) Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live. Numéro de notice : A2021-184 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1863000 date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.1080/23729333.2020.1863000 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97114 [article]

A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381 Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] appariement automatique [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] pondération [Termes descripteurs IGN] qualité des données Résumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158 [article]

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1559 - 1577 Titre : STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Zhenhong Du, Auteur ; Yuhua Gu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1559 - 1577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] origine - destination [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] taxi Résumé : (Auteur) Clustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York. Numéro de notice : A2020-768 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12662 date de publication en ligne : 19/07/2020 En ligne : https://doi.org/10.1111/tgis.12662 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96660 [article]

Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101524 Titre : Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city Type de document : Article/Communication Auteurs : Azam Raha Bahrehdar, Auteur ; Benjamin Adams, Auteur ; Ross S. Purves, Auteur Année de publication : 2020 Article en page(s) : n° 101524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] Londres [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] orthoimage géoréférencée [Termes descripteurs IGN] perception [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. Numéro de notice : A2020-710 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101524 date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101524 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96255 [article]

A framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 4 (October 2020) . - pp 745 - 776 Titre : A framework for group converging pattern mining using spatiotemporal trajectories Type de document : Article/Communication Auteurs : Bin Zhao, Auteur ; Xintao Liu, Auteur ; Jinping Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 745 - 776 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] base de données spatiotemporelles [Termes descripteurs IGN] comportement [Termes descripteurs IGN] convergence [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] jointure spatiale [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] trajectoire Résumé : (Auteur) A group event such as human and traffic congestion can be very roughly divided into three stages: converging stage before congestion, gathered stage when congestion happens, and dispersing stage that congestion disappears. It is of great interest in modeling and identifying converging behaviors before gathered events actually happen, which helps to proactively predict and handle potential public incidents such as serious stampedes. However, most of existing literature put too much emphasis on the second stage, only a few of them is dedicated to the first stage. In this paper, we propose a novel group pattern, namely converging, which refers to a group of moving objects converging from different directions during a certain period before gathered. To discover efficiently such converging patterns, we develop a framework for converging pattern mining (CPM) by examining how moving objects form clusters and the process of the “cluster containment”. The framework consists of three phases: snapshot cluster discovery phase, cluster containment join phase, and converging detection phase. As cluster containment mining is the key step, we develop three algorithms to discover cluster containment matches: a containment-join-algorithm, called SSCCJ, by using spatial proximity; a signature tree-based cluster-containment-join-algorithm, called STCCJ, which takes advantage of the cluster containment relations and signature techniques to filter enormous unqualified candidates in an efficient and effective way; and third, to keep the advantages of the above algorithms while avoiding their flaws, we further propose a signature quad-tree based cluster-containment-join algorithm, called SQTCCJ, which can identify efficiently matches by considering cluster spatial proximity as well as containment relations simultaneously. To assess the proposed methods, we redefine two evaluation metrics based on the concept of “Precision and Recall” in the field of information retrieval and the characteristics of converging patterns. We also propose a new indicator for measuring the duration of the converging stage in a group event. Finally, the effectiveness of the CPM and the efficiency of the mining algorithms are evaluated using three types of trajectory datasets, and the results show that the SQTCCJ algorithm demonstrates a superior performance. Numéro de notice : A2020-494 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00404-z date de publication en ligne : 25/04/2020 En ligne : https://doi.org/10.1007/s10707-020-00404-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96114 [article]

An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

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Breaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis / Batuhan Kilic in Cartography and Geographic Information Science, Vol 47 n° 4 (July 2020)  

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Mining spatiotemporal association patterns from complex geographic phenomena / Zhanjun He in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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Co-visualization of air temperature and urban data for visual exploration / Jacques Gautier (2020)    

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Extraction de connaissances pour la description de l'environnement maritime côtier à partir de textes d'aide à la navigation / Léa Lamotte in Revue des Nouvelles Technologies de l'Information, E.36 (2020)

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Knowledge graphs and big data processing / Valentina Janev (2020)  

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La modélisation en géographie / Denise Pumain (2020)

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Spationomy / Vit Pászto (2020)  

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