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Incorporation of spatial anisotropy in urban expansion modelling with cellular automata / Jinqu Zhang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 86 - 113 Titre : Incorporation of spatial anisotropy in urban expansion modelling with cellular automata Type de document : Article/Communication Auteurs : Jinqu Zhang, Auteur ; Yu Ling, Auteur ; A - Xing Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 86 - 113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] anisotropie [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] Kouangtoung (Chine) [Termes IGN] modèle de simulation [Termes IGN] régression logistique [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations. Numéro de notice : A2022-044 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1852475 Date de publication en ligne : 30/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1852475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99403 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible

A learning-based approach to automatically evaluate the quality of sequential color schemes for maps / Taisheng Chen in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)  

Public [article]  inCartography and Geographic Information Science > Vol 48 n° 5 (September 2021) . - pp 377-392 Titre : A learning-based approach to automatically evaluate the quality of sequential color schemes for maps Type de document : Article/Communication Auteurs : Taisheng Chen, Auteur ; Menglin Chen, Auteur ; A - Xing Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 377-392 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] amélioration des couleurs [Termes IGN] apprentissage automatique [Termes IGN] charte de couleurs [Termes IGN] cohérence des couleurs [Termes IGN] contraste de couleurs [Termes IGN] couleur (rédaction cartographique) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] palette de couleurs [Termes IGN] saturation de la couleur [Termes IGN] visualisation cartographique Résumé : (auteur) Color quality evaluation is key to judging map quality, which can improve data visualization and communication. However, most existing methods for evaluating map colors are tedious and subjective manual methods. In this paper, we study sequential color schemes, a widely used map color type and propose a learning-based approach for evaluating the color quality. The approach consists of two steps. First, we extract and characterize the cartographic factors for determining the quality of sequential color schemes, such as color order, color match, color harmony, color discrimination and color uniformity. Second, we present a model to predict the color quality based on AdaBoost, a type of ensemble learning algorithm with excellent classification performance and use these factors as input data. We conduct a case study based on 781 samples and train the AdaBoost-based model to predict the quality of sequential color schemes. To evaluate the model’s performance, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). The AUC values are 0.983 and 0.977 on the training data and testing data, respectively. These results indicate that the proposed approach can be used to automatically evaluate the quality of sequential color schemes for maps, which helps mapmakers select good colors. Numéro de notice : A2021-642 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1936184 Date de publication en ligne : 29/06/2021 En ligne : https://doi.org/10.1080/15230406.2021.1936184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98335 [article]

Predictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 315 - 331 Titre : Predictive mapping with small field sample data using semi‐supervised machine learning Type de document : Article/Communication Auteurs : Fei Du, Auteur ; A - Xing Zhu, Auteur ; Jing Liu, Auteur ; Lin Yang, Auteur Année de publication : 2020 Article en page(s) : pp 315 - 331 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage semi-dirigé [Termes IGN] covariance [Termes IGN] échantillon [Termes IGN] modèle de simulation [Termes IGN] représentation cartographique Résumé : (Auteur) Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small. Numéro de notice : A2020-174 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12598 Date de publication en ligne : 04/12/2019 En ligne : https://doi.org/10.1111/tgis.12598 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94900 [article]

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1873 - 1893 Titre : A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 1873 - 1893 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Aves [Termes IGN] carte thématique [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] échantillon [Termes IGN] erreur d'échantillon [Termes IGN] erreur de positionnement [Termes IGN] erreur systématique [Termes IGN] habitat (nature) [Termes IGN] modèle de simulation [Termes IGN] phénomène géographique [Termes IGN] pondération [Termes IGN] précision de localisation [Termes IGN] régression logistique [Termes IGN] representativité [Termes IGN] science citoyenne [Termes IGN] Wisconsin (Etats-Unis) Résumé : (auteur) Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy. Numéro de notice : A2019-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615071 Date de publication en ligne : 10/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93490 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2019092	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Improving the quality of cartographic colour reproduction using the self-organizing map method / Mingguang Wu in Cartographic journal (the), Vol 55 n° 3 (August 2018)  

Public [article]  inCartographic journal (the) > Vol 55 n° 3 (August 2018) . - pp 273 - 284 Titre : Improving the quality of cartographic colour reproduction using the self-organizing map method Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; A - Xing Zhu, Auteur ; Li He, Auteur Année de publication : 2018 Article en page(s) : pp 273 - 284 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte de Kohonen [Termes IGN] carte thématique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] couleur imprimée [Termes IGN] qualité cartographique [Termes IGN] représentation cartographique Résumé : (Auteur) Colour distortion, which is caused by the unavoidable mismatch between a map’s gamut and a device’s gamut, negatively affects the semiotic quality of maps. Cartographic communication often suffers from undesirable colour inconsistency. This method models cartographic colour reproduction as a constrained transform problem, namely, adapting a map’s gamut to fit a device’s gamut while preserving the semiotic quality. First, the characteristics of the map’s gamut are investigated by considering cartographic principles, and the fundamental concerns of preserving semiotic quality are proposed. Then, the self-organizing map method is introduced to iteratively optimize the cartographic colour reproduction. We implement this method and evaluate it based on a series of thematic maps. The results indicate that the proposed algorithm offers better results than two alternatives in terms of facilitating cartographic colour reproduction. Numéro de notice : A2018-519 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2017.1414106 Date de publication en ligne : 18/10/2018 En ligne : https://doi.org/10.1080/00087041.2017.1414106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91325 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information / Yongzhi Wang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena / Guiming Zhang in Transactions in GIS, vol 22 n° 1 (February 2018)  

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A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

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Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function / Guiming Zhang in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

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vol 27 n° 7-8 - july - august 2013 - Digital terrain analysis and modelling [Suivi de] Geological applications of digital terrain analysis (Bulletin de International journal of geographical information science IJGIS) / Qiming Zhou

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Knowledge discovery from area-class resource maps: capturing prototype effects / F. Qi in Cartography and Geographic Information Science, vol 35 n° 4 (October 2008)  

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An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm / C. Qin in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)  

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An experiment using a circular neighborhood to calculate slope gradient from a DEM / X. Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 2 (February 2007)  

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A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes / Tao Pei in International journal of geographical information science IJGIS, vol 20 n° 2 (february 2006)  

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Knowledge discovery from soil maps using inductive learning / F. Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)  

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