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An automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method / Jim Graham in Cartographica, Vol 57 n° 4 (December 2022)
[article]
Titre : An automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method Type de document : Article/Communication Auteurs : Jim Graham, Auteur Année de publication : 2022 Article en page(s) : pp 257 - 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Projections
[Termes IGN] carroyage
[Termes IGN] intégrité des données
[Termes IGN] polygone
[Termes IGN] projection
[Termes IGN] Python (langage de programmation)Résumé : (auteur) Selecting a map projection is key to minimizing distortion and thus clear communication of spatial data and accurate spatial analysis. Methods exist for selecting projections based on the intended area of use but not for finding polygons that can be used to clip geographic data to ensure the data are projected correctly and within desired distortion limits. The projection methods available in the Proj library were examined to determine the nature of the errors and distortions they created based on global data and a wide variety of available settings. Approaches were then identified for each projection including simple bounding boxes and more complex clipping polygons. To make sure that errors were not introduced into the projected data, data integrity polygons (DIPs) were created by placing a grid of cells over the Earth and then finding a cell near the origin that was within the specified criteria. Adjacent cells were added to the DIPs that met the criteria until no additional cells could be added. The criteria included projected cell sides could not intersect with themselves or other cells, the order of the cell corners could not be reversed, and distortion within the cell had to be within specified limits. I found that up to two DIPs with a limit on length distortion of a factor of 4 provided a general solution for all but three projection methods. Limitations included the time to find DIPs at high resolution. Clipping polygons and visualizations of the results were made available on a website. Numéro de notice : A2022-923 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2021-0015 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.3138/cart-2021-0015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102465
in Cartographica > Vol 57 n° 4 (December 2022) . - pp 257 - 269[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2022041 RAB Revue Centre de documentation En réserve L003 Disponible Cartographic propaganda in the age of social media: Empirical evidence from Ethiopia / Daniel K. Waktola in Cartographica, Vol 57 n° 4 (December 2022)
[article]
Titre : Cartographic propaganda in the age of social media: Empirical evidence from Ethiopia Type de document : Article/Communication Auteurs : Daniel K. Waktola, Auteur Année de publication : 2022 Article en page(s) : pp 281 - 290 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] désinformation
[Termes IGN] Ethiopie
[Termes IGN] géopolitique
[Termes IGN] information cartographique
[Termes IGN] propagande
[Termes IGN] représentation cartographique
[Termes IGN] réseau socialRésumé : (auteur) Cartographic propaganda is a conscious manipulation of a map to influence the reader’s belief. Countries often use it to claim disputed territories or project fear over opposing nations or political alliances, but little is known about the manipulations of maps along internal sociolinguistic and political fault lines on social media platforms. The author investigated the nature and intent of propaganda maps in Ethiopia before and after the 2018 government reform based on six purposely sampled maps prominently circulated on social media. While falling short of the acceptable cartographic qualities, the analysis of sample propaganda maps revealed two fundamental characteristics during the pre- and post-government reform. First, their role shifted from a centripetal force in the political coalition to a centrifugal force in the coalition’s disintegration. Second, their mode of dissemination transitioned from cartographic misinformation to disinformation. The findings of this study contribute empirical evidence to the ongoing cartographic information discourse that lags behind the rapidly changing map-making and map-sharing platforms in the age of geospatial and social media revolutions. Numéro de notice : A2022-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2022-0005 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.3138/cart-2022-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102464
in Cartographica > Vol 57 n° 4 (December 2022) . - pp 281 - 290[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2022041 RAB Revue Centre de documentation En réserve L003 Disponible Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)
[article]
Titre : Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale Type de document : Article/Communication Auteurs : Shengwu Qin, Auteur ; Shuangshuang Qiao, Auteur ; Jingyu Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2709 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] éboulement
[Termes IGN] hétérogénéité spatiale
[Termes IGN] prévention des risquesRésumé : (auteur) Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale. Numéro de notice : A2022-854 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05487-5 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1007/s11069-022-05487-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102101
in Natural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738[article]From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping / Tian Lan in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)
[article]
Titre : From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Oliver O'Brien, Auteur ; James Cheshire, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] carte interactive
[Termes IGN] cartographie sensible
[Termes IGN] culture
[Termes IGN] données démographiques
[Termes IGN] données socio-économiques
[Termes IGN] impact social
[Termes IGN] récit
[Termes IGN] Royaume-Uni
[Termes IGN] sciences sociales
[Termes IGN] web mappingMots-clés libres : narrative mapping Résumé : (auteur) Modern web mapping techniques have enhanced the storytelling capability of cartography. In this paper, we present our recent development of a web mapping facility that can be used to extract interesting stories and unique insights from a diverse range of socio-economic and demographic variables and indicators, derived from a variety of datasets. We then use three curated narratives to show that online maps are effective ways of interactive storytelling and visualisation, which allow users to tailor their own story maps. We discuss the reasons for the revival of the recent attention to narrative mapping and conclude that our interactive web mapping facility powered by data assets can be employed as an accessible and powerful toolkit, to identify geographic patterns of various social and economic phenomena by social scientists, journalists, policymakers, and the public. Numéro de notice : A2022-541 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-022-00117-x Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1007/s41651-022-00117-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101105
in Journal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 22[article]Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] ArcGIS
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] classification par arbre de décision
[Termes IGN] colinéarité
[Termes IGN] estimation bayesienne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] TéhéranRésumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292[article]Testing of a new way of cadastral maps renewal in Slovakia / Peter Kyseľ in Geodetski vestnik, vol 66 n° 4 (December 2022 - February 2023)PermalinkAutomatic vectorization of fluvial corridor features on historical maps to assess riverscape changes / Samuel Dunesme in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)PermalinkEvaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)PermalinkGraph neural networks with constraints of environmental consistency for landslide susceptibility evaluation / Haowei Zeng in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkTerrain representation using orientation / Gene Trantham in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)PermalinkFlash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkChallenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images : A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])PermalinkCorrecting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France / Michaela Nováková in Remote sensing of environment, vol 280 (October 2022)PermalinkDeep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)PermalinkDeveloping a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkCartographic enclosure and urban cadastral mapping in the Ethiopian Somali capital / Romy Emmenegger in Cartographica, vol 57 n° 3 (September 2022)PermalinkCharacteristics of augmented map research from a cartographic perspective / Yi Cheng in Cartography and Geographic Information Science, Vol 49 n° 5 (September 2022)PermalinkDesign and construction of a colourblind-friendly Surabaya city angkot route map prototype / Arzakhy Indhira Pramesti in Cartographica, vol 57 n° 3 (September 2022)PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)PermalinkA general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkHistorical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)PermalinkPoint-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)Permalink