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Research on map emotional semantics using deep learning approach / Daping Xi in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)  

Public [article]  inCartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 465 - 480 Titre : Research on map emotional semantics using deep learning approach Type de document : Article/Communication Auteurs : Daping Xi, Auteur ; Xini Hu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] émotion [Termes IGN] réseau neuronal profond Résumé : (auteur) The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules. Numéro de notice : A2023-235 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2172081 Date de publication en ligne : 21/02/2023 En ligne : https://doi.org/10.1080/15230406.2023.2172081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103594 [article]

A conceptual framework for developing dashboards for big mobility data / Lindsey Conrow in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)  

Public [article]  inCartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 495 - 514 Titre : A conceptual framework for developing dashboards for big mobility data Type de document : Article/Communication Auteurs : Lindsey Conrow, Auteur ; Cheng Fu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 495 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cadre conceptuel [Termes IGN] données massives [Termes IGN] mobilité humaine [Termes IGN] tableau de bord Résumé : (auteur) Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets. Numéro de notice : A2023-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2190164 Date de publication en ligne : 11/04/2023 En ligne : https://doi.org/10.1080/15230406.2023.2190164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103595 [article]