代表(政治)
计算机科学
集合(抽象数据类型)
多样性(控制论)
自然灾害
妥协
数据科学
定量分析(化学)
人工智能
数据挖掘
情报检索
人机交互
地理
社会科学
化学
色谱法
社会学
政治
气象学
政治学
法学
程序设计语言
作者
Thomas Candela,Matthieu Péroche,Arnaud Sallaberry,Nancy Rodriguez,Christian Lavergne,Frédéric Leone
标识
DOI:10.1080/13658816.2022.2063872
摘要
The mapping of the damage caused by natural disasters is a crucial step in deciding on the actions to take at the international, national, and local levels. The large variety of representations that we have observed leads to problems of transfer and variations in analysis. In this article, we propose a representation, Regular Dot map (RD), and we compare it to 4 others routinely used to visualise post-disaster damage. Our comparison is based on a user study in which a set of participants carried out various tasks on multiple datasets using the various visualisations. We then analysed the behaviour during the experiment using three approaches: (1) quantitative analysis of user answers according to the reality on the ground, (2) quantitative analysis of user preferences in terms of perceived effectiveness and appearance, and (3) qualitative analysis of the data collected using an eye tracker. The results of this study lead us to believe that RD is the best compromise in terms of effectiveness among the various representations studied.
科研通智能强力驱动
Strongly Powered by AbleSci AI