A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research
A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research
复制标题
将视觉支持的推理插入地理分析工作流程的框架：在道路安全研究中的应用
DOI：
10.1111/gean.12338
复制
发表时间：
2022
影响因子：
3.6
通讯作者：
Beecham R
中科院分区：
文献类型：
--
作者：
Beecham R
道路安全研究是一个数据丰富的领域，具有巨大的社会影响。就像医学研究一样，目标是围绕可以挽救生命的风险因素建立知识。与医学研究不同，道路安全研究从混乱的观察数据集中产生经验性的发现。道路交通事故的记录包含许多交叉的分类变量，主要模式是复杂的混淆，当条件的数据，使推理网，观察到的影响是不确定性，由于样本量减少。我们展示了可视化数据分析方法如何将严谨性注入到对这些数据集的探索性分析中。一个框架，图形被用来暴露，建模和评估空间模式的观测数据，以及防止错误的发现。证据的框架是通过应用数据分析的国家碰撞模式记录在STATS19，在英国的道路碰撞信息的主要来源。我们的框架超越了探索性数据分析的典型概念，并转移到现代地理分析的复杂数据分析决策空间。
Road safety research is a data‐rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by confounding and, when conditioning on data to make inferences net of this, observed effects that are subject to uncertainty due to diminishing sample sizes. We demonstrate how visual data analysis approaches can inject rigor into exploratory analysis of such datasets. A framework is presented whereby graphics are used to expose, model and evaluate spatial patterns in observational data, as well as protect against false discovery. Evidence for the framework is presented through an applied data analysis of national crash patterns recorded in STATS19, the main source of road crash information in Great Britain. Our framework moves beyond typical depictions of exploratory data analysis and transfers to complex data analysis decision spaces characteristic of modern geographical analysis.