Padgham Mark, Boeing Geoff, Cooley David, Tierney Nicholas, Sumner Michael, Phan Thanh G, Beare Richard
Active Transport Futures, Muenster, Germany.
School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, United States.
Front Neurol. 2019 Aug 7;10:743. doi: 10.3389/fneur.2019.00743. eCollection 2019.
There is interest in the use geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source softwares (R and Python) for analyzing geospatial data. It is hoped that this introduction will stimulate interest in the field as well as generate ideas for improving stroke services. Instructions on installation of libraries for R and Python, source codes and links to census data are provided in a notebook format to enhance experience with running the software. The code illustrates different aspects of using geospatial analysis: (1) creation of choropleth (thematic) map which depicts estimate of stroke cases per post codes; (2) use of map to help define service regions for rehabilitation after stroke. Choropleth map showing estimate of stroke per post codes and service boundary map for rehabilitation after stroke. Conclusions The examples in this article illustrate the use of a range of components that underpin geospatial analysis. By providing an accessible introduction to these areas, clinicians and researchers can create code to answer clinically relevant questions on topics such as service delivery and service demand.
鉴于及时获得急性再灌注治疗的重要性，人们对利用地理空间数据来发展急性中风服务很感兴趣。本文旨在向临床医生和公民科学家介绍开源软件（R和Python）在分析地理空间数据方面提供的可能性。希望这一介绍能激发该领域的兴趣，并产生改善中风服务的想法。以笔记本格式提供了R和Python库的安装说明、源代码以及人口普查数据的链接，以增强运行软件的体验。代码展示了使用地理空间分析的不同方面：（1）创建描绘每个邮政编码区域中风病例估计数的分级统计图（专题地图）；（2）使用地图来帮助定义中风后康复的服务区域。展示每个邮政编码区域中风估计数的分级统计图以及中风后康复的服务边界图。结论本文中的示例说明了一系列支撑地理空间分析的组件的使用。通过对这些领域进行通俗易懂的介绍，临床医生和研究人员可以创建代码来回答有关服务提供和服务需求等主题的临床相关问题。
