从基于Web的数据生成上下文变量用于健康研究:关于Web抓取,文本挖掘和空间覆盖分析的教程.
Pablo Galvez-Hernandez1,2, Angelina Gonzalez-Viana3, Luis Gonzalez-de Paz4,5
1Lawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
JMIR public health and surveillance
|January 8, 2024
概括
这项研究引入了一种新的8步方法 (WeTMS),它结合了网络抓取,文本挖掘和空间分析,以生成地理区域的上下文健康数据. 该方法成功识别了9000多个健康资产,以增强老年人的社会联系.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 地理空间健康
- 计算社会科学 计算社会科学
背景情况:
- 地理区域的上下文变量对于健康和社会研究至关重要.
- 由于缺乏监测系统或人口普查数据,获取上下文数据可能很困难.
研究的目的:
- 描述和实施一个8步方法 (WeTMS) 用于将Web文本数据转化为可分析的上下文数据集.
- 创建健康资产数据集,旨在加强加泰罗尼亚老年人的社会联系.
主要方法:
- WeTMS 方法集成了网络抓取,文本挖掘和空间叠加分析.
- Python和R用于数据提取,文本预处理,分类和空间分析.
- 该方法用于从2015年到2022年在374个卫生管辖区识别健康资产.
主要成果:
- 分析了17000多个网站,产生9546个健康资产 (5,022个活动,4,524个资源).
- 休和技能发展活动以及休/文化协会是最普遍的资产类型.
- 资产注册在不同司法管辖区有很大的差异,协议率在62.02%至99.47%之间.
结论:
- WeTMS方法提供了一个强大的方法,用于从互联网文本数据中生成上下文变量.
- 这种方法可以帮助卫生和社会研究人员有效地创建准备分析的数据集.
关键词:
背景变量是背景变量的变量.健康资产健康资产医疗服务研究 医疗服务研究多层次分析的多层次分析.程序评价 程序评价 程序评价社会连接 社交连接社会环境 社会环境 社会环境空间覆盖层分析文本采矿 文本采矿是什么网络抓取 网络抓取更多相关视频
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