Spatial accessibility of COVID-19 healthcare resources in Illinois, USA

This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * `COVID-19Acc.ipynb` is a notebook for calculating spatial accessibility and `COVID-19Acc.html` is an export of the notebook as HTML. * `Data` contains all of the data necessary for calculations: * `Chicago_Network.graphml`/`Illinois_Network.graphml` are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. * `GridFile/` has hexagonal gridfiles for Chicago and Illinois * `HospitalData/` has shapefiles for the hospitals in Chicago and Illinois * `IL_zip_covid19/COVIDZip.json` has JSON file which contains COVID cases by zip code from IDPH * `PopData/` contains population data for Chicago and Illinois by census tract and zip code. * `Result/` is where we write out the results of the spatial accessibility measures * `SVI/`contains data about the Social Vulnerability Index (SVI) * `img/` contains some images and HTML maps of the hospitals (the notebook generates the maps) * `README.md` is the document you're currently reading! * `requirements.txt` is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with `python3 -m pip install -r requirements.txt`本数据集涵盖了为测量新冠医疗资源空间可达性所开展研究的全部代码、Jupyter笔记文档与数据集，研究聚焦于美国伊利诺伊州（Illinois）。具体而言，本数据集针对伊利诺伊州居民获取医院床位与重症监护室（Intensive Care Unit, ICU）床位的空间可达性进行了测算。空间可达性的测算采用改进型两步移动搜索法（Enhanced Two-Step Floating Catchment Area, 简称E2FCA，Luo & Qi, 2009），该方法综合考量了需求端（即潜在患者/人口数量）与供给端（即床位或医师数量）的交互作用。最终输出的结果为医院床位空间可达性分布图，可识别出亟需补充医疗资源的区域，例如ICU床位与呼吸机配备情况。本配套笔记文档可为新冠疫情防控中医疗床位的区域调配提供参考指引。 ### 压缩包内容说明 本ZIP压缩包包含以下组件： 1. `COVID-19Acc.ipynb`：用于计算空间可达性的Jupyter笔记文档；`COVID-19Acc.html`为该笔记文档的HTML导出格式文件。 2. `Data/`文件夹：存放计算所需的全部基础数据： - `Chicago_Network.graphml`与`Illinois_Network.graphml`：分别为芝加哥与伊利诺伊州的开放街道地图（OpenStreetMap, 简称OSM）路网GraphML格式文件。 - `GridFile/`：包含芝加哥与伊利诺伊州的六边形网格文件。 - `HospitalData/`：包含芝加哥与伊利诺伊州医院的形状文件（Shapefile）。 - `IL_zip_covid19/COVIDZip.json`：JSON格式文件，包含来自伊利诺伊州公共卫生部（Illinois Department of Public Health, 简称IDPH）的按邮政编码划分的新冠确诊病例数据。 - `PopData/`：包含按人口普查区域与邮政编码划分的芝加哥与伊利诺伊州人口数据。 - `Result/`：用于输出空间可达性测算结果的存储目录。 - `SVI/`：包含社会脆弱性指数（Social Vulnerability Index, 简称SVI）相关数据。 - `img/`：存放部分图片与医院位置HTML交互地图（本笔记文档可自动生成此类地图）。 - `README.md`：即您当前阅读的说明文档。 - `requirements.txt`：列出运行本笔记文档所需的Python第三方包清单（不含Jupyter/IPython环境）。您可通过命令`python3 -m pip install -r requirements.txt`完成依赖安装。