High-high cluster and high-low outlier road intersections for road traffic crashes involving severely injured pedestrians within the CoCT in 2017, 2018 and 2019

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high pedestrian crash counts resulting in serious injuries observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 231 KBNumber of Files: The dataset contains a total of 245 road intersection records (7 "high-high" clusters and 238 "high-low" outliers)Date Created: 21st May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved a pedestrian with a severe or fatal injury type were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these pedestrian crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with pedestrian crashes that resulted in a severe injury identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

本数据集详尽地收录了开普敦地区道路交叉口及其对应郊区的信息，经过精心编制，旨在突出2017年至2019年间在“高-高”集群和“高-低”异常网格单元中观察到的，导致严重伤害的高行人事故发生情况。为提升其实用性，数据集对与事故发生频率增加相关的月份进行了细致的色彩编码，从而提供了更为丰富的视角。此外，数据集根据交叉口在“高-高”集群（以粉色标签标记）或“高-低”异常单元（以红色标签标示）中的位置对道路交叉口进行分类。为了便于导航，交叉口还根据所在郊区的名称按字母顺序排列，确保了数据的可访问性和清晰度。

数据详细信息
数据类型：具有数值属性的地理时空分类数据
文件格式：Word文档（.docx）
大小：231 KB
文件数量：数据集包含总计245条道路交叉口记录（7个“高-高”集群和238个“高-低”异常）
创建日期：2024年5月21日

研究方法
数据收集方法：从开普敦市网络信息中获取了每起事故中涉及的事故受害者描述性道路交通事故数据
软件：ArcGIS Pro、Open Refine、Python、SQL
处理步骤：原始道路交通事故数据经过Python软件的综合精炼处理，以确保其准确性和一致性。随后，消除了重复项，仅保留每起事故事件的一条记录。接着，使用Open Refine软件进行了进一步的数据精炼，特别关注于提取独特的交通事故描述，以便在ArcGIS Pro中进行后续的地理编码。值得注意的是，在此过程中，仅保留了具有空间定义的道路交叉口事故，因为它们是唯一的事故类型。

地理信息
地理覆盖范围：
西经边界坐标：18°20'EE
东经边界坐标：19°05'EE
北纬边界坐标：33°25'S
南纬边界坐标：34°25'S
坐标系：使用通用横轴墨卡托投影的南非参考系统（Lo19）

时间信息
时间覆盖范围：
开始日期：01/01/2017
结束日期：31/12/2019