High-high cluster and high-low outlier road intersections for motorcycle road traffic crashes resulting in injuries 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 motorcycle (Motorcycle: Above 125cc, Motorcycle: 125cc and under, Quadru-cycle, Motor Tricycle) crash counts that resulted in injuries (slight, serious, fatalities) 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: 157 KBNumber of Files: The dataset contains a total of 158 road intersection records (11 "high-high" clusters and 147 "high-low" outliers)Date Created: 22nd 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 either a motor tricycle, motorcycle above 125cc, motorcycle below 125cc and quadru-cycles and that were additionally associated with a slight, 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 motorcycle crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with motorcycle crashes 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

本数据集详细收录了开普敦市内的道路交叉口及其所属郊区，经精心整理编制，重点呈现2017至2019年间，在“高-高”聚类与“高-低”异常渔网网格单元中，涉及摩托车（Motorcycle，含125cc以上、125cc及以下子类）、四轮摩托（Quadru-cycle）、机动三轮车（Motor Tricycle）且造成人员受伤（含轻伤、重伤、死亡）的高事故量案例。为提升数据集实用性，本数据集对事故高发月份进行了细致的颜色编码，以提供更精细化的分析视角。此外，数据集还根据道路交叉口所在的聚类类型进行分类：“高-高”聚类采用粉色标记点标注，“高-低”异常单元则以红色标记点标识。为便于浏览查询，所有道路交叉口均按郊区名称的字母顺序排序，确保内容清晰易读。数据详情：数据类型为时空分类数据（Geospatial-temporal categorical data），附带数值属性；文件格式为Word文档（.docx）；文件大小157 KB；记录数量：本数据集共包含158条道路交叉口记录，其中“高-高”聚类记录11条，“高-低”异常记录147条；创建日期：2024年5月22日。研究方法：数据收集方式：本次研究的道路交通事故描述性数据取自开普敦市网络信息系统，涵盖每起事故中涉事受害者的相关信息。处理软件包括ArcGIS Pro、Open Refine、Python及SQL。处理流程：首先通过Python对原始道路交通事故数据开展全面清洗，以确保数据准确性与一致性，同时移除重复条目，保障每起事故仅保留一条有效记录。随后使用Open Refine进行进一步数据整理，重点提取唯一的事故描述信息，用于后续在ArcGIS Pro中执行地理编码。需说明的是，本次处理仅保留道路交叉口事故数据，因其是唯一具备明确空间定义的事故类型。完成地理编码后，筛选出涉及机动三轮车（Motor Tricycle）、125cc以上摩托车（Motorcycle: Above 125cc）、125cc以下摩托车（Motorcycle: 125cc and under）及四轮摩托（Quadru-cycle），且造成轻伤、重伤或死亡后果的道路交叉口事故，作为后续时空分析的研究对象。本次研究采用的时空分析方法包括空间自相关（spatial autocorrelation）、热点分析（hotspot analysis）以及聚类与异常值分析（cluster and outlier analysis）。通过上述分析方法，筛选出被识别为“高-高”聚类或“高-低”异常值的道路交叉口事故，纳入本数据集。地理空间信息：空间覆盖范围：西边界坐标：18°20'E；东边界坐标：19°05'E；北边界坐标：33°25'S；南边界坐标：34°25'S；坐标系：南非参考系统（South African Reference System (Lo19)），采用通用横轴墨卡托投影（Universal Transverse Mercator projection）。时间信息：时间覆盖范围：起始日期：2017年1月1日；结束日期：2019年12月31日。