High-high cluster and high-low outlier road intersections for road traffic crashes within the CoCT in 2017, 2018, 2019 and 2021

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018, 2019, and 2021. 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: 602 KBNumber of Files: The dataset contains a total of 625 road intersection records (606 "high-high" cluster and 19 "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. Following this, duplicate crash records were eliminated to retain only one entry per crash. 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 crashes that were able to be spatially defined.Once geocoded, the road traffic crash data underwent rigorous spatio-temporal analyses, encompassing spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections 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: 31/12/2021 (2020 data omitted)<br><br><br><br>

本数据集详细收录了开普敦市内的道路交叉口及其对应郊区信息，经精心编纂后，重点呈现2017、2018、2019及2021年期间，在“高-高聚类（high-high cluster）”与“高-低异常值渔网网格单元（high-low outlier fishnet grid cells）”中观测到的高事故次数案例。

为提升数据集的实用价值，本数据集对事故高发关联月份进行了细致的颜色编码，以提供更为精细化的分析视角。此外，本数据集根据道路交叉口所属的“高-高聚类”（以粉色标签标记）或“高-低异常值单元格”（以红色标签标记）进行分类。为便于浏览检索，所有道路交叉口还按照郊区名称的字母顺序进行排序，确保数据的可访问性与清晰性。

### 数据详情
数据类型：兼具数值属性的时空分类地理空间数据
文件格式：Word文档（.docx）
数据大小：602 KB
数据条目规模：本数据集共计收录625条道路交叉口记录（其中606条属于“高-高聚类”记录，19条为“高-低异常值”记录）
创建日期：2024年5月21日

### 研究方法
数据采集方法：本数据集所使用的涉事道路交通事故描述性数据，均取自开普敦市网络信息系统。
使用软件：ArcGIS Pro、Open Refine、Python、SQL
处理流程：首先通过Python软件对原始道路交通事故数据开展全面清洗；随后删除重复的事故记录，确保每起事故仅保留一条有效条目。接着使用Open Refine软件进行进一步精细化处理，重点提取唯一的事故描述信息，用于后续在ArcGIS Pro中开展地理编码工作。值得注意的是，本流程仅保留可进行空间定位的道路交叉口事故数据。完成地理编码后，对道路交通事故数据开展严格的时空分析，涵盖空间自相关分析、热点分析以及聚类与异常值分析。通过上述分析方法，提取出被判定为“高-高聚类”或“高-低异常值”的道路交叉口，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：采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：
起始日期：2017年1月1日
结束日期：2021年12月31日（未纳入2020年数据）