University of Cape Town
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.docx (231.56 kB)

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

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posted on 2024-06-05, 08:58 authored by Simone VieiraSimone Vieira, Simon HullSimon Hull, Roger Behrens

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 Specifics

Data Type: Geospatial-temporal categorical data with numeric attributes

File Format: Word document (.docx)

Size: 231 KB

Number 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 2024


Data Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network Information

Software: ArcGIS Pro, Open Refine, Python, SQL

Processing 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 Information

Spatial Coverage:

West Bounding Coordinate: 18°20'E

East Bounding Coordinate: 19°05'E

North Bounding Coordinate: 33°25'S

South Bounding Coordinate: 34°25'S

Coordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projection

Temporal Information

Temporal Coverage:

Start Date: 01/01/2017

End Date: 31/12/2019


Centre for Transport Studies UCT



Department of Architecture, Planning and Geomatics

Product Type

  • Site Map File Set