A GIS-based analysis of the status of streetlighting on the WCG road network: towards a spatial asset repository to guide decision making and asset management - Truter, Jason Pierre (Department of Civil Engineering, 2026)
Background: The Western Cape Government (WCG) Roads Infrastructure Directorate oversees the management and maintenance of road network assets through systems like the Road network Information System (RNIS). However, streetlight data within the RNIS is incomplete, unverified, and lacks consolidation, limiting its utility for asset management and strategic planning. Accurate and comprehensive streetlight asset records are essential for supporting initiatives such as the Green Lighting program, which promotes LED retrofits to enhance lighting infrastructure quality and reduce energy consumption and running costs. Additionally, South Africa's rising electricity costs and scarcity necessitates efficient management of streetlighting infrastructure. GIS offers proven capabilities for spatial data management, analysis, and visualisation, making it a suitable tool for addressing these challenges. However, the absence of accurate and precise data, coupled with a lack of GIS-based analysis tools, hampers decision-making and planning efforts. This study aims to address these gaps by capturing, formalising, and transforming streetlight data into an actionable format to guide infrastructure management. Aim This study seeks to establish a comprehensive spatial dataset of streetlighting assets along the WCG road network and leverage GIS to inform decision-making processes. The primary objectives are: • To create an improved and verified spatial dataset of WCG-maintained streetlight assets. • To compile and visualise a conclusive streetlight repository, representing the current state of streetlighting. • To demonstrate the application of GIS analysis in identifying areas of concern and prioritising resource allocation. • To evaluate the suitability of GIS-based tools in enhancing strategic planning and infrastructure management. The study will employ geomatics-based methods for data capture, integration of data into a GIS environment, and use spatial analysis to derive actionable insights related to streetlight density, extent, and alignment with complementary datasets. These outputs aim to optimise resource allocation and support the expansion and maintenance of streetlighting infrastructure along the WCG road network. Method The study employed a GIS-based methodology within the Esri environment to develop a mobile streetlight asset capture system and analyse streetlight infrastructure across the Western Cape Road network. Consumer-grade smartphones equipped with GNSS capabilities, and the Esri Field Maps application were used for field data collection, offering cost-efficiency, ease of use, and compatibility with existing WCG GIS infrastructure. The application enabled geolocated streetlight data to be captured with 4-meter accuracy, and seamlessly integrate with the WCG ArcGIS Online environment, enabling the storage and live update of data in a Spatial Database Engine (SDE) geodatabase. Fieldwork, spanning two years, utilised a master feature class of existing streetlight records created through data aggregation and linear referencing, to guide data collection and verification. Challenges, such as GNSS accuracy and data gaps, were mitigated through strategic planning, supplemental remote sensing, and manual data edits. Captured data was analysed through spatial techniques to provide actionable insights. Spatial analysis was structured into three components: • Association of Related Attributes: Relationships between streetlights and the road network were established to create core datasets, forming the foundation for subsequent analyses and establishing the streetlight repository. • Streetlight Analysis: Location-based GIS tools were employed to map streetlight distributions, analyse light coverage through buffer analyses, and assess light intensity using overlapping count methods. • Road network Analysis: Geospatial techniques were applied to examine streetlight distribution along road networks, evaluate road illumination percentages, and perform density analyses (point and kernel density) to identify lighting patterns and hotspots. Outputs comprise of comprehensive geospatial datasets, encompassing over 9300 streetlights and 900 traffic light sets. Analysis also resulted in various streetlight and road network-based datasets to supporting strategic planning and optimised roadway lighting management. Results This study successfully developed a GIS-based system for capturing and analysing streetlight data for the WCG Roads Directorate. It achieved its primary objective of creating a consolidated, verified streetlight asset repository, significantly improving data centralisation for infrastructure management. The data capture process, using ArcGIS Field Maps, integrated GNSS data into the GIS environment, effectively addressed challenges such as system integration and fieldwork inefficiencies. While technical limitations like intermittent offline functionality and GNSS accuracy were identified, the methodology proved highly practical for large-scale data collection. The analysis tools demonstrated GIS's ability to provide actionable insights, such as identifying areas with inadequate light coverage or intensity, while the web application allowed stakeholders to visualize and query streetlight data alongside other road infrastructure layers and related datasets. Conclusions The study validated GIS as a powerful tool for enhancing decision-making, asset visualisation, and strategic planning in the WCG Roads Directorate. It demonstrated that GIS can effectively support both short-term operational needs and long-term infrastructure goals by providing comprehensive, spatially referenced datasets and analysis capabilities. The research offers a replicable model for integrating GIS into road asset management workflows, addressing challenges such as incomplete datasets and disconnected field operations. By facilitating data-driven decisions and optimising resource allocation, the system lays a strong foundation for future advancements in road asset management. This study also underscores the value of GIS in presenting data in interactive formats, supporting informed decision-making, and promoting efficient infrastructure planning and management practices.