GIS-based Spatial Modelling of Road Traffic Noise Distribution in Nairobi City, Kenya

Abstract

Background: Noise is one of the externalities of transportation systems in cities. To help understand which neighborhoods have noise levels exceeding the World Health Organization’s (WHO) thresholds, scholars have proposed the use of spatial noise maps. Most cities in developed nations have regularly produced these maps. Unfortunately, in developing nations, citizens still lack this important tool to help in the identification of quieter neighborhoods. One such, is Nairobi, the capital city of Kenya hence the reason for this study. In previous studies, varying methods have been used to produce Road Traffic Noise maps, such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), among others, each of which has its pros and cons. Aims: This study aimed to develop a GIS-based spatial model of Road Traffic Noise distribution in Nairobi city in Kenya. This was motivated by the fact that the ambient environmental conditions of urban neighborhoods are an indicator of how the welfare of their residents is taken care of. Study Design: This study deployed the GIS-based IDW method. Place and Duration of Study: The Department of Civil and Construction Engineering, University of Nairobi coordinated data collection across the city, between 6th July, 2025 and 12th July, 2025. Methodology: A seven-day Road Traffic Noise data for twelve hours from 6 AM to 6 PM using a handheld Class 1 Sound Level Meter was collected. The data was then bundled into two-hour intervals, resulting in six map presentations. GIS-based spatial maps were then subsequently produced using IDW method. Results: According to the maps, the noisiest time falls between 12 PM and 2 PM, while the low-noise periods occur from 8 AM to 10 AM and from 4 PM to 6 PM. Inferentially, noisy road corridors have high speed in the periods mentioned, and quieter corridors are heavily congested over the same time period. Notorious noisy road corridors in Nairobi include Ngong Road, Waiyaki Way, Eastleigh area, and Thika Road, and the quieter neighborhoods are in Runda and Karen areas. Conclusion: This study concludes that the GIS-based noise maps are helpful tools for residents to choose areas of habitation with a tolerable soundscape and to make noise management sustainable, by deploying GIS-based real-time noise maps accessible via the internet web display.