Original Research

Tracking Gauteng thunderstorms using Crowdsourced Twitter data between Soweto and Pretoria

Laurie Butgereit
The Journal for Transdisciplinary Research in Southern Africa | Vol 10, No 3 | a178 | DOI: https://doi.org/10.4102/td.v10i3.178 | © 2014 Laurie Butgereit | This work is licensed under CC Attribution 4.0
Submitted: 07 March 2016 | Published: 30 December 2014

About the author(s)

Laurie Butgereit, Research associate at Nelson Mandela Metropolitan University, South Africa

Full Text:



Summer thunderstorms in Gauteng are often dramatic, noisy, wet events. They can appear suddenly on exceptionally hot sunny days travelling fast across the province. With such dramatic arrivals, people often flock to social media sites such as Twitter to comment on the rain, wind, hail, lightning and thunder. This paper investigates the possibility of mapping the track of Gauteng thunderstorms by using crowdsourced data from Twitter. This paper describes a model (entitled the ThunderChatter Model) and instantiation of that model which extracts data from Twitter, analyses the textual information for thunderstorm information and plots the appropriate data on a map. For evaluation purposes, these generated maps are then compared against lightning-stroke maps provided by the South African Weather Service. The maps are visually compared by independent people using Content Analysis techniques ensuring unbiased and reproducible results. The results of this research are mixed. For thunderstorms which traverse the strip of land between Soweto and Pretoria more or less correlated to the N1 highway (and representing the most heavily populated area of Gauteng and the area with the highest percentage of home Internet facilities), the results are excellent. However, in outlying areas of Gauteng such as Carletonville, Heidelberg, Hammanskraal and Bronkhorstspruit, the thunderstorms are only trackable using crowdsourced Twitter data in the case of extreme storms which damage property. The results imply that data obtained from social media could be used in some cases to supplement geographical data obtained from traditional sources.


No related keywords in the metadata.


Total abstract views: 2212
Total article views: 1339

Crossref Citations

No related citations found.