Analyses of Trends in the Fire Losses and the Fire-Brigade Call-Outs in South Africa Between 2004 and 2017

Abstract

The current study aimed at performing correlation analyses to gain a more detailed systems understanding of the temporal trends in financial losses from fires (disaster impacts) and the fire brigade call-outs (disaster response) in South Africa from the 2004 to 2017 period. The analysis is performed using data on the fire disaster impacts at the national level and the local level (Makana Local Municipality). The data on disaster impacts was extracted from databases and reports published by the Fire Protection Association of Southern Africa and Statistics South Africa. The total number of fire brigade call-outs/fires for the entire territory of South Africa ranged from 26574 in 2010 to 49567 in 2017. There was a direct correlation between the total number of fires and the losses from fires in residential settings in South Africa with time for the studied period. The losses from fire disasters in residential settings accounted for between 21.84 % and 74.06 % of all financial losses in South Africa between 2004 and 2017. On the other hand, call-outs to fire brigades with a residential cause or related to rubbish/bush/grass accounted for an average of 81.7 ± 2.7 % between 2004 and 2007, while it reached 88.5 ± 4.9 % in Makana Local Municipality. This indicates that fires in residential settings or related to rubbish or plant-based fuel account for a significant majority of the time fire brigades spent fighting fires in South Africa between 2004 and 2017. Fuel and waste management, and increasing fire human resilience at the household level, will play a significant role in the fire disaster risk management in South Africa. Vegetation cover and management will play a key role in the fire DRM in South Africa and local municipalities such as Makana.

Quantitative and Qualitative Data in Disaster Risk Management of Fires: A Case Study From South Africa at Various Geographical Levels

“…Some of the data from that study are reused here, namely the total number of the Google searches for fire in South Africa between 2004 and 2017. However, the data here are based on three data extraction occasions and reported as averages, which are slightly different than the previously reported data by Madondo et al (2022). That difference is minimal but is the results of the time sliding scale of the reverse data production by Keywordseverywhere.com plugin (Kovalenko et al, 2021).…”

“…To the best of the authors' knowledge, only fire tariffs for following financial years are available in the public domain for Makana Local Municipality: 2015/2016 and 2016/2017 (Makana, 2015(Makana, -2017 and 2019/2020 (Makana, 2019(Makana, -2020. Given the findings of Madondo et al (2022), majority of the fire brigade call outs are related in South Africa to fires dwellings, at household level. Therefore, a model fire case, and the related tariffs likely to be charged for it to residence in Makana Local Municipality, was run as follows.…”

“…(1) Those yearly volumes were investigated for statistically significant correlation with the total number of fire callouts/fires in South Africa between 2004 and 2017 (as summarised by Madondo et al, 2022 and designated as TFBC in further text of the article). At the same time, the total financial losses from fires were correlated with the total yearly searches for fire in South Africa (see Table 1 for value details and the summary by Madondo et al, 2022). That was done using the using the Spearman coefficient at 5 % level of significance (see https://www.socscistatistics.com/tests/spearman/default2.aspx for details; website accessed on 23 rd January 2023).…”

“…It is important to use data available to assess the public interest in fires at the smaller geographical scales. Madondo et al (2022) summarised the Makana fire callout numbers from 2004 until 2017. The data on fire brigade call outs in Makana can be used to estimate the probability of a fire on any day in a given calendar year in the municipality.…”

“…At the same time, the Spearman correlation coefficient between the TFSSA and TLFR was equal to 0.4780, but the correlation was not statistically significant at 5 % level of significance (p-value = 0.0985). Madondo et al (2022) derived Equation (3) to predict the total number of fires outside of the 2004-2017 time period for which the fire brigade calls out statistics were available from the Fire Protection association of Southern Africa. Some of the data from that study are reused here, namely the total number of the Google searches for fire in South Africa between 2004 and 2017.…”