Botswana comprises 28 census districts, which are constituted by administrative districts, cities and towns Figure 1. A total of 28 census districts from the population and housing census data were used for the analysis the unit of analysis is the census district. The selection of the study site was justified by the fact that most of the variables were available at census district level, and all the census districts have different levels of vulnerability which need to be investigated in order to inform mitigation and adjustment of related policies and programmes.
All the people in the census districts are at risk of experiencing any disaster owing to their varying coping ability demographic and socio-economic factors and recovery from the impacts of natural hazard events, hence the need to investigate their level of social vulnerability. Botswana population and housing census districts, Census districts shown using circles are constituted by cities and mining towns. A total of eight original variables of the Social Vulnerability Index Model developed by Cutter et al.
A vigilant selection and rationalisation of the variables, with the intention of retaining only those most pertinent as indicators of social vulnerability, was done. Some of the additional indicators capture ethnicity and the cultural structure of Batswana. Botswana has a variety of ethnic groups living together within the same area.
They gather together and socialise during certain events, like wedding ceremonies, funerals, Kgotla meetings and at workplaces. Each ethnic group holds on to its own culture institutional practices, beliefs, values, norms and traditional religion.
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The variable on the percent speaking a language at home other than Setswana was also used because it is an important component of ethnicity. The majority of the ethnic groups speak the Setswana Language as it is one of the two official languages in Botswana, other than the English Language see Table 2. The use of electricity for either cooking or lighting is a preparedness variable in the sense that when electricity is down owing to floods or as an impact from any other natural hazard event, electricity on the main grid can be switched on after a short time as opposed to using biomass for cooking.
Studies performed elsewhere support this, for example, Boruff and Cutter and Dunno Dunno adds that:. The District Social Vulnerability Index Model was computed because it quantifies the social vulnerability of different communities in a district to natural hazards, using social vulnerability indicators. After the data were collected, they were then normalised in order to create a standard dimension in the data set for ease of interpretation of the assessment results, though Liu and Li did not use the range 0—1.
The data were normalised into a standard dimension. The standardisation was carried out using the following formula, for both positive and negative correlation indicators:. While max x i and min x i mean the greatest and the smallest values of the selected indicators in the data set. An assessment of the weights was performed at a later stage by experts in the climate change field disasters expert, environmental statistician, climatologist and climate change expert.
Prior to calculating the individual index weights, the following processes were undertaken. It also maximises the sum of variances of the components.
The calculation of the rotated component matrix values followed. The aforesaid values were used in the calculation of the PCA-based weights as shown in the following sub-section. The calculation of PCA-based weights is an objective weighting method. The weights of the indicators included in this methodology can then be obtained from the above equation as follows:.
Spatial data were obtained from the census. The DSVI scores were mapped using standard deviations SD as the classification algorithm to highlight the extremes low and high in social vulnerability in the study area. This comparative measure allows one to visually or numerically see how similar or how different places are relative to each other Letsie , and therefore make the DSVI a comparative measure of vulnerability.
The colour ramps for the index scores were categorised as follows:. The eigenvalues were calculated, showing a total initial eigenvalue of 8. Table 3 also shows a total cumulative percentage of The highest squared loadings for social vulnerability indicators in each principal component are highlighted in bold. Initial eigenvalues and rotated eigenvalues of principal component analysis performed on indicators.
- Measuring social vulnerability to natural hazards at the district level in Botswana.
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Note: Extraction method — principal component analysis. Refer to Table 5 for full names of indicators and definitions. It is evident from Table 5 that the indicators with the highest contribution to district social vulnerability are number of persons per household 0. The standard data and assessment of District Social Vulnerability Index results by census district. Note: For abbreviated indicators, refer to Table 5 for full descriptions. For the spatial distribution of the DSVI scores of the districts, a mean of 0.
The district with the highest social vulnerability was Ngamiland West with a 1.
Therefore, the results show that urban census districts are less vulnerable to natural hazards as compared to the urban—rural census districts. With regard to the proportion of each category on the total number of districts, it is evident from the results that the number of districts with high, medium high, medium, medium low and low vulnerability were 3, 11, 4, 7 and 3, respectively, constituting Ranked District Social Vulnerability Index scores from the lowest to the highest by district.
A correlation analysis was run between DSVI scores and the number of households affected by floods to help to empirically provide evidence of the association between DSVI and the incidence of floods. The t -test shows a 0. However, there is still a need to run a correlation using long-term data to test for a correlation between the DSVI scores and the number of households affected by floods in order to get the robust results. In terms of gender being a predictor of social vulnerability, the result of this study is consistent with the study by Ajibade, McBean and Bezner-Kerr which reveals that gender coupled with income, occupation and health care access are predictors of social vulnerability.
Fako and Molamu confirm that the majority of female-headed households are generally resource-poor; hence, they are unable to mobilise enough resources to cushion themselves against the impacts of drought. Another social vulnerability study by Muyambo, Jordaan and Bahta reveals that gender and external support contribute majorly to the social vulnerability of communal farmers to drought in O.
Tambo District, and this is influenced by a huge imbalance in decision-making related to drought risk reduction. The cultural values of most African communities e. Tswana, Sotho and Xhosa, among others do not allow women to make decisions regarding the management of livestock; this is the sole responsibility of men. This study also reveals that low education contributes to increased social vulnerability, which is in accordance with the findings of the studies conducted elsewhere e.
Rufat et al. Even if the poor and marginalized face fewer economic damage costs, the relative impact of damaging flood events are generally greater for low-income groups. On the contrary, other studies e. Muyambo et al. It was expected that the O. Tambo district which has high illiteracy levels would score high on social vulnerability. Instead, the study revealed that the respondents perceived indigenous knowledge as contributing to the slight resilience the farmers exhibited towards drought Muyambo et al.
Literature elsewhere e. The aforementioned population often lacks the ability to cope and recover from the impacts of natural hazard events as they often have low economic status and they are physically not able bodied enough to cope. According to the census data, there is high unemployment, high poverty levels and a high percentage of female-headed households, among others, in the aforementioned urban—rural census districts.
These results are consistent with the findings of the study by Chen et al. Lack of employment exacerbates poverty levels, leaving the poor more prone and less resilient to the impacts of natural hazards. In an event where the unemployed and the poor are hit by floods, they cannot afford to rebuild houses that were destroyed by floods because of limited or a lack of financial resources. Furthermore, Cutter et al.
Disability was found to be one of the factors contributing to social vulnerability to natural hazards in Botswana. These are rural—urban districts with almost the same socio-economic developments. There is high poverty and high unemployment, among other factors, in the aforesaid districts. The communities in these districts are mostly dependent on agriculture in order to sustain their livelihoods.
Cutter et al. Most of them lack the ability to communicate language barrier for the deaf and the ability to see the blind , and these factors put them at high risk of being affected by the impacts of natural hazard events.
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