The “conflictuality index” describes the impact that conflicts have in a certain area in 2010. Area subjected to violence and conflict may have problem to receive assistance to develop adaptation measures and may lack the wiliness of the authority to implement such adaptation. The index results from the second cluster of the Principal Component Analysis preformed among 18 potential variables. The analysis identifies two dominant variables, namely “distance from conflict events” and “conflict events density”, assigning equal weights (0.5) to the two variables. Before to perform the analysis the variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for “conflicts events density”) in order to be comparable. A shapefile of conflict events was gathered from the Uppsala Conflict Data Programme (UCDP). Considering that the effect of conflict aftermath may during years, we collect all the events position and number of victims from 1989 to 2010. The “distance from conflicts” was obtained via Euclidean distance calculation of the conflict events. The “conflict events density” is the result of a kernel density analysis on number of victims with a search radius of 200 Kilometer. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “adaptive capacity index” symbolizes the adaptive potential to implement adaptation measures that help avert potential impacts in 2010. Adaptive capacity is a significant factor in characterizing vulnerability. The IPCC (2001) describes adaptive capacity as the potential or ability of a system, region, or community to adjust to the effects or impacts of climate change (including climate variability and extremes). Adaptive capacity is considered to be “a function of wealth, technology, education, information, infrastructure, skills, access to resources and stability and management capabilities”. The means of the adaptive capacity are the assets and entitlements that communities and individuals can mobilize in the face of environmental change. The more assets people have, the less vulnerable they are and vice versa. Here adaptive capacity is described as being dependent upon four assets: human capital, technological capital, financial capital and institutional capital. Therefore, the “adaptive capacity index” derived from the addition of four underlying indexes: the “human capital index”, the “technological capital index”, the “financial capital index” and the “institutional capital index”. Principal Component Analysis determines the weights of the four underlying indexes. The analysis run among the variables that compose such indexes and results in four weights: 0.58 for the “human capital index”, 0.16 for the “technological capital index”, 0.20 for the “financial capital index” and finally 0.06 for the “institutional capital index”. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “infrastructure index” describes the degree of development of physical facilities and networks in a certain area in 2010. The quality of infrastructure is an important measure of the relative adaptive capacity of a region. Regions with developed infrastructure systems are presumed to be better able to adapt to climatic stresses. Improved infrastructure may reduce transactions costs, and strengthen the links between labor and product markets. Moreover, improved infrastructure should encourage the formation of non-farm enterprises as a source of diversification in the short run and, eventually, a transition out of agriculture. The index results from the second cluster of the Principal Component Analysis preformed among 10 potential variables. The analysis identifies three dominant variables, namely “road density”, “road availability” and “infrastructure poverty”, assigning weights of 0.47, 0.36 and 0.17, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. A shapefile of road network was published by the Center for International Earth Science Information Network of Columbia University in 2013. The “road density” was computed by calculating the Kilometers of road per cell (size 0.5 arc-minute) and then running a focal statistic (radius of about 30 km to spread the effect of a transportation network in a neighborhood). The “road availability” is the road density divided by the logarithm of population. The 0.5 arc-minute grid “infrastructure poverty” is based on the average lights per pixel in 2010, which was produced by NOAA National Geophysical Data Center, divided by the logarithm of population. The original data was highly fragmented and at fine resolution may have contained fine-scale artifacts at urban edges due to data mismatch between the population and night-lights datasets. Thus focal statistics ran within 20 Km to calculate an average values and represents some of the extend influence of the infrastructure network for local people. The density and availability of road is a normally accepted indicator of infrastructure development degree. Moreover, developed road network facilitate the diffusion of rural products to large markets enhancing the income of rural population and sharing the risk of crisis among larger area. The average night light density per capita represents the diffusion of electricity among population and here is considered a proxy of diffusion of developed infrastructural network. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “human sensitivity index” represents the social component of sensitivity in 2010 and it is related to the degree to which a human society is dependent from the agriculture production. It derived from the combination of three underlying indexes: the “rurality index”, the “malnourishment index” and the “gender gap index”. These original indexes resulted from Principal Component Analysis among 14 potential variables, and they represent the three principal component highlighted by such analysis. The analysis allowed compute the weights for the three underlying index as 0.5, 0.3 and 0.2 for the “rurality index”, the “malnourishment index” and the “gender gap index” respectively. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “technological capital index” symbolizes the physical means that determine the potential adaptive capacity degree of a particular unit of analysis in 2010. It is linked with the diffusion of basic life technology and infrastructure (e.g. transportation network). It is the combination of two underlying indexes: the “household technology index” and the “infrastructure index”. These original indexes correspond to the two principal components recognized by Principal Component Analysis among 10 potential variables. The analysis assigned relative weights to the “household technology index” and the “infrastructure index”, that are 0.62 and 0.38, respectively. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The “vulnerability index” represents the level of vulnerability of a certain region to climate change impacts, given the situation of 2010. The index results from the multiplication of the “exposure index” by the “sensitive index”, and divided by the “adaptive capacity index”. The relative weights were defined through Principal Component Analysis (PCA) among the natural logarithm of the variables which compose the underlying indexes, and they were used like exponential coefficient (0.11 for exposure, 0.33 for sensitivity and 0.56 for adaptive capacity). Alternatively an additive model can be adopted. In this case the PCA run among the underlying variables. However, the result was not significantly different. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.