Ten factors resulting from the statistical analysis for SoVI® Brazil explain 67.0 % of the variance—similar to the original SoVI® constructions for the United States. Overall the factors correspond to Brazil’s settlement and development process. Each factor’s name, cardinality, and drivers are listed in Table 2. A brief interpretation of the factors is presented below.
The first factor explains 19.5 % of the variance and represents poverty. The poorest and least developed populations are usually more easily devastated by natural hazards and have a harder time recovering from them. This is the factor where indicators of wealth, development, and education loaded negatively (white population, female labor force, college education) and indicators of poverty, dependency, and lack of education loaded positively (for example, Brown population, households with large numbers of people, social dependency, illegal workers). The northern regions show a concentration of areas with higher vulnerability (Fig. 3a), a function of the country’s historic and enduring development process.
The second factor (Fig. 3b) identified areas of low development (agriculture workers, no phone, subsistence, lacking garbage collection and sewer infrastructure) loading negatively and indicators of urban areas (employment (transportation, commerce workers, renters, legal workers) and college education) loading positively. We took the inverse of this factor so that higher percentages of agricultural workers, and no sewer infrastructure, meant higher levels of social vulnerability. This factor explains 17.6 % of the variance.
The third factor explains 5.4 % of the variance and is driven by residents born in other states and immigration within the past 3 to 5 years. The Center-West and part of the North region show the highest vulnerability (Fig. 3c). In the past, the Southeast region used to receive the highest rate of immigrants. The expansion of agricultural land in the Center-West and North regions that has attracted more people, and the lower rates of economic growth in the Southeast that is reflected in lower job offer rates, precipitated this change in internal migration patterns. Many internal migrants are returning populations that left in the past and are now coming back to their original cities or states. In the case of a disaster, migrants, especially those who have recently moved to a new city, would have less experience with the conditions in their new living area and lack knowledge about the types of natural hazards likely to happen there. This could result in more difficulty reacting to and recovering from disasters.
Special Needs Population
The fourth factor is driven by population with special needs and females and explains 4.9 % of the variance. The Northeast region shows the highest vulnerability, the North region has the lowest overall vulnerability (Fig. 3d). Special needs populations can be greatly affected by disasters, since they require special attention or infrastructure for mobility and rescuing purposes. Females can have a harder time when facing disasters, especially in the recovery period, due to dependency on certain employment sectors (service industry such as hotel maids), lower wages, and family care responsibilities.
Race (Indian) and Poor Infrastructure
The fifth factor is driven by Indian population, households with no electricity, and houses built with poor construction materials. The factor explains 4.2 % of the variance. Indian population is usually related to lower levels of development and poor infrastructure, as well as more fragile types of housing construction. These populations have a harder time preparing for and recovering from disasters. The areas close to the Amazon have higher social vulnerability and reflect the large concentrations of Indian populations in those areas (Fig. 3e).
Lack of Public Employment
The sixth factor explains 3.9 % of the variance and is driven by the level of public sector employment (for example, the lack of population employed in public administration, defense, and social security). A minor driver is the lack of health coverage. Public employment is usually related to secure jobs, which would indicate an asset especially in the recovery process from a natural hazard event. Health coverage indicates populations with better health indices and a larger availability of health assistance after a natural disaster. The Center-West region (including the Federal District) and part of the North region presented the cities with lower social vulnerability scores (Fig. 3f).
The seventh factor explains 3.2 % of the variance and is driven by employment in accommodation and food service activities. Regions that are heavily dependent on tourism-related activities have a harder time recovering from a natural hazard event that could diminish the tourist resources and infrastructure in the area for a long time. Border and coastal cities that are usually driven towards tourist activities are the most vulnerable (Fig. 3g).
Black populations drive this factor that explains 3.1 % of the variance. Pardo and Asian populations loaded positively and White and Indian populations loaded negatively, the latter with less strength. This factor highlights the racial and ethnic diversity and racial mixing in the country. Bigger cities and more developed areas usually have more diverse racial concentrations where disparities in income and education contribute to increased social vulnerability. Smaller communities and those with similar racial backgrounds generally have better community organization traditions and enhanced social networks that lead to lower levels of vulnerability. Because the different indicators of race are ambiguous and have different signs (positive and negative), we adopted the absolute value for this factor (Fig. 3h).
The ninth factor is driven by population density (inhabitants per km2) and explains 2.8 % of the variance. A large population in the same area suggests not only that more people would be affected by a disaster, but also that they would have more difficulty in an eventual evacuation or rescue situation, making them more vulnerable to the natural hazard. State capitals and larger cities, usually with regional economic importance, and a few places in northeastern areas illustrate higher vulnerability (Fig. 3i). The Center-West region has many agricultural areas and lower social vulnerability cities.
The tenth factor is driven by extractive industry employment and explains 2.6 % of the variance. Populations that rely on extractive industries can face a long period of unemployment after disasters. If an entire region or city depends on extraction industry activities, major economic problems could occur in the aftermath of a disaster. The central portion of the country concentrates extractive industry activities that make these areas more socially vulnerable than other areas in the country (Fig. 3j).