A GIS-based modeling of environmental health risks in populated areas of Port-au-prince, Haiti
par Myrtho Joseph
University of Arizona - Master in Natural Resources Information System 1987
The assessment of the state of vulnerability of an area to natural disasters has traditionally paid attention to the intensity or the scale of a natural event (Hamza and Zetter, 1998). Obviously, as a possible source of danger, the more powerful a hazard is the greater the likelihood that a catastrophe will result. However, the living conditions that frame a population's neighborhood before a disaster occurs is a key factor of the vulnerability of this population to the event (idem). Blaikie et al. (1994) relate vulnerability to the capacity of a person or a group to foresee, to deal, to resist, and recuperate from the impact of a catastrophe. As this approach suggests, people's social and economic characteristics are the center of disaster assessment. According to Bakrim (2001) two extents of vulnerability can be measured: collective and individual. The collective vulnerability result from the conditions prevailing in the economy as a whole, which determines the Gross Domestic Product (GDP), the institutional framework, the financial resources available, and the infrastructures (Adger, 1999). At the individual level the vulnerability is measured by the access of a person or groups to the resources (Bakrim 2001). In this context, at either level, poverty is one of the determinants of vulnerability.
Based on these aforementioned characteristics of vulnerability, several models have been proposed for its assessment. Smit and Pilifosova (2003) represent vulnerability as a function of the exposure (E) and the adaptive capacity (AC) of a given community, in a given location, for a given climatic stimuli, and at a given period of time:
Vslit = f(Eslit, ACslit)
Though the specific mathematical form of this relationship is not stated, the direction of variation is known. E is positively correlated to V while AC has a negative correlation with V. That is, the vulnerability increases with the exposure level, and the greater is the potential to cope with the hazard, the lesser the vulnerability (Mcleman and Smit 2006, Ferrier and Haque 2003).
Furthermore, the components of vulnerability have been identified as access to various forms of capital, financial, physical, social, and human (Sorensen et al. 2006), which some studies crystallize in the GDP per capita or the population density (e.g. Schmidt-Thomé 2006, UNDP 2004). As a result, poor people face greater exposure to hazards because of lower housing standards, location, and lack of access to capital and information (Sorensen et al. 2006, Goodyear 2000).
The Disaster Risk Indexing (DRI) program of the UNDP in partnership with UNEP-GRID (UNDP 2004) uses two measures of human vulnerability, which are 1) the relative vulnerability calculated as the ratio between mortality and population exposed to a hazard; 2) a step-wise multiple regression of disaster mortality as the independent variable and a set of socio-economic dependent variables including economic status, economic activities, environmental quality, demography, health and sanitation, education and human development. On the other hand the Hotspots indexing project implemented by Columbia University and the World Bank under the umbrella of the ProVention Consortium, represented vulnerability by the historical disaster mortality and economic losses resulting from each hazard type (Dilley et al. 2005). Finally the Americas Programme of IDEA in partnership with the InterAmerican Development Bank (IDB) generated four indicators among which, the Disaster Deficit Index (DDI) vulnerability, function of a country's financial exposure to disaster loss and resiliency; and the LDI vulnerability, which is the proneness of a country to significant disasters and their cumulative effects.
Some issues that are worth consideration in these proposed models are, first of all, the access to reliable and consistent information about past events and their correlated casualties. We reiterate that this information is not always collected and in our case simply does not exist. Therefore the probability that the current area of study will be harmed can not be assessed. The second issue is one of scale. Some of the models can be applicable at national or regional scale but not at the small communities scale for which macro-economic or social indicators are not available. Furthermore, including an important number of independent variables in multi-regression analysis results in increasing the level of complexity of these models (Schmidt-Thomé 2006), yet these variables may be inter-correlated - an issue that is likely to overemphasize the model.
Several studies of vulnerability assessment were realized in Haiti, with the purpose to evaluate human and structural vulnerability to natural and human-induced disasters, one at national level, one at departmental level, another one at communal level, and the last one in selected sites (CDERA 2003). However, none has paid attention to environmental health issues, and none was focused on this specific area with very high population density.
The current study evaluates vulnerability at fine geographical scale and integrates physical exposure such as proximity of dense populations to potential sources of hazards. It is recognized that poor communities are more likely to occupy hazardous locations and are forced to use inadequate materials to build their houses, which adds up to their vulnerability (UNDP 2005, Mathee 2002, WHO 2001).
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