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A GIS-based modeling of environmental health risks in populated areas of Port-au-prince, Haiti

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par Myrtho Joseph
University of Arizona - Master in Natural Resources Information System 1987

précédent sommaire suivant Housing density

High density housing poses serious threats to human health by forming sinks for outdoor pollutants, which is aggravated by the fact that the houses are not well-ventilated (Ezatti et al. 2005, YTV 2007). Housing density also makes up the primary source of indoor pollution due to unsafe human excreta disposal, and household fuel consumption (Ezatti et al. 2005). Consequently these unsafe settlements with poor environmental quality expose their residents to social instability and communicable and non-communicable diseases, such as skin and eyes infections, diarrhea, respiratory infections and environmental hazards (WHO 2001a). Slums populate different locations of Port-au-Prince's sea coast, built on and in unstable, stinky and unhealthy waste, in the dust or in the mud. They are called «Brooklyn» or «Manhattan», using a supreme sense of the sarcasm (Wargny, 2004).

We acknowledge that housing density embodies both aspects of hazards and vulnerability. Though present as a vulnerability factor in almost all the variables considered, and acting as the main trigger of the different risks considered, it was suitable to reduce the redundancy that would result by adding it each time. We use the term risk for the reason stated above that housing density represents a factor of air pollution hazard and at the same time a vulnerability feature. That is, even if there is no other risk factor present in a location, high and very high housing density are deemed sufficient conditions for the existence of high risk in these places.

Housing density was originally based on a grid 0.5x0.5 kilometers in size, which classified density in 6 categories from null to extremely high (Table 4). Though it was unclear what theoretical background this categorization was based upon, it was found inappropriate to use it in this study. A first example of this inconvenience provided in Figure 2 indicates that extremely high density represents more than 72% of the area, including slums, commercial and residential neighborhoods. Another illustration is that the difference between average and very high density is only 6 houses per km2. Hence it was too general and would not allow us to discriminate neighborhoods with environmental patterns significant for health assessment. Then a grid of higher resolution (0.3x0.3 km, Figure 3) was generated. This was manually reclassified based on SDE count of housing published by IHSI and with the assistance of a live image from Google Earth, which clearly displayed the density difference. Table 5 provides a summary of risk levels associated with different thresholds of housing density.

Table 4: Housing Density classification in the original grid


Density Level


Null/Very Low








Very High

65 - 300

Extremely High

Figure 2: Housing Density as classified in the original grid 0.5x0.5 km

Figure 3: Housing Density after reclassification (grid size: 0.3x0.3 km)

Table 5: Housing Density and Risk Levels

Housing Density

Risk Levels

Low (1-2)

Low (1)

Medium (3)

Moderate (2)

High (4)

High (3)

Very High - Extreme (5-6)

Extreme (4)

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