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 results of this modeling exercise are divided into three sections: the first section presents the summary results of the model as a whole along with the different aggregation and classification schemes; then statistics are shown for the multiple factors that make up the model; finally, the effect of changing one assumption about the model is analyzed.
Participants in the Expert Opinion survey ranked waste as the major source of pollution (0.14) above traffic, markets and housing density, which equally came second with a weight of 13%. A lesser weight was allocated to cemetery, hospitals, and high voltage, which accounted for 8%. Indeed, the largest gap between the factors' weight was at most 6%.
Irrespective of the combination or the classification technique used, about 41% of the study area was found exposed to high and very high environmental health risks, all factors considered. This result was brought out by simultaneously averaging the Expert Opinion survey, the equal influence, and the personalized combination schemes on one hand, the quintile, the natural breaks, the equal interval, and the geometric interval techniques on the other hand, so that this number is freed of the influence of any single combination method or classification technique. The averaged results are displayed in the table below. A more complete set of results is provided in Table 16 in Appendix A.
The standardized classification scheme, which terms as a control (Table 10), led to a far different result with no areas exposed at very high risk and at most 15.9% of areas with high risks. In both cases the largest percentage was found in the category moderate risks, which amounts up to 59.3% in the standardized classification versus 36.5% in the first case. This perceptible discrepancy brings forward how the analysis built on one or another technique may lead to erroneous conclusions.
Comparing the three methods used to combine the multiple factors, EOW and the personalized approach provided very similar results displaying a maximum difference of 0.7% for areas at very high risks (Figure 4). Whereas the gap between the EOW and the personalized weighting was minimal, a greater percentage (maximum 4.3%), discriminated them from the equal influence technique. The equal weighting technique was prone to assign more cells in low and moderate levels and consequently promoted the influence of low values over high values than did the two other techniques. Despite this difference the general trend showed a larger percentage of areas at moderate and low risk irrespective of the classification technique utilized, as it appears on the figure below. What may explain this?
Average percent of areas by combination scheme
Most of the parameters did not have a spatial domain covering the entire area. The average area extent for all the parameters was 52%, with only housing density having 100% assignment. For a given factor, areas with no risk participating in the combination reflect on the result. Large scores (values greater than 3) can be obtained only if the largest possible number of cells participating in the combination contributes with a maximum score. In other words, the various factors considered are dispersed over the area of study with few cases of high value overlap. Since the equal weighting method does not discriminate the variables, the final grid calculation was heavily affected by these zeros. This fact is evident in the standardized classification technique which established the classes based on discrete values at fixed interval. The category 4 (values ranging from 3 to 4) was virtually absent in all the combination schemes.
The map displayed on Figure 5 results from the EOW combination using the geometric interval reclassification technique. It indicates a concentration of high risk areas in the downtown area and heading toward the coast. Conversely, areas at moderate and low risks are located away from the downtown toward the periphery mostly made of high slopes. This observation confirms the assertion that high population density associated to untidy urbanization in a context of weak institutional framework is mainly responsible for the daily occurrence of life-threatening hazards.
9Impact, le film from Onalukusu Luambo on Vimeo.