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

( Télécharger le fichier original )
par Myrtho Joseph
University of Arizona - Master in Natural Resources Information System 1987

## 4.2 Comparison of the Classification Techniques

This section presents a comparison of the performance of the classification methods, namely quantile, natural breaks (Jenks), equal interval, and geometric interval, in distributing values among risk levels. Once again to attenuate the effect of the combination schemes, the scores of each classification were averaged for the 3 approaches. A summary of the results is displayed in Appendix A, Table 17, while Figure 7 facilitates the comparison of the distribution of the area among the four risk stages.

Figure 7: Environmental Health Risks in Port-au-Prince - Percent of area at-risk by classification technique

Two different patterns are exhibited regarding the distribution of risk levels. First, whereas the maximum gap among risk levels is very important for Natural Breaks and Equal Interval (21 and 41%), the Quantile and the Geometric Interval techniques show a maximum difference of 16%. The second pattern relates to the cluster of values in opposite categories (low and high). In this regard, though all the classification techniques have a higher proportion of values classified as low and moderate risk, equal interval and geometric interval place a smaller proportion of areas in high and very high risk (36%) than do Quantile and Natural Breaks (43% and 41%). The former techniques appear to be pessimistic while the latter is rather optimistic. The intent of this observation is not to generalize the comparison. This result might depend on the specific distribution of values and the way these values were classified beforehand. A visual comparison is offered in Figure 8 and Figure 9.

For this specific study, if the goal was to highlight the severity of environmental health risks, the Quantile or the Natural breaks methods would be appropriate. Conversely, Equal interval or Geometric interval would be suitable to provide a certain sense of environmental stability and improvement.

 Figure 8: Environmental Health Risks - Own weighting scheme using the quantile technique: greater proportion of high/very high risks Figure 9: Environmental Health Risks - Own weighting scheme using the geometric interval technique: smaller proportion of high/very high risks

These discussions and the analysis of the combination schemes made above illustrate the influence the choice of a specific model or technique can have on the final results. These results can be manipulated in a certain way to respond to decision makers' willingness to impress either about assumed progress accomplished by an implemented project or to attract funding for a prospective project by drawing a somber cliché of the state of the environment. As suggested by Osaragi (2002), the selection of a classification method depends on the nature of data and what we want to communicate about data.

## 4.3 Neighborhoods Exposed at High Risks

Another important step was to focus on areas identified as highly prone to environmental health hazards and assess their most salient characteristics. While we can't display here all the combinations for the different schemes, only the results obtained with the personalized weighting scheme coupled with the natural break (chosen randomly) are displayed in Figure 10.

Figure 10: Environmental Health Risks in Port-au-Prince: Percent of areas at-risk using the Own weighting scheme and the natural breaks classification method

Twenty two percent (22%) of areas was found overlapped for high and very high risk from one aggregation method to the other. That is, these areas always presented high risks irrespective of the approach used to combine the nine factors. This forms a space of certitude about the results. Most of these areas are located in the downtown neighborhood and include all the shanty towns. In particular these areas are well known for high traffic volume, high population density (so housing density), are located in neighborhoods of public facilities such as markets, hospitals, cemetery, in low elevation and near water bodies and the sea coast. These neighborhoods deserve the greatest attention from the government and/or the non-governmental agencies for interventions aimed at preventing or lessening impacts on health resulting from these identified hazards. The complete list of figures is provided in Appendix B, Figures 21-37. Through these maps the same geographic pattern can be seen for the different techniques considered. The differentiation, mainly quantitative, is not substantial.