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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
  

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3.5 Model's Summary

Here are the four combination schemes employed to aggregate the environmental health risks factors considered in the model.

1) Expert Opinion Weighting (EOW)

EHR = 0.13(Traffic Pollution) + 0.14Waste + 0.08(cemetery + hospitals + High Voltage Power) + 0.13Markets + 0.13(Housing Density) + 0.12(Polluted water) + 0.11(Distance to the sea)

2) Own Ranking

EHR = 0.14(Traffic Pollution) + 0.16Waste + 0.06(Distance to Cemetery + Distance to the Hospitals) + 0.10*(Distance to market places) + 0.14(Housing Density) + 0.10(Pollution from Streams) + 0.16(Distance to the sea) + 0.08(High Voltage Power)

3) Equal Weight

EHR = 0.1111*(Traffic Pollution + Waste + Distance to Cemetery + Distance to the Hospitals + Distance to markets places + Housing Density +.Pollution from Streams + Distance to the sea + High Voltage Power)

4) Maximum output

EHR = Max(Traffic Pollution, Waste, Distance to Cemetery, Distance to the Hospitals, Distance to markets places, Housing Density, Pollution from Streams, Distance to the sea, High Voltage Power).

3.6 Sensitivity Analysis

The spatial model presented in this study is defined by a series of input factors, parameters, variables, weights, and combination techniques subject to many sources of uncertainty. These sources comprise lack of data, insufficient support of literature review for the study area, and error of measurement, and weight attribution, which impose limit on our confidence in the output model. The range of alternatives available in implementing the overlay provides the flexibility to come to any desired conclusion (O'Sullivan and Unwin 2003). Therefore, it is important to evaluate our confidence in the model and assess the uncertainties associated with the modeling process and with the output. A sensitivity analysis evaluates the effects of modifying one or several input parameters on the model output (Akçakaya 1996, Franklin et al. 2002) and examines the overall variability in the possible output (O'Sullivan and Unwin 2003). The weight of factors believed to be of greater magnitude in the model such as traffic and waste pollution was modified while adjusting the coefficient of the other variables to keep the total sum of the weights to 1. Three different scenarios were assessed. First, using the weights obtained from the expert opinion survey as basis, the coefficient of traffic pollution was increased from 0.13 to 0.3. Second, the weight of waste pollution was increased from 0.14 to 0.3. The last approach was to allocate weights in proportion to the area extent of each factor. We observed the change in proportion occurred between the four risk levels and compared the arithmetic sum of changes in the three cases to determine the sensitivity of the model.

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