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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