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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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4.5 Sensitivity analysis

Though the comparison of the different aggregation schemes constitutes a sort of analysis of sensitivity, this section introduces a more in-depth consideration of this issue. To keep the comparison of the results as simple as possible, reference was made only to the Expert Opinion weighting using the geometric interval classification. The maps are displayed in Appendix B, Figures 38 to 40.

Traffic Pollution Influence

The assumption of an increase of traffic pollution's weight to 30% results in decreasing the coefficient of all the other parameters of the model while keeping the total weight to 1. Table 11 shows a substantial alteration of the percent area in each risk level. The most significant change was observed in the lower risk level which declined by 67%. At the opposite, high and very high risk levels increased by 50% and 77% respectively. The overall change is almost 60%. This reveals the strong influence traffic pollution has on health risk in the study area. To this regard, traffic pollution represents one of the most urgent issues that policies should address. Let's recall that traffic pollution occurs in about 76% of the study area.

Table 11: Increase in traffic pollution weight compared to EOW

 

EOW

Increase in

Traffic weight

Difference (%)

Absolute

Difference

Low

36.0

15.8

-56.1

-20.2

Moderate

27.1

24.0

-11.5

-3.1

High

19.3

29.0

50.2

9.7

Very High

17.7

31.3

76.8

13.6

Sum of changes

 
 

59.4

 

Waste Pollution Influence

The geographical incidence of waste represents 95% of the area. With the alteration of waste coefficient, the model records a decline in low classes of about 54% while high and very high risk levels indicate a positive joint change of 100%. The aggregate change is 45%, 15 points smaller than changes induced by traffic pollution, though the physical occurrence of waste is greater than traffic pollution (Table 12). Nevertheless, this influence on the model is still substantial.

Table 12: Increase in waste pollution weight compared to EOW

 

EOW

Increase in

waste weight

Difference (%)

Absolute

Difference

Low

36.0

19.1

-46.9

-16.9

Moderate

27.1

25.2

-7.0

-1.9

High

19.3

32.9

70.4

13.6

Very High

17.7

22.8

28.8

5.1

Sum of changes

 
 

45.3

 

4.5.3 Proportional Spatial Incidence of the factors

This weighting, brought out based on the relative physical extent of each model's parameters, assigned a greater coefficient to housing density (0.21), followed by waste (0.20), rivulets (0.19), and traffic pollution (0.16). Unlike the previous two scenarios, the area incidence of high and very high risk levels increased only by 60% and the aggregate change is less than 30% (Table 13). This weighting shrank the gap between risk levels and enables a better balanced distribution than did EOW.

Table 13: Comparison of EOW and Proportional Incidence Weighting Results

 

EOW

Proportional

Weighting

Difference (%)

Absolute

Difference

Low

36.0

23.8

-33.9

-12.2

Moderate

27.1

27.0

-0.3

-0.1

High

19.3

31.3

62.2

12

Very High

17.7

17.9

1.1

0.2

Sum of changes

 
 

29.1

 

To summarize, the alteration of some of the coefficients of the model's parameters influenced the results in different ways. The increase of the traffic pollution weighting had the greatest impact on the model, altering drastically the categories' rank from top to bottom. This exercise definitely illustrates the responsiveness of the model to changes of its parameters.

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9Impact, le film from Onalukusu Luambo on Vimeo.


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