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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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3.3 Data limitation

During the digitization, attributes were partially collected and put into the associated tables. In spite of our knowledge of the area, which we used to fill the gap of missing information on the topographic map, this task could not be completed. A field data collection would be necessary to correct this deficit. However due to time and resource limits, this was beyond the scope of this study. In addition to this, an independent data set would be required to validate the digitized features. In fact many errors of digitization inherent to human might have escaped the topologic validation but without compromising the data integrity. As noticed by Murphy (2005), digitizing contours... is a tedious and mistake ridden process. Nonetheless we don't feel that this significantly affected the results of this study.

Essentially, the biggest concern was the lack of data that restricted the insertion of some important factors in the model. The last population census was built upon the SDE unit and contains data about the number of people and other demographic characteristics. Nonetheless, the format of this data has not been made available to the public. We estimate that it is an important step toward comprehending the reality at micro-spatial scale and we strongly encourage researchers to adopt the SDE in future assessments. Finally, during the data collection process it was difficult, if not impossible, to discover any national government entity's website providing access even for purchasing spatial data. The consequence was a loss of much time and energy that could be allocated elsewhere.

3.4 Construction of the model

3.4.1 Process Overview

The choice of variables affecting environmental health hazards and vulnerability arises from the literature review, data available for the study area, and ground-specific reality that might not be in line with any known theory. Generally, tools such as buffer and Euclidean distance were applied to measure people's exposure level to the hazards considered. A raster structure was utilized to facilitate the integration of the multi variables through Boolean operations and overlay combination. Nine factors were included in the model and each was assigned a weight between 0 and 1, based on its relative importance in affecting health. Since we could not access any specific study providing weights for the study area different weighting approaches were brought out. The sub-variables contributing to the making of one factor, such as in the case of traffic pollution, waste pollution, and pollution from rivulets, were weighted on the basis of our own perception of their respective importance. Again this approach was used because of lack of support from the literature.

The entire modeling process was compiled, validated and run within the ArcMap Model Builder through multiple iterations. The model's outline and the script of its execution are provided in Appendix C and Appendix D.

To summarize, the model was generated in four main stages.

1) The first stage consisted of transforming the basic parameters into factors either by aggregating sub-variables or calculating distance where applied. The general form of this process is as follow:

Fi = w1*V1 + w2*V2 + ...+ wj*Vj or Fi = ?wj*Vj;

with Fi: Factor i; Vj: sub-variable j; wj: weight of sub-variable j, and w1+w2+...+wj = 1

2) Subsequently, grids with continuous values were standardized into discrete values from 1 to 4 using the geometric classification technique.

3) In the third stage, the factors were aggregated using WLC:

Environmental Health Risks (EHR) = ?Wi*Fi,

where Wi = weight of factor I, and W1+W2+...+Wi = 1

3) In the final stage the weighted sum of factors was reclassified into discrete values representing the four risk levels, using four different reclassification techniques.

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