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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