Environmental Engineering Reference
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dustrial activities can be considered to be the main factor affecting electricity con-
sumption in the central-southern portion of the country.
There is a distinct contrast between the clusters of high electricity consuming
provinces in the west and the low electricity consuming provinces in the east.
This contrast is marked by the artificial dividing line discussed in subsection
14.3.3. It might be concluded, therefore, that the nature of the spatial pattern of
electricity consumption is a clustering directly related to industry type (energy in-
tensive, high scale or services) and population. In other words, the determinants of
the observed spatial pattern in Turkey are the type of commercial activity and the
population size (see subsection 14.3.3).
Fig.14.7. Sectoral electricity consumption (kWh) in 2006
14.4 Exploration
Visualisation allows decision makers to grasp the spatial nature of the phenomena
under investigation. Exploring spatial data, though, provides better insight into the
data for identifying spatial patterns and relationships. Exploratory spatial data
analysis methods were implemented to provide an in depth investigation of the
spatial patterns of, and correlations between, electricity consumption and supply.
These methods are also useful to check the validity of the conclusions derived
from the visualisations shown in section 14.3. Using exploration techniques pro-
vides increased objectivity in the analyses, which can then generally be used with
higher confidence as a support tool for decision making during policymaking and
management processes. Exploration of spatial data has two basic components: ex-
ploration of first order effects and exploration of second order effects. The first
order effects relate to global trends in the data, while second order effects refer to
spatial correlations. In this study, Kernel Density Estimation, which is detailed in
subsection 14.4.1, is used for the first order exploration. As an alternative to Ker-
 
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