Geoscience Reference
In-Depth Information
3.2.3 Economy
The next series of tests concern the economic data for each country: its
GDP per capita (PPP) and the structure of its economy. Gross Domestic
Product (GDP) is a measure of the total value of all goods and services
output during a single year within national borders and in this case, this
figure is divided by the total population. Purchasing-power parity (PPP) is
a rate of currency conversion that equalizes purchasing power by eliminat-
ing differences in price.
The results from the tests of association show that there is a significant
negative correlation between GDP per capita (PPP) and the number of
symbols devoted to the Natural Features class (
Table 5
). Additionally,
there are significant positive correlations between GDP per capita (PPP)
and the proportion of symbols devoted to the Paths and Tourism and Sport
Facilities classes (
Table 11
). As these two Level III classes are related to
leisure, it might be suggested that in wealthier countries, topographic maps
at this scale are more of a recreational product than a technical resource,
although no significant correlation was found between GDP per capita
(PPP) and the Level II class of Tourism, Recreation, and Conservation,
which is an aggregation of these two classes and others within the Level III
classification.
The structure of the economy is represented by a number of factors at
varying levels of detail. In addition to a comparison of individual sectors
of employment (such as mining and quarrying and electricity, gas, and
water supply) broader indicators such as the percentage employed in agri-
culture, industry, and services are included as well as figures for the total
labour force and population employed. The tests of association found a
variety of significant correlations between these data and the symbology of
topographic maps in the sample. Generally, there are highly significant
positive correlations between the number of people employed in the manu-
facturing and construction sectors and the proportion of symbols allocated
to the Human/Artificial Features class (
Table 8
). Among the other highly
significant results are the positive correlation between the number of
people employed in mining and quarrying and the number of Vegetation
symbols (
Table 9
); the negative correlation between those employed in
electricity, gas, and water supply sector and the number of General Built-
correlations between various sectors and the proportion of symbols
devoted to the Canals class (e.g.,
Table 10
).
