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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
radiance. The overall derived relationship such as that shown
in Figure 4 is an aggregate of these sector combinations.
Keeping the map resolution sufficiently coarse avoids issues of
sector specific GDP representations at finer scales where the
aggregate relationship may not be valid. Although the night-
time data has a nominal resolution of 1km, this is resampled
from 2.7km raw data. This also supports a decision to increase
the spatial resolution of the map by reducing any noise effects
present in the fine resolution data.
Bearing these points in mind, the output resolution was set at
5km as this was reasoned to provide both a detailed map at the
continental scale whilst being coarse enough to generalise
economic activity to a level which shows enough detail in large
cities without compromising small towns. In addition to this is
the advantage of making the mapping process less
computationally intensive. Outlying areas identified for certain
countries were excluded from the main relationship of that
country. However, values were attributed to the radiance cells
in these areas by allocating the remaining value from the
national total to these areas thus ensuring that all of the cells
in a country sum to the published total. The map will over
estimate and under estimate certain areas. This is an inevitable
consequence of the method. Nonetheless it is encouraging to
see that very little adjustment to the value of outlier(s) was
required to constrain the map to a country total. Full details of
the results and maps are presented in Doll ef al. (2004)
6. CONCLUSION
Although, the method used to construct the economic activity
map from a given relationship has the inevitable consequence
of over- and underestimation, alternative aggregations of the
finest-scaled zones data did not appear to give substantially
different results compared to the administrative NUTS-1
aggregation from the original data. Estimating country level
GDP in the EU was found to be accurate to within 5% for most
countries and within 7% for all. For a given country-level
percentage error, the method used for the treatment of outliers
will yield an increasing amount of error between its predicted
and observed value as the outlier forms a decreasing
percentage of total GDP for that country.
This is a highly encouraging result when considering that the
map is based on just one variable and re-affirms night-time
light data as a promising tool for mapping socio-economic
parameters. Economic activity mapping could also benefit from
the inclusion of other variables as has been shown with respect
to human population mapping from the Landscan project. The
use of such data would facilitate the construction of *poverty
maps' where GDP/capita can be spatially mapped thereby
highlighting areas of social inequality. An example of this for
Guatemala can be found in Sutton et al. (2004). Indeed, the
application of these datasets are arguably of most use to the
developing world. The importance of other variables such as
land-use and transportation networks is likely to become more
acute as the spatial resolution of the map increases.
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