International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
Figure 4. This is the most extreme example of an outlying
point’s effect being carried throughout the aggregation process
encountered for any of the countries analysed and highlights
how points can shift in the plot space according to whether an
economically overactive area is aggregated with points of
average economic activity or below average activity.
4.1 Characteristics of Outlying points
For a few of the countries tested in the analysis of European
countries, there is a number of points which deviated from the
derived regression line. These points invariably represent
heavily urbanised areas, usually a capital city or its hinterland.
The scatterplot shown in Figure 4 is a visualisation of how the
total amount of radiance in a NUTS region is related to its
GDP output on a national scale. It is a more sophisticated
measure than a simple lit-area versus GDP plot since pixels of
equal areas can have very different radiances. It was hoped that
this feature might help to reduce the magnitude of outlying
regions but it appears that this is still not sufficient for some
countries.
11 EU countries were analysed in a similar manner to that of
Spain. Certain countries exhibit extreme outliers, where
points on the graph lies well above the main trendline. They
are not the most radiant administrative areas but they are the
most economically productive. France and Denmark both had
these types of scatterplots. There is a number of similarities
between the French and Danish outliers. Firstly both Paris and
Copenhagen city centres are themselves NUTS regions. They
are of the order of 100 km“, when the average NUTS-3 region
is 5700km? in France and 2800 km? in Denmark. The Ile-de-
France region around Paris is also a NUTS-1 region which is a
fraction of the size of its sister NUTS-1 regions in France. It is
highly urbanised and has a high proportion of higher radiance
pixels. The Ile-de-France region is still an obvious outlier
even after aggregation. However, if it is further combined with
the much larger annular Bassin Parisien region, it begins to
align with the other NUTS-1 regions. If a single scale
independent relationship exists however, then it shouldn’t
matter what spatial units are employed to construct the
relationship.
The difference in the relative size of NUTS regions is an
important issue. Contrasting Spain’s NUTS-3 boundaries with
that of France or Denmark, Spanish areas are reasonably large
and uniform in size — even for Madrid and Barcelona. It begs
the question whether a correlation is only as good as the spatial
units it is based on. Would Barcelona’s NUTS-3 point be an
outlier of the magnitude of Paris or Brussels, if it had a small
administrative boundary in the centre of the city? If so, then
the disaggregation will underestimate the centre of the city and
overestimate the surrounding area by virtue of not having an
appropriate spatial unit to describe its radiance relationship to
economic activity. The same can be said for Milan or Rome,
which also have large NUTS-3 areas when compared to the
concentrated zones of Paris or Copenhagen. Whilst the
problem can be “aggregated away”, the application of a
‘macro-relationship’ to create a disaggregated map would be a
misrepresentation of the finer points of the data.
Examining the relationship between total radiance and
different sectors of the economy (Figure 5), it is clear to see
that different sectors of the make vastly differing contributions
794
to an area. In this example from the Italy, it can be seen that
the services sector has a far higher gradient with total radiance
than for agriculture.
140000 4
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Total Radiance (W.cm^-2.um^-1.sr^-1)
Figure 5. Total Radiance versus GRP by economic sector for
the Italy at NUTS-2 level.
A single point in the total radrance-GRP relationship shown for
Spain in Figure 4 is the combination of each set of points for
the three economic sectors. Most regions have an even mix of
each type of economic sector within a NUTS region, however
very small urban NUTS areas such as Paris or Copenhagen are
exclusively urban and the service sector relationship dominates
the economic make-up of the area generating an outlying point
in the scatterplot.
Since outlying points in the scatterplot are smaller in size as
well as highly urbanised NUTS regions, two additional metrics
may be identified as being potentially helpful to discriminate
these areas. Firstly, the mean radiance of a region could be a
useful feature to discriminate those areas, which, while having
equal total radiance, have different GRP. Secondly, the
proportion of area lit in a NUTS region can give an indication
of how urbanised that region is and therefore an inference can
be made as to which sector of the economy is contributing to
its GDP. The fact that different economic sectors have
different GRP per unit radiance values suggests that a priori
knowledge of these areas (from a land use map for example)
could be used as weighting functions for a region.
5. MAPPING RESULTS FROM RADIANCE-
CALIBRATED DATA
What implications do these observations have for the practical
mapping of economic activity? One key consideration is how
fár it is prudent to extend the relationship beyond the known
data range to finer spatial resolutions (The Ecological Fallacy).
Reconciling what the map aimed to display and the technical
specifications of the DMSP-OLS sensor affected the choice of
spatial resolution for the map. Whilst the relationship between
total radiance and GRP has been demonstrated to be consistent
from the NUTS-3 level up to NUTS-1, an unknown factor is
how far the relationship can be extrapolated in the other
direction (NUTS-3 to finer scales) before it becomes invalid.
In addition to this unquantified micro-scale relationship, there
is a desire to maintain an aggregate nature to the GDP figures
in the map. Figure 5 shows that different relationships exist
between the different economic sectors and total night-time
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