IEEE Geoscience and Remote Sensing Magazine - September 2017 - 30

Requirements Covered (%)

100
90

80
70

60
A

Current State

40
30

100 200 300 400 500 600 700 800
Infrastructure Improvement (h)
(a)
Frequency

Requirements Covered (%)

100
95

90
85
B

80
75

70
65

Current State

60
55

50
100
150
200
Infrastructure Improvement (h)
(b)

250

Horizontal Coverage

100
Requirements Covered (%)

in the infrastructure brings eight times more benefit than it
would at point C. The same type of reasoning can be applied
to the cases labeled A, B, C, and D in Figure 6(b).
Figure 6(c) presents another interesting case. In the
region between points A and B, there is a possibility of
incremental improvement of the horizontal coverage, resulting in sustainable growth of covered requirements.
On the other hand, after coverage equaling approximately
5,000 km2 is achieved, a substantial amount of investment
is needed to raise the requirement coverage percentage to
the next level.

Access

B
90
A

85
80
75

Current State
0

5,000
10,000
15,000
Infrastructure Improvement (km2)
(c)

20,000

FIGURE 6. Product requirement coverage as a function of

product parameter improvement: (a) dependence based on
the access parameter, (b) dependence based on the update
frequency parameter, and (c) dependence based on the horizontal
coverage parameter. The scales of the figures differ.

in which gaining a relatively small improvement in requirement coverage requires a substantial infrastructure improvement, such as in the case of the transition from point
C to point D. If we consider the cost of the infrastructure improvement to be linearly dependent on the amount of improvement needed, then at point A, 30 times less investment
30

OUCIS BREAKDOWN TO MAIN ELEMENTS
Here, we evaluate the scores presented previously in
more detail. For that purpose, let us first define the
main variables that play important roles in the final
use-case scores. The NS is calculated as defined in the
"Overall Use Case Interest Score" section. The fraction
of users score (FU) is related to the need and is calculated by dividing the number of users mapped to the
need to the overall number of users. This number complements the NS. The SS is also calculated as defined
in the "Overall Use Case Interest Score" section. The
fraction of products in the upper 25% of score values
(FPU) shows the fraction of products that score higher
than 75% of the highest score value within the portfolio. The fraction of products in the lower 25% of score
values (FPL) is similar but accounts for the products
that score in the lowest 25% of the highest score value
within the portfolio. These two numbers together describe the gap distribution between products within
the same service portfolio.
The choice of the aforementioned variables was driven
by the building blocks of the OUCIS. First, the NS accompanied with the FU suggests the average importance of users who have the particular need. The next building block
is the SS, which depends on the product's performances
within its portfolio.
The variables FPU and FPL represent the distribution
of performance gaps within the portfolio. The larger the
value of the second term, the lower the service performance. Figure 7 presents information about the aforementioned variables for three examples of top-use cases
across all weighting schemes defined in the "Implementation" section. As the figure shows, use cases can obtain
high scores either when their need achieves a high score
or when the service does so. For example, in Figure 7(b)
and (c), the services have the same score, because the
use cases are both for marine applications, but the NSs
differ. Nevertheless, the fact that the SS is highest in
terms of the OUCIS value for each case puts them in the
topmost positions.
The radar diagram also provides insights into why the
SS is so high: half of the products in the marine portfolio
belong to the upper percentile of the scores, meaning they
perform poorly. Conversely, in the use case of Agriculture
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

SEPTEMBER 2017

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2017