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INTRODUCTION TO THE SPATIAL INTERTICLES THE DA
INTRODUCTION
INTRODUCTION TO THE SPATIAL INTERPOLATION COMPARISON (SIC) 2004 EXERCISE AND PRESENTATION OF THE DATASETS
The Spatial Interpolation Comparison (SIC) 2004 exercise was organised during the summer 2004 to assess the current know-how in the field of “automatic mapping”. The underlying idea was to explore the way algorithms designed for spatial interpolation can automatically generate maps on the basis of information collected regularly by monitoring networks. Participants to this exercise were invited to use some prior information to design their algorithms and to test them by applying the software code to two given datasets. Estimation errors were used to assess the relative performances of the algorithms proposed. Participants were not only invited to minimize estimation errors but also to design the algorithms so as to render them suitable for decision-support systems used in emergency situations. The data used in this exercise were daily mean values of gamma dose rates measured in Germany. This paper presents the exercise and the data used more in detail.
Figure 1 Description of the Spatial Interpolation Comparison (SIC) concept: participants are invited to minimize errors when using n observations (left, circles) to estimate values located at N locations (right, crosses). The left map shows the sampling locations of 200 training data and the right map shows 1008 point locations for which estimated values were requested
Figure 2 Frequency histograms for the first 10 training sets (from top to bottom, left to right, sets 1 to 10) described in Table 1. The smooth curve depicts the normal distribution.
=
SIC 2004 EXERCISE ARTICLES
09-5
Datasets: N = 1008 n = 200 Minimum distance (km) 1.6 5.0 Maximum distance (km) 21.0 53.0 Median distance (km) 10.7 16.0 Mean distance (km) 10.7 18.0 Standard deviation (km) 3.0 9.0 Coefficient of variation 0.3 0.5 Skewness 0.0 1.2 Clark & Evans’ test for complete spatial randomness 1.4 1.1 Table 1 Nearest-neighbour distances statistics for the SIC2004 datasets. Measurement units are km. Training sets (n = 200) Set 1
Min.
Max.
Mean
N-n = 808 1.6 23.5 11.3 11.3 3.2 0.3 0.2 1.3
Median
Std. dev.
Skewness
Kurtosis
55.8
150.0
97.6
98.0
19.1
0.0
-0.5
Set 2
55.9
155.0
97.4
97.9
19.3
0.1
-0.5
Set 3
59.9
157.0
98.8
100.0
18.5
0.1
-0.3
Set 4
56.1
152.0
93.8
94.8
16.8
0.2
0.0
Set 5
56.4
143.0
92.4
92.0
16.6
0.2
-0.2
Set 6
54.4
133.0
89.8
90.4
15.9
0.1
-0.5
Set 7
56.1
140.0
91.7
91.7
16.2
0.1
-0.4
Set 8
54.9
148.0
92.4
92.5
16.6
0.1
-0.1
Set 9
56.5
149.0
96.6
97.0
18.2
0.0
-0.4
Set 10
54.9
152.0
95.4
95.7
17.2
0.1
-0.2
Skewness
Kurtosis
Table 2 Statistics for the 10 datasets used to train the algorithm used in SIC2004. Measurement units are nSv/h. Complete sets (N = 1008) Set 1
Min.
Max.
Mean
Median
Std. dev.
55.0
193.0
98.9
99.5
21.1
0.4
0.7
Set 2
54.9
188.0
98.8
99.5
21.2
0.4
0.6
Set 3
59.9
192.0
100.3
101.0
20.4
0.5
0.7
Set 4
56.1
180.0
95.1
95.4
18.8
0.6
1.1
Set 5
56.1
168.0
93.7
94.0
18.1
0.5
0.6
Set 6
54.4
168.0
90.9
91.6
17.2
0.4
0.6
Set 7
56.1
166.0
92.5
92.9
16.9
0.4
0.4
Set 8
54.9
176.0
93.5
94.1
18.1
0.5
1.0
Set 9
56.5
183.0
97.8
98.7
19.9
0.4
0.6
Set 10
54.9
183.0
96.6
97.1
19.0
0.5
0.8
Table 3 Descriptive statistics for the full datasets from which the training data were extracted. Measurement units are nSv/h.