ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 
3.3 Topological information on map: Voronoi neitfibours 
As has been discussed in the previous section, the construction 
of dual graph for map features is a difficult task because the vast 
majority of map features are disjoint. However, with the Vonoroi 
region, all features have been connected together to form a 
tessellation. The generation of dual graph for map features could 
be replaced by the dual graph of the Voronoi region of these 
features. This is illustrated in Figure 5. Figure 5(a) is the 
Voronoi region and Figure 5(b) is the corresponding dual graph. 
Figure 5 A Voronoi diagram and its dual graph 
Then, the entropy of this map can also be computed as did for 
the graphs in Figure 2. 
It has also been discussed before the entropy computed using 
the number of nodes in the graph is that of the distribution of 
different kind of vertices. It does not really reflect the complexity 
of dual graph directly. Indeed, sometimes, it is misleading, as 
shown in the case of Figure 2. Therefore, a new index needs to 
be designed. As the complexity of a dual graph can be indicated 
by the number of neighbours for each vertex, therefore, this 
number is a good measure already. In order to compare the 
complexity of dual graph with different vertices, the average 
number of neighbours for each vertex may be used as a value to 
indicate the complexity of a dual graph. 
M, 
n Nj 
In 
V 
K N U 
(13) 
Suppose there are in total N symbols on a map, the total amount 
of thematic information for this map is then 
H(TM) = jr H, (TM) (14) 
;=l 
4. A COMPARATIVE ANALYSIS THROUGH EXPERIMENTS 
In the previous section, a set of new measures has been 
proposed for the spatial information of a map. It is appropriate to 
conduct some experimental tests on the usefulness of these new 
measures and also to see whether these new measures are 
more meaningful than existing ones. 
4.1 Metric information vs statistical information 
The first test is on metric information. The two maps in Figure 1 
were used. The corresponding Voronoi regions are shown in 
Figure 3. The results for the entropy of Voronoi regions and the 
ratio between mean and standard are listed in Table 1. 
Table 1 Metric information of the two maps in Figure 1 
(The area of the map is a unit) 
H(M) 
Rm 
S E 
o 
Map in Figure 1 (a) 
4.2848 
80.51% 
0.025 
2.84% 
Map in Figure 1 (b) 
5.1260 
96.32% 
0.025 
1.51% 
From Table 1, it is clear that the map shown in Figure 1(b) 
contains higher metric information than that in Figure 1(a). 
Considering the fact that they should have the same amount of 
statistical information as pointed out in Section 2, it seems logic 
to claim that these measures is more appropriate than the 
statistical information. 
Let, N s be the sum of the numbers of neighbours for all vertices 
and N T the total number of vertices in a dual graph. Then, the 
average number of neighbour for each vertex is: 
3.4 Thematic information on map: Entropy of neighbour 
types 
Thematic information related to the thematic types of features. It 
is understandable that, if a symbol has all neighbours with the 
same thematic hype, then the importance of this symbol is very 
low, in terms of thematic meaning. In the other hand, if a symbol 
has neighbours with different thematic types, it should be 
regarded as having higher thematic information. [Here, the 
neighbours are also defined by the immediately-neighbouring 
Voronoi regions]-. Based on this assumption, the thematic 
information of a map symbol can then be defined. Suppose, for 
the i ,h map symbol, there are in total Ni neighbours and Mi types 
of thematic neighbours. There are in total nj neighbours for j ,h 
thematic type. Then the probability of the neighbours with f 
thematic type is as follows: 
p j=Y' = (12) 
The thematic information of the ith map symbol is then as 
follows: 
4.2 Topological information: new vs old 
The second test is on the topological information. Using the new 
index, the results for the two graphs in Figure 2 would be 
different. In Figure 2(a), there are seven vertices and the total 
number of neighbours for all vertices are twelve. The average 
number of neighbours for each vertex is 1.7. In Figure 2(b), 
there are seven vertices as well but the total number of 
neighbours for all vertices are fourteen. The average number of 
neighbour for each vertex is 2.0. It is then clear that Figure 2 (b) 
is more complex than Figure 2 (a). 
Table 2 The average number of neighbours for Figs. 3 and 5 
N T 
N s 
H T 
Figure 3(a) 
40 
206 
5.15 
Figure 3(b) 
40 
188 
4.70 
Figure 5 
13 
54 
4.15 
To further elaborate the adequacy of this new measure, the index 
value for the Voronoi regions shown in Figure 3 and 5 are also 
computed and listed in Table 2. It shows that the map shown in 
Figure 1(a) is more complex than that shown in Figure 1(b). This 
is because the three symbols are mixed into the building 
symbols.