Visualisation of Prediction Modeling

This section presents the evaluation of the Visualisation module. The evaluation was conducted by considering different types of probability map sequences: (i) monthly probability map sequences and (ii) yearly probability map sequences. The first is

From/To 127 128 147 148 149 168 169 187 127 0.04 0.02 0 0 0 0 0 0 128 0 0.05 0 0.02 0 0 0 0 147 0 0 0.03 0 0 0 0 0 148 0 0 0 0.06 0 0 0 0 149 0 0 0 0 0.04 0 0 0 168 0 0 0 0 0 0.06 0 0 169 0 0 0 0 0 0 0.03 0 187 0 0 0 0 0 0 0 0.02

Table 6.5: January 2004 Type 2 Percolation Matrix indicating the probability of an event “percolating” from one location area to another innstep

considered in Sub-section 6.5.3.1 and the second in Sub-section 6.5.3.2. The selected results presented in Sub-sections 6.5.3.1 are from between January and May 2003 for CTS Type 1, Type 2 and Type 3 combination patterns. Whereas in Sub-section 6.5.3.2, the results presented are for January 2003, 2004, 2005 and 2006. The rest of the probability maps for CTS Type 1 and Type 2 between February and December 2003, 2004, 2005 and 2006 can be found in Appendix A to H.

6.5.3.1 Evaluation of Monthly Prediction Modeling

So as to evaluate the analytical support provided by the monthly probability maps all three identified types of combination pattern were considered in turn.

Type 1 CTS combination pattern ({Sender Area, Animal Age =

all Animal Age sub patterns, Breed T ype=all Breed T ype sub patterns,

N umber Animal M oved=all N umber Animal M oved sub patterns, Receiver Area}) were considered first. Figures 6.6, 6.7, 6.8, 6.9 and 6.10 show the probability maps generated for a sequence of five time stamps covering the period from January to May 2003. Note that, in all of the probability maps Lf romLocation and LtoLocation values

are used as the node labels. The links describe Type 1 movement patterns. From the maps it can be noted that most nodes in the maps have a probability of 0.01 that movement will occur within the same node. The maps include a number of “islands” of nodes that are connected together. The majority of these islands, in all the probability maps, comprise the same node patterns. Nevertheless there are a few new nodes that appeared or disappeared as the sequence progresses. In some case islands split into new smaller islands in a following month in the sequence. For example, the island comprised of location areas (nodes) {207,226,227,245,246} in the maps for January, February and April 2003 was divided into two in March and May 2003. From the maps it can also be seen that there is a possibility that an event might originate from within several nodes that link to some receiver nodes, as in the case of location area 47 in Figure 6.6.

Figure 6.6: January 2003 Type 1 Combination Patterns Probability Map There were also indirect connections between nodes, such as between nodes 207 and 245 in the January, February and April 2003 maps. Thus, considering Figure 6.6, if an event happens in location area 245, it could originate either from location area 227 in aone step percolation (with probability of 0.01) or from location area 207 intwo steps percolation, with probability of 0.0001 (0.01×0.01 = 0.0001). In terms of the identified islands if there is an event, for example an animal infection, it can be predicted that it is likely to spread within the detected islands of areas, but less likely to spread outside the islands.

The percolation matrices for CTS Type 2 combination patterns ({Sender Area, N umber Animal M oved ≤ 5, Receiver Area}) were also used to generate probability maps. Figures 6.11, 6.12, 6.13, 6.14 and 6.15 show the probability maps for between January and May 2003. Again, most of the movement patterns travel within the same location areas as the majority of nodes have self-links. In addition, in all the maps, only a few links existed between the adjacent areas. Inspection of all maps indicates that there is consistent link traffic from the location

Figure 6.7: February 2003 Type 1 Combination Patterns Probability Map area (node) 127 to the location area (node) 128, with probabilities of between 0.05 and 0.06 respectively. Figures 6.11 and 6.12 also show link traffic from location area 48 to location area 47 with a probability of 0.02. From Figure 6.15 it can also be deduced that cattle movements may occur within three location areas. There is a possibility that if an event, such as disease spread, happens in location area 148, that it might originate from location area 127 or 128. Even though location area 127 is not connected directly to 148, but it is connected in terms of atwo steps percolation with a probability of 0.001.

Further evaluation was conducted using Type 3 combination patterns ({Sender Area, Breed T ype=Luing, N umber Animal M oved ≤5, Receiver Area}). The Type 3 combination pattern had more criteria with which to filter the CTS fre- quent patterns, thus its usage resulted a smaller number of frequent patterns than in the case of Type 1 and Type 2 combination patterns. Figures 6.16, 6.17, 6.18 and 6.19 showed the probability maps generated for the period from January to April 2003. In-

Figure 6.8: March 2003 Type 3 Combination Patterns Probability Map

spection of the maps indicates that in this case all the cattle movements happen within the same nodes (location areas). Therefore, it is easy to identify which location areas have cattle movements with Breed=Luing (the Luing is a relatively rare beef cattle bread, it is very hardy and usually found in upland areas).

As mentioned above a Google Earth tool was included in the framework to relate areas to geographical locations. Most of the example probability maps presented above have similar node patterns, thus for evaluation of the Google Earth tool only the CTS Type 1 combination patterns between January and February 2003 are considered here. To produce the desired map the identified probability map needed to be first translated into a KML file, the result is as shown Figure 6.20. From the figure the relevant location areas in relation to the overall geography of GB can be clearly identified, as can the likely traffic flows between areas (indicated by red edges in the figure). The figure also indicate the areas within GB where significant cattle farming activities are concentrated. Discussion with domain experts has highlighted the usefulness of this

Figure 6.9: April 2003 Type 1 Combination Patterns Probability Map display.

6.5.3.2 Evaluation of Yearly Prediction Modeling

The Percolation Matrix module produces a sequence of percolation matrices which can be visualized in the form of prediction maps. The mechanism can also be applied to address “longitudinal” studies. Thus given a sequence of episodes (recall that we divide our time stamps into episodes) we can compare time stampi in episode j with

time stampi in episode j+ 1, and so on. The evaluation of longitudinal studies of

the form reported in this chapter is again focused on the CTS data. The reported comparisons were made using Type 1 and Type 2 combination patterns. Figure 6.6, 6.21, 6.22 and 6.23 show the cattle movements for the month of January with respect to four consecutive episodes (2003, 2004, 2005, 2006) using the Type 1 combination patterns ({Sender Area, Animal Age = allAnimal Age sub patterns, Breed =

all Breed T ype sub patterns, N umber Animal M oved =

all N umber Animal M oved sub patterns, Receiver Area}). Again several is- lands can be observed in the maps to show how nodes in areas are connected directly

Figure 6.10: May 2003 Type 1 Combination Patterns Probability Map

(one step) and also indirectly (two steps). However, the yearly maps show slightly different types of islands, in terms of the number of areas and also the direction of links between them, demonstrating how the islands change over a number of years. Inspection of Figure 6.21 indicates that in January 2004 there are more “complex” connections between nodes. For example events within location area 127 could reach location area 149 following two separate routes (via node 148, or via nodes 128 and 129), the first with a probability of 0.0002, the second with a probability of 0.000006.

Similarly, Figures 6.11, 6.24, 6.25 and 6.26 show yearly comparisons using Type 2 combination patterns {Sender Area, Number Animal Moved ≤ 5, Receiver Area} for the month of January. Inspection of the figures shows that all the maps hold similar results; most of the cattle movements happened within the same nodes. Nevertheless, there are a few pairs of nodes that are connected byone step percolations.

Only the January 2004 map (Figure 6.24) features a two step link. There were movements that happen from within location area 127 to location area 148 with a

Figure 6.11: January 2003 Type 2 Combination Patterns Probability Map

Figure 6.12: February 2003 Type 2 Combination Patterns Probability Map probability of 0.0004 (0.02×0.02). Unlike the other islands on the map, if there is an event that happens within location 47 this may be caused by an event either in the location area 46 (with a probability of 0.02) or the location area 48 (with a probability of 0.02) or both with a probability of 0.0004.