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These three examples show that it is possible to map out areas of good and bad access to specific targets, with an indication of the travel times involved in reaching them. It is also possible to combine any number of such maps to get an overview of the general quality of access to all services, which will give a good indication of general service deprivation in different locations. This can be done either by simple aggregation of the maps, or by using a weighted overlay function where some services, say hospitals, are deemed more important than others.
In the first case, the travel time maps are all reclassified to a common fixed interval ordinal range, as a summation of travel times to different services would be quite meaningless. In this case the total travels times are reclassed on a simple ordinal scale of 1- 5, representing best to worst. The three maps are then averaged to give a final output of average quality of access to all three services, ranging from best - worst (figure 13)
It may be the case that some services are considered of more importance than others. Access to hospitals is important to all members of society, whilst access to schools is only important to one portion. It is possible to aggregate the classified maps using a weighted overlay algorithm where each map is given an express weighting to specify its importance. Two examples are given here, first where hospitals are deemed most important (figure 14) and secondly where schools are given priority (figure 15). These are subjective determinations and must be treated with great caution.
These maps show differences, but the general distribution of access quality does not vary greatly between them. This means that in this case study, the distributions are not too sensitive to variations in these subjective choices. This would indicate that the model, whilst very simple, is also quite robust and so could be a useful tool for decision making.