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So far, we have looked at signals, but information regarding the processes themselves can affect how we should interpret the signal. For example, areas of low crime can be more susceptible to false signals, whereas signals in high-crime areas may require more attention. By displaying process information, we can show the number of processes in the data, as well as the variability.
Below, each tile on our map contains a mini-SPC chart showing only the processes as rectangles, centred on the mean, and with the height of the rectangle sized by the control limits. This allows us to see the number of processes and amount of variability within a neighbourhood. It does not, however, allow us to make any comparisons between neighbourhoods.
We can further enhance this design by adding information and an encoding to show the mean of the processes. A vertical line between processes emphasizes the change.
To enable us to compare processes across neighbourhoods, we can use a global measure of variance. So now we can show how much variation exists between the control limits and the mean. The disadvantage of this approach is that it places greater emphasis on neighbourhoods with low crime levels.
Another way in which we can make comparisons between neighbourhoods is to set each mini-SPC chart to have the same minimum and maximum values on the y-axis. Not only can we now compare the amount of variability between neighbourhoods, we can also see the amount of crime a neighbourhood has compared to its neighbours. In contrast to the visualisation above, this method now places greater emphasis on neighbourhoods with high crime.
Finally, rather than showing crime frequency, we can show crime rates based on neighbourhood population. However, this places vgreat emphasis on city centres, where resident population counts are low but daytime populartion is high and so are crime levels. Therefore, without daytime and nighttime population data, crime frequency may be a more informative measure to visualise.
Now that we have established that we're going to look at crime frequency with a global y-axis, we can think about reintroducing NPU boundary data. One approach is to colour each process by its NPU, this way we can even remove the background and leave a faint outline.
The issue with the above approach is that it reinforces 'artificial' NPU boundaries, such that if a spatial association exists between neighbourhoods, the colour may inhibit its detection if it occurs across multiple NPUs. To mitigate this issue, we faintly colour the background by NPU. Using this method, the processes still dominate the attention, but the NPU colouring acts as a passive indicator.
Which of these two charts enables you to identify clusters of neighbourhoods in which processes are varying meanigfully in either consistent or inconsistent ways?
Which enables you to do so while considering the NPUs?
This concludes our investigation into Summarising Processes Geographically.
Our next step is to combine the signal and process information to create a rich overview for each neighbourhood Combining Multiple Representations.