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Summarising Processes Geographically

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So far, we have looked at signals, but information regarding the processes 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 square shows 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, but does not allow us to make any comparisons between neighbourhoods.

A gapped chart showing the number of processes per neighbourhood. Click on a neighbourhood to see its SPC chart.

We can further enhance this figure by showing the mean of the processes, as well as adding a vertical line between processes to visually enforce the change.

A gapped chart showing the number of processes per neighbourhood, the mean is also displayed. Click on a neighbourhood to see its SPC chart.

To provide a relationship between processes, 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.

A gapped chart showing the number of processes per neighbourhood. Click on a neighbourhood to see its SPC chart.

Another way in which we can make comparisons between neighbourhoods is to set each mini-SPC 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.

A gapped chart showing the number of processes per neighbourhood with the y-axis globally aligned. Click on a neighbourhood to see its SPC chart.

Finally, rather than showing crime frequency, we can show crime rate (based on neighbourhood population). However, we believe this places too much emphasis on city centres, where census population counts are low but crime is high. Therefore, without daytime and nighttime population data, we believe crime frequency to be a more informative measure to visualise.

A gapped chart showing the number of processes per neighbourhood (based on crime rate) with the y-axis globally aligned. We exclude the NEC and airport from the analysis. Click on a neighbourhood to see its SPC chart.

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.

A gapped chart showing the number of processes per neighbourhood with the y-axis globally aligned, and coloured by NPU. Click on a neighbourhood to see its SPC chart.

The issue with the above approach is that it reenforces 'artificial' NPU boundaries, such that if some correlation exists between neighbourhoods, the colour may hinder the observation of such correlation if it exists across multiple NPUs. To mitigate this issue, we faintly colour the background by NPU. Using this method, we hypothesise that the processes still dominate the attention, but the NPU colouring acts as a passive indicator.

A gapped chart showing the number of processes per neighbourhood with the y-axis globally aligned, and with a background colour representing NPU. Click on a neighbourhood to see its SPC chart.

This concludes our investigation into processes. Our next step is to combine the signal and process information to create a rich overview for each neighbourhood.