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Today is

Representing Signals Geographically

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We have used colour to represent a quantity (the population of an area) a characteristic of a place (the NPU). Here we use colour to show signals that occur at the most recent data point in our temporal sequence of crime data - which we will refer to as 'today'.

In the first visualisation below, we use a simple mapping to start with - red is any signal over the mean and blue is any signal under the mean. The data span from January 2011 to December 2016 and 'today' is currently set to .

You can move the date and one month at a time using these grey buttons.

We have also added some buttons at the bottom right that will persist as you scroll.

A tile map showing whether a signal has been triggered in a neighbourhood. Click on a neighbourhood to see its SPC chart.
The date is .

We can add information about the type of signal that occurred 'today' by varying the shape of symbols in the reporting areas in which signals occur.

Click the tiles in the tile map to see the SPC chart for any neighbourhood. Remember to use the and buttons to change the month that we consider to be 'today'.

Can you relate the symbols on the map to the signals detected 'today' through the SPC chart?
Can you find any geographic clusters?

A tile map showing the type of signal that has been triggered in a neighbourhood.
The date is .

Let's compare these methods, and the extent to which they are useful, in maps of different sizes. The fist graphic remains more clear when rendered at a smaller size when compared to the icon version. This is demonstrated in the visualisations below. The colouring on the right is visible at the second smallest size, whereas the icons only remain clear until the third smallest size. However, neither are clear at the smallest size.

This demonstrates how different methods of representing signals can be used depending on the size of the visualisation, something we will discuss later on when we wish to show many maps simultaneously.

Using colour (left), and colour and shape (right) to show information about signals in each reporting area in maps of different sizes. While the icons in the maps on the right convey more information, it's hard to detect the colours from the varied shapes, and difficult to discern the shapes in maps of smaller size.

We can also use a simple mark to represent signals. Here we use a horizontal line to signify no signal, and a line tilted up (positive) or down (negative) to signify a signal. We reinforce this with colour. One way in which we can take advantage of this approach is to remove the underlying map and show only the marks.

Here we represent each region purely as a line.
We use a discrete mapping for angle - a small angle represents eight over the mean, whereas a large angle represents one over three SD.
The date is .

Click the horizontal and angled lines in the map - try some coloured angled lines and some flat horizontal ones.
Can you relate these angled lines to the signals detected 'today' through the SPC chart?

Rather than representing a discreet value with the angle, perhaps we can convey further information by using line angles for numeric information such as difference between most recent data point and its predecessor. This continuous mapping allows us to see some subtle differences between regions that share the same signal. We can use this mark to further convey information for all neighbourhoods. If there is no signal, then we can show the difference between the value of the latest data point and the process mean. This allows the opportunity to see unexpected patterns in the data. Although for any neighbourhood without a signal, this could be considered noise caused by random fluctuations.

Here angle represents the difference between the process mean and the mean of the signal. For neighbourhoods without a signal, the angle represents the difference between the value of the latest data point and the process mean.
The date is .

We can add more information to the geographic representation of SPC (or SPC map) by representing the number of data points that exceed an defined signal length as the thickness of the line. So the line appears thicker if, for example, we have a sequence of nine months with reported crime levels above the mean (8 for the signal + 1 extra), or two months with levels over three standard deviations (1 for the signal + 1 extra). This allows us to see signals that continue to exhibit persistent negative or positive behaviour -- those that have not yet been addressed.

The same chart as above but with the addition of representing the number of points exceeding the sequence required to trigger the detected signal as line thickness.
The date is .

So far we have only visualised a signal that occurred 'today', but what if we want to show some historical aspect or trends in the data? We could use the regular spaces provided by our tile map to show tiny simplified SPC charts in each square. Showing all of the detail of the SPC charts is likely to make them difficult to read, so we need to develop designs that abstract the key information and make this readable on our tiles. We propose a 'trend grid' as an aggregated method of showing all of the signals in a chart in their geographic setting. We divide the time points into five bins (columns). If a signal is detected in a bin then a small box is drawn in that column. Its row is determined by the type of signal. Signals under the mean are drawn in the bottom half in blue, and signals above are drawn in the top half in red. The further away the signal is from the mean, the further away from the centre point it is drawn. If a bin contains more than one type of signal then we see multiple entries in one column.

A tile map of trend grids showing an aggregated view of all signals over time, in their spatial setting.
The date is .

Aligning signals vertically requires a lot of space and, therefore, leaves a lot of unused space. Another approach, rather than a grid, is to have two channels (positive and negative). We still bin the time points (our columns), but now we use the height of the channel to show the most 'severe' signal (the one furthest away from the mean), and use opacity to show when multiple signal types exist in a single column. Currently, each channel occupies half of a grid square, but we can reduce that - something that will become useful later on when we show multiple visualisations in the same grid square.

A tile map of trend channels showing an aggregated view of all signals over time, in their spatial setting.
The date is .

This concludes our look at design ideas for Representing Signals Geographically.

On the next page we will consider design options for Summarising Processes Geographically.