Choropleth Map of Change in Accident Rate

The choropleth approach is useful in visualising ordinal, ratio or interval data with geographic variation - why? Rates of road traffic accident are recorded by LSOA of accident location (as well as lat and longitude). The LSOA is a small unit of population, therefore there are benefits and  limitations to its use in choropleths. For small or middle sized metropolitan scale maps, they work well. Capturing (fine granular) data at a level interpretable visually on the small maps used in poster presentations. Furthermore, unlike large geographical units the map is not dominated by single regions, e.g. the U.S. or Russia on a World choropleth. At larger geographical map sizes, LSOAs can prove difficult to interpret due to the excess of detail. This is particularly true if the patterns of relationships are more subtle than rural versus urban.
(image - histogram of rates of change for use in choropleth map....
Change in accident rate was chosen as the (metric) because the aim was to emphasise the relative incidence of road traffic accident injuries in Swansea during the period of interest. A map plotting counts or relative rates would appear as below (include map). This is a useful image in identifying areas of high incidence and has been widely produced with the STATS19 data. It can however mask areas of improvement, or relative decline, that are useful in forming future road policy.
 Classification of data is an important part of  the creation of choropleth maps and can have a significant impact on their appearance. Examining the distribution of the data we can see that is it positively skewed, with over 60% falling into the lower bins, minor improvement or stability. In choosing classes of 1% there is a risk of losing intra-class discriminating details. This occurs as so many are within 1% of each other. Despite this risk, the 1% binning was chosen as it emphasises improvement versus deterioration. The method of selection based on quantiles would also have been a useful approach. Cutting the data up into a number of equal sized bins would better demonstrate differences that lay within the 1% bins. The result of this however, would have emphasised very small differences in classes, including binning small increases, stable LSOAs and small decreases into  single classes.
The use of a divergent colour scheme was also chosen in order to emphasise distance and direction of deviation from zero change. These changes should be interpreted with caution as they are often very based on small numbers of accidents per LSOA. The near uniformity of the direction of change, however, represents a visual analogue of the significant difference in accident rate across the entire region from 2009 to 2016 (see graphic - include bar chart of difference)
If space were not at a premium a good option would be to display adjacent choropleth maps. These would include accident rates in one, and the chosen change in rate in another.
An alternative means of displaying this data would have been a bivariate map displaying total accident rate and change in accident rate over the period. 

Future Work

References

1.  Cleveland and McGill. Graphical Perception: Theory, experimentation and applications to the development of graphical methods. JASA 79 (387): 531-554; 1984.
2. The Truthful Art: Data, Charts and Maps for Communication.