Visualising subsequent judicial consideration: Part One

Visualisation of legal information is gathering momentum, albeit in baby steps. Without a doubt, Justis have been responsible for the pushing the envelope further than anyone else in this respect (see their Precedent Map in JustCite and text heat mapping in JustisOne).

Text visualisation tools almost certainly have a bigger role to play in the provision of online legal information. The challenge is to make sure that they are genuinely relevant and that the trends they describe are accurate. 

One facet of legal information that is ripe for useful visualisation techniques is the mapping of how a case has been treated by subsequent cases. The Justis precedent map runs along these lines: the case under analysis appears as the central node in a sort of clock dial. The nodes in the left hemisphere of the dial are the cases that have been considered by the case at the central node. The nodes in the right hemisphere of the dial are that cases that have themselves considered the case at the central node. Colour-coding is also used to denote the class of consideration, e.g. positive, negative etc. 

There is a potential use-case in which a user might wish to view the "authoritative profile" of a particular decision in a way that is not neatly satisfied by the Justis precedent map or any other tool I'm aware of. Say, for example, I wanted to form a bird's eye view of a particular case's authority profile in order to help me make an assessment as to whether it is good authority for some line of argument I'm seeking to run. What I want to be able to see, at a glance, is the rough shape of how that case has been treated by later cases. With this in mind, I tried to develop an extremely rough and ready visualisation that could assist in this situation. 

Plotting instances of subsequent consideration on a radar chart

The solution may or may not be something that runs along the lines of the radar graph below. 

The graph works by plotting points on a radar chart that correspond to the frequency over time a case has received a particular class of subsequent consideration. The classes of consideration (e.g. applied, distinguished etc) that run around the chart area are based on the granular consideration matrix developed by The Incorporated Council of Law Reporting for England and Wales. The right hemisphere consists of "positive" treatment classes, the left hemisphere consists of "negative" treatment classes. 

The larger the area of shading in the right hemisphere, the more positively the case has subsequently been treated. Conversely, the greater the area of shading in the left hemisphere, the more negatively the case has been subsequently treated. 

Authority Profile D3.js Radar Chart

The data in the radar chart above is actually based on material subsequent judicial considerations on Chan Wing-Siu v The Queen [1985] AC 168. Fourteen cases materially considered Chan Wing-Siu (that is to say, fourteen cases engaged with the Chan Wing-Siu beyond giving it mere mention). 

As the chart shows, the vast bulk of subsequent consideration has been positive, with 64% of the subsequent cases applying Chan Wing-Siu. The chart does provide an attractive visual profile of the ways in which Chan Wing-Siu has been subsequently considered, but the picture it paints of the future authoritative value of the case is where the chart falls down. 

Problems with the radar model

There are, I suppose, are a number of ways in which this data model falls down. The major problem is that is does not account for the importance of the most recent class of consideration. As we know, Chan Wing-Siu is no longer good law for the test to be applied to assess whether a defendant had sufficient intent to establish murder or grievous bodily harm in the context of a joint enterprise. The Supreme Court disapproved of the Chan Wing-Siu test earlier this year in R v Jogee [2016] UKSC 8[2016] 2 WLR 681 earlier this. The radar model is therefore misleading, because that single instance of negative consideration was sufficient to destroy the Chan Wing-Siu case's future authoritative value.  

Addressing this problem with the model

One simple way of addressing this problem with the radar model might simply be to assign a signal to the most recent class of consideration. That way, the user is placed on notice about the most recent class of consideration the case under analysis received and is able to adjust their assessment of the case accordingly. This seems a little clunky.

The better way, perhaps, is to admit that whilst the radar model is effective insofar as it is able to graphically demonstrate the basic profile of the sorts of subsequent consideration a case has received, a more effective method might be provide an additional graphical representation of consideration over time. I'll set out a time-based graphical model in a later post. 

Building the radar model

The radar model featured in this blog post was written using the d3.js library. The code borrows heavily from the radar chart function written by Nadieh Bremer ( and the original code (free of my hacking) can be found at on the excellent website here