At Science-Metrix we are obviously very focused on data—sweet, sweet data! We are also very aware that bibliometric data or pages and pages of analysis can be overwhelming and that more intuitive data presentation can help our clients to better understand their study results, which in turn helps them to take action on the findings we return to them. One graphic presentation we find particularly helpful is the positional analysis chart. Positional analysis is a way to visually depict two, three or even more indicators for a given set of entities instead of using a standard (and boring) table. Here’s how it works.
First, let’s pretend you have a table containing a list of countries doing research in nanotechnology, and each country possesses three attributes: the sum of its nanotech scientific articles, their citation impact (using the average of relative citations, or ARC) and a nanotech specialization index. I know you’ve seen a thousand tables just like this, all of which seem to be completely identical, featuring numerous lines, with many numbers per line. I can hear you yawning already!
So, how can we synthesize all this information in a simple visual? The backbone of a positional analysis is a 2D Cartesian coordinate system, where each axis represents an index or an indicator. By positioning a point on the coordinate system, you automatically assign two values to this point. Going back to our country table described above, let’s set the specialization index as the x-axis (horizontal) and the citation impact as the y-axis (vertical), with the world average of each index set at the origin (where the major axes meet).
Here is a simple example of how it might look (note, I’ve used fictitious data):
By positioning each country according to its values of citation impact and specialization index, it’s easy to identify which countries perform better than the world average on each of the indices. Countries are more impactful than the global average if they’re above the major horizontal axis, less impactful if they are below it. They are also more specialized than the global average if they’re to the right of the major vertical axis, less specialized than average if they are to the left. This makes it easy to rank countries at a glance with respect to each other: who’s above or below whom, and to the left or to the right of whom.
The observant among you will have noticed that there is another indicator represented in the chart above: it’s the number of articles produced by each country and it’s represented by the size (or area) of the country’s circle. In looking at this, we should note that size of production and specialization are separate but linked properties. A large bubble on the left side of the chart shows that the country’s production is large (demonstrated by the size of the bubble) but that the field is still not one of specialization for it (demonstrated by its position to the left of the centre line). Such a situation would indicate that the country is producing many papers in a field with a large publication output on the world stage—a relatively smaller drip in a huge pool. Conversely, a small bubble on the right side of the chart would denote a subfield in which the country is not producing many papers (demonstrated by the small bubble), but that this subfield is small at the world level, and therefore the country’s small output in this subfield is still above the global norm (demonstrated by the position of the bubble to the right of the centre line).
Reading the figure above, we can easily see that the largest number of nanotechnology articles are published by France (FR), the United States (US), Canada (CA) and Germany (DE). Among those four countries, France obtained the higher citation impact, while Germany obtained the lowest, being cited even less than the world average. (Recall that the data used are fictitious; I don’t mean to offend any country here!) In terms of specialization index (share of a country’s articles in nanotech over the share of the world’s articles in nanotech), our fictitious Canada and Germany are the most specialized within the four largest publishing countries mentioned above.
We could also consider a fourth indicator to be presented on the very same chart, one based on the colour of the circle. For example, shades of green and red could be used to denote the growth of papers in nanotech over a given period. The example above uses colour for aesthetic purposes only, but colour is one way to synthesize even more data within a single visual. We could even make separate positional analyses as annual snapshots, and then animate them as a GIF to show evolution over time, integrating a fifth dimension into our visual.
Positional analysis charts help to communicate a lot of information in very little time. They’re also excellent for highlighting the relationships between different attributes, and they are a very useful aid in strategic planning, through identifying the strengths and weaknesses of a given entity. I’ve demonstrated one example here using countries and some bibliometric indicators, but in fact a positional analysis can be used to represent all kinds of entities and all kinds of data, whether bibliometric or not.
If you’d like to see these charts applied to some real-life data, head over to our Twitter feed. Throughout 2018, we’ll be rolling out a Twitter series of positional analyses presenting the research performance of countries (USA, China) or groups of countries (EU, South America) across the fields of science.
Note: All views expressed are those of the individual author and are not necessarily those of Science-Metrix or 1science.