Representing economic activity using pictograms
Visualization can produce significant insights when applied to quantitative data. It is currently undergoing a renaissance that mirrors other developments in computing and data science. Sophisticated open source libraries such as d3.js or matplotlib, to name but a couple, are enabling an ever wider range of users to distill valuable information from the avalanche of data being produced.
Yet when it comes to visualizing data relating abstract concepts it can be quite difficult to find an appropriate grammar to express the related quantitative context. A classic example is the depiction of economic activities. Statistical agencies collect enormous amounts of information on the diverse number of activities comprising the modern economy. Over time it has been necessary to develop formal taxonomies that aim to group activities by common characteristics. One major such effort is the NACE system used in the European Union
NACE stands for “Nomenclature statistique des activités économiques dans la Communauté européenne”, that is, the Statistical classification of economic activities in the European Community. It is a four-digit classification providing the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics (e.g. production, employment and national accounts) and in other statistical domains developed within the European statistical system (ESS)
How could we represent economic activities visually? For example what fraction of GDP is related to each type? One solution is to adopt the used of pictograms.
An open source collection of pictograms representing economic activity as classified by the NACE system
We have developed a catalog of 21 pictograms that correspond to the highest level classifications (21 in total), coded also by letters A through U in NACE Rev. 2. Each activity is represented by a two pictograms that differ only in the shape of their perimeter (square or circular).
The collection is released under a Creative Commons Commercial, Attribution, Share-alike license. It is available for download and use in the usual place.
Let us know of any good usage examples or suggestions and watch this space for examples using the collection for credit portfolio management applications!