Importantly, it improves the decisions they make, nationally and locally, governments and citizens. Weather is by no means a unique example. Micro-level data, digital twins and massive-scale simulations are being used to better understand all manner of other systems at all manner of resolutions – from the sub-atomic scale when seeking the Higgs-Boson, to the global scale when modelling flows across oceans, the world wide web and galaxies. All have benefitted from drawing on mass micro-level data, crowd-  sourced, centrally-stitched, micro-to-macro. Until recently, fewer such high-resolution data existed when it came to tracking flows of goods and services, people and money through our economy. That is changing. These data increasingly exist in companies, whose management systems capture activity in close to real time. They exist through now-ubiquitous  sensor, camera and satellite technology. And they exist in governments, local and central, in the form of administrative data on people, goods and money flows. Increasingly, these data are being put to use to understand the economy.40 In the UK, the Office for National Statistics recently started publishing a range of high frequency economic indicators, based on such sources as administrative data on VAT returns and geospatial data on traffic flows. The Bank has made increasing use of these sorts of data in its own real-time assessment of the economy. Let me give a couple of examples. One topic attracting attention recently has been the economic consequences of a disruptive Brexit.
Patterns in these systems often self-replicate at different resolutions; they are “fractal”. 7 One interesting question is whether economic systems also exhibit self-replicating patterns. If so, this has implications for how we understand and model them. We consider these questions using a sequence of maps. Many of these maps are not new. For example, Philip McCann from the University’s Productivity Insights Network (PIN) has published an excellent book setting out a range of fascinating regional facts and maps. So too have others.8 These maps provide useful context when testing the Tip O’Neill hypothesis and when modelling and managing the economy. To provide the clearest visual guide, these maps are scaled by economic rather than geographic size – so-called cartograms.9 Each region is initially scaled to have the same geographic area and then rescaled in line with economic differences. This means some areas shrink (such as Scotland) while others expand (such as London) even without differences between them. This helps when visualising local differences. (a)  Differences between regions  To fix ideas, Charts 1a and 1b plot a map and an accompanying cartogram of population by region in the UK and, by way of comparison, Germany. It uses a resolution one-level below the national – administrative “regions” in England and each of Scotland, Wales and Northern Ireland. Comparing the map and cartogram suggests the UK has a significant demographic skew towards London and the South-East.