MapCarte 138/365: Surveying illumination in public spaces by Spin Unit, 2014

MapCarte138_illuminationThe trend for dark backgrounds and base maps in modern web mapping is¬†widespread. There’s often a good reason since contrast between a base and the figural foreground is helped considerably when you use a flat, sparse background. This could be plain white or, conversely, dark grey or black. Given that most screens use an RGB additive colour model then the natural background is black (0,0,0). This is analogous to a traditional blackboard – with contrast being achieved through the use of white chalk. On screen or on a black background, the natural human perception of darker colours meaning ‘more’ is inverted…so as colour increases in lightness and tends towards white, we perceive more.

It’s a dramatic effect and often used with data that itself creates the shape of the geographical features being studied. The impetus for many of these types of image are NASA’s classic Earth at night image where human settlement is clearly picked up through the pattern of urban lighting across an otherwise dark backdrop. We clearly see the outlines of countries and other features.

What makes Spin Unit’s work here so mesmerising is that they haven’t merely represented a single phenomena with symbology that ranges from dark to light values to pick out the form of a street network. That would simply be the location of street lights, each one symbolised with a radial symbol with a light centre that fades to black or a linear feature representing a road with equal treatment along its length. ¬†Of course, light does not appear that way in reality. Instead, they go way beyond simply showing the position of street lights to define a cartography that measures light from the ground level, where people walk, drive and ride. They use statistical models to predict how public space is illuminated. It’s a predicted surface which reveals far more subtlety in shade and areas of dark and light that define lit and unlit neighbourhoods.


The map shows light intensity for central Tallinn but it’s not actual light…it’s statistically modelled light. The beauty of the map is that it gives us a look and feel that gets close to what we actually experience walking through dimly lit streets. There isn’t uniform light so why map it as such? Maps do not have to show actual features with simple symbology; instead they can show us information derived from an analytical process to give us a more realistic appearance.

What makes this map so intriguing is we perceive it as reality because it makes use of an increasingly familiar mapping style. It works because we are increasingly attuned to seeing such map styles and symbology. It also captures a certain atmosphere and almost looks painted in a smudged fashion with a wide range of dark, light and shadow that picks out different real world features as they might be seen by our own eyes.