Description

The analytical methodology we apply to understand this dataset is one of pattern recognition. By acknowledging that black is ‘no data’, we can start to exploit implicit relationships in the data based on these patterns. Some of the things we can infer initially are:

• The edges of the data appear to correspond with coastlines.
• The ‘no data’ corresponds with water features on the data corresponding with land features.
• The data is mostly organic in nature.
• The data spatial distribution seems to correlate with human population.

What could the data be representing?

We can infer that the data could be representing:

• Population: based on a number of data inputs, the density shows where people are located.
• Light: one of the primary data inputs is light; lines of communication and borders display as data points, while static data would only show dwellings.
• Human activity: there is a relationship between light data and human activity.
• Terrain classification: population directly correlates to habitability.

The keen geographer may have turned this image upside down. The data is intentionally displayed as it is to avoid anchoring bias in viewers familiar with this region.

What is represented at A?

We can now hypothesise that A is representing a human relationship with an organic feature. Pattern recognition and scale let us infer that this is likely a major watercourse. If we reorient the image by inverting it, we can come to the conclusion that this is the River Nile.

What is represented at B?

By comparing the feature immediately above B to the feature we have already identified at A, we can analyse the differences. Whereas A has an organic and irregular pattern, B has a more linear pattern. When we add this to our assessment of what the dataset is likely representing, we can conclude that this feature is likely a line of communication, probably a major road. This also reinforces that this dataset is likely to represent population over time.

What is represented at C?

Building upon our previous assessments, we can infer that the feature represented at C is a population centre where large numbers of people live, linked by lines of communication and a transport hub/node.