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 dataset represents movement tracks
• the tracks are affected by geospatial features that constrain their paths
• tracks converge at certain features
• the ‘no data’ areas help us to understand the scale of the dataset.

What do you think the data might be representing?

Based on the previous inferences, we can start to form our analysis. Once we have understood the scale of the dataset and identified that the tracks correlate exclusively with hydrographic features/water, we can assess that these are shipping tracks. Furthermore, we can assume that these represent travel over time, rather than just a snapshot in time.

What is represented at A?

Building upon our previous assessment we can make the following analytical judgements:

• The Suez Canal
• a well-known shipping hub geographically located in this area
• an area that may have caused a gap in the data due to local requirements (such as no need for electronic reporting/assisted piloting/vessel access).

What is represented at B?

Building upon our previous assessment we can make the following analytical judgements:

• The Black Sea (Left) and The Caspian Sea (Right)
  • geographically located in an area that corresponds
  • likely location of vessel traffic
  • an inland waterway where vessel traffic is expected.