Data Analysis

Spatial Analysis

Imagine you are stuck in outer-space and need to find your way back to planet earth. A good bet would be to follow the stars,  but a problem exists – how to deduce a pattern to act a makeshift landmark. The same problem persists when data is visualized on  the Cartesian plane – how to make valid conclusions.

Space and Spatial Representation of Data.
Space and Spatial Representation of Data.

Then I came across an amazing package in R – adegenet. It was built for spatial analysis of genetics data but within it packs several methods of joining the dots on spatially represented data. Of major interest to me was the Minimum Spanning Tree – a means to draw a line that passes all the dots  once using the shortest route. This algorithm is important for ISPs since it let’s them know the pattern of laying the cable that would incur the minimum cost. Let’s look at an example:

The map above shows Zuku coverage in Nairobi. Assuming these cables are not existent yet, what’s the best means of laying the cable so as to use the least amount of cables – run a Minimum Spanning Tree algorithm on the Geo-coordinates. Voila! Here’s the analysis.

Minimum Spanning Tree of Zuku Location in Nairobi.
Minimum Spanning Tree of Zuku Locations in Nairobi.

The optimized route is radiating the cables from the CBD. I hope this is how the Zuku cables are planned around the city.

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