05 May

Spatial Analytics

I’ve written before around the key value aspects of data being spatial and temporal. I have been doing a lot of reading on some of the spatial side of structured data.

To start with I wanted a basic understanding of GIS. This is the core of most of the cutting edge spatial analytics available and probably way out of the reach of big data. It has been interesting because a lot of the analytics are discrete (point information forming a grid) but some is interpolated smooth information. The other aspect of this is kernelling and clustering which is key to big data.
Follow this:
Map Step – give the the location of each widget sold (widget, (location,1))
Reduce Step – Sum widget sales per area

Now I know where each widget was sold. This allows many aspects of the business to be optimised:

  1. Logistics – how many widgets should I be moving to each of these areas?
  2. Sales Planning – if widgets are not selling well can we run specials?
  3. Resourcing – do certain widgets sell better in certain areas?
  4. Bundling – are people buying two different widgets frequently in one area and not in another? Can this be exploited?
  5. Layout – are there sales gaps (zero widget sales) between two high use areas?

One of the fundamentals around spatial analytics revolves around the ability to visualize this effectively. I have some faith in computers but they need to be watched. While I’m on this journey I will need to see some thing to believe them. So this gap in the last point is the core of big data, the rest is pretty much the analysis of structured data you already have. You know where your stores are and what your sales are. Even the smallest enterprise has this data. The clincher to take this to the next level is to map my data about against a few more datasets:

  1. SARS demographics – What is my disposable income (higher tax brackets) in the area where widgets are selling well or badly?
  2. Census data – are there gaps where there is population?
  3. Climate data – do certain widgets sell better in warmer areas?

Data is king, questions the Queen but correlation is the Ace