
(Data provided by the NYC Taxi & Limousine Commission)[/caption] When you’re ready to render all of the data you’ve collected, you will run into another common choke point. If you are trying to visualize millions or billions of data points you will be putting a massive load on your rendering engine and you are going to be waiting a long, long time. GeoWave solves this by effective use of spatial subsampling. Each pixel on a map can only represent a finite amount of data, so GeoWave transforms the pixel space on the map using the underlying datastore to restrict the amount of data rendered onto a single pixel. In the example below, we are using this spatial subsampling to display 52 million GDELT data points. In the world of Big Data this is a relatively small amount of data, but it would still be more than enough to cause headaches for any analyst while he or she waits hours to view the data. Using GeoWave to help visualize data turns this into a process that takes a second. The subsampling is then performed again as you zoom in so that the best possible visualization of the data is maintained. [caption id="attachment_5218" align="aligncenter" width="1210"]

