// Databricks notebook source exported at Tue, 28 Jun 2016 09:54:51 UTC

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and

The html source url of this databricks notebook and its recorded Uji :

What is Geospatial Analytics?

(watch now 3 minutes and 23 seconds):

Some Concrete Examples of Scalable Geospatial Analytics

1. Let us check out cross-domain data fusion in MSR’s Urban Computing Group

• lots of interesting papers to read at http://research.microsoft.com/en-us/projects/urbancomputing/.

//This allows easy embedding of publicly available information into any other notebook
//when viewing in git-book just ignore this block - you may have to manually chase the URL in frameIt("URL").
//Example usage:
// displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
def frameIt( u:String, h:Int ) : String = {
      """<iframe 
 src=""""+ u+""""
 width="95%" height="""" + h + """"
 sandbox>
  <p>
    <a href="http://spark.apache.org/docs/latest/index.html">
      Fallback link for browsers that, unlikely, don't support frames
    </a>
  </p>
</iframe>"""
   }
displayHTML(frameIt("http://research.microsoft.com/en-us/projects/urbancomputing/",700))

1. Several sciences are naturally geospatial

• forestry,
• geography,
• geology,
• seismology,
• etc. etc.

See for example the global EQ datastreams from US geological Service below.

**A bold idea: ** Imagine the non-parametric inference problem of estimating co-exciting Hawkes-like processes for modelling earth quakes on the entire planet!

For a global data source, see US geological Service’s Earthquake hazards Program “http://earthquake.usgs.gov/data/.

displayHTML(frameIt("http://earthquake.usgs.gov/data/",700))

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and