// 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 Image of Uji, Dogen's Time-Being:

sds/uji/week2/week10/035_ScalableGeoSpatialComputing

What is Geospatial Analytics?

(watch now 3 minutes and 23 seconds):

Spark Summit East 2016 - What is Geospatial Analytics by Ram Sri Harsha

Some Concrete Examples of Scalable Geospatial Analytics

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


//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

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