Greetings! Welcome to Venky Rao's blog on Predictive Analytics, Geospatial Analytics and Visualization. This blog aims to present interesting analysis of geospatial data and to de-mystify predictive analytics for the layman. My blog is featured on: http://www.kdnuggets.com/ - Analytics and Data Mining Resources.
Over the past year, I have been exploring the geospatial capabilities of various R packages. Today, I want to share the most basic of geospatial capabilities, which is producing a map. Using R, you can do this in just 5 lines of code.
Let's produce a map of Boston, Massachusetts. Boston has a longitude of -71.0588801 and a latitude of 42.3600825. Since we list x and y coordinates in order (i.e. we list y after x), we list longitude (the horizontal coordinate) before latitude (the vertical coordinate). Let's create our map!
Code line 1:
This command installs the ggmap package in your R environment.
Code line 2:
This command loads the ggmap package in your R environment.
Code line 3:
boston <- c(lon = -71.0588801, lat = 42.3600825)
This line creates a variable called "boston" and assigns the lon and lat coordinates in it.
I have the great honor of knowing ex-IBM Fellow Jeff Jonas, the co-Founder, CEO and Chief Scientist of Senzing. Apart from being exceptionally talented, Jeff is also an amazing human being who is always willing to help others. I have personally been the beneficiary of his generosity and continue to benefit from his counsel every day. Jeff is one of the main reasons why I have chosen to follow a technical career path at IBM.
Jeff left IBM in 2016 to start a new venture called Senzing. Senzing has built the first real-time AI software product for Entity Resolution (ER), a space that Jeff is the world's #1 expert in. Senzing's new offering has huge implications in the post-GDPR world and has the potential to increase trust in Blockchain networks. Jeff recently gave a keynote at the IBM Think conference where he described what Senzing does and its potential applications (including as part of IBM Blockchain). I strongly recommend watching it.
This post attempts to visualize the 1854 London Cholera Outbreak based on data collected by Dr. John Snow and provided in the HistData R package. Dr. Snow was able to identify that cholera was a water borne disease by visualizing his data in 1854 and was able to bring the Cholera outbreak to an end. This dataset and analysis speaks to power of geospatial data and its importance in decision making.