Showing posts from February, 2017

Deadliest Airplane Disasters

I created a web app on the deadliest airplane disasters (fatalities >= 200) in recorded history (upto 25 Feb 2017).  I created the dataset based on the information contained in this wikipedia page.

Here is the web app in all it's glory:

If you want to explore the web app on a separate web page, please click here.

I have built in various charting and querying options to help you explore the data.  Additionally, you can search the map by the name of the airline.

I have also configured this web app to work on various IO/S and Android devices so feel free to check it out on a mobile device of your choice.

I'd love to hear what you think of my web app so please let me know by leaving a comment below.

Historic Earthquakes and Hurricanes in the US

Here's a web app I designed showcasing historic earthquakes and hurricanes in the US from 2000 to 2008:

Click here!

Here is what an image of the app looks like with a Dark Gray Canvas base map:

I used a map created by Dr. Pinde Fu of Esri to design this app.  It has been designed to work on many IO/S and Android devices so when you are ready, take it for a test drive!

ICC Cricket World Cup (1975 - 2015): a visual history

Being the cricket obsessed fan that I am (my obsession has gotten worse since I moved away from India and Australia to the US where I am starved of watching live cricket - distance does make the heart grow fonder!), I worked on a fun project this weekend.  I first started with a story map tour of all the cities that have hosted the finals of the various ICC Cricket World Cup tournaments from inception (1975) until the last edition (2015).

You can access the story map tour web app here:

Next, I created a map of all the countries that have ever participated in the World Cup.  Here is what the map looks like:

The size of the flag indicates the number of times that a country has been the world champion (min value = 0; max value = 5).

Then I focused on the two World Cups (held in 1987 and 2011) that were hosted by Asian countries.  For each of these editions, I created a map that marked the various venues where the cricket matches were played.  Here is what the map …

A simple app to collect Volunteered Geographic Information (VGI)

Per Wikipedia, Volunteered Geographic Information (VGI) is the harnessing of tools to create, assemble, and disseminate geographic data provided voluntarily by individuals.  VGI is a special case of the larger Web phenomenon known as user-generated content.  While there is concern over the authority of the data, research has shown that VGI may provide benefits to the end user above and beyond that of traditional data sources, in part due to its ability to collect and present data not collected or curated by traditional/ professional sources.  Additionally, VGI has been shown to provide positive emotional value to users, not only in functionality, but also in satisfaction, social connection, and ethics.

I created a simple (and fun!) app to collect VGI on most wanted Police suspects.  I used four famous James Bond villains as my suspects (Dr. No, Goldfinger, Jaws and Oddjob) and created a form for any user to record any sightings of these murderous hoodlums.

Here is where I spotted the…

Recent Earthquakes

I created a web app the mapped recent earthquakes in the world based on data from the US Geological Survey (  To understand the potential impact of these earthquakes, I added a layer that showed the population of the world's major cities (Esri's World's Cities layer).  Some countries with large populations (China, India, Indonesia) lie in areas that are prone to earthquakes.  Some less populated countries (eg New Zealand) are also in earthquake prone areas.  The other observation is that there is significant and regular seismic activity in the Indian and Pacific Oceans especially when compared to the Atlantic Ocean.  Here is the web map that I created:

I also created an interactive web app based on this data.  To view the web app in all its glory, go here:

What conclusions can you draw from this data?

US City-wise Population Growth and Unemployment

I conducted some basic analysis of US population growth in the 50 most populated US cities since 2010 and compared that with the unemployment rate in these cities.  Not surprisingly, there were some clear correlations: cities with high unemployment rates saw low population growth and vice versa.  Some highlights from the analysis:

The four cities with the most population growth since 2010 are Austin, Denver, New Orleans and CharlotteThe two cities with negative population growth since 2010 are Detroit and Cleveland. Here is the web map that I created:

I also created an interactive web app based on this data.  To view the web app in all its glory, go here:
What conclusions can you draw from this data?