NZRS recently announced the launch of the Internet Data Portal, a place where we expect to publish Open Data about the Internet in New Zealand that can be useful for researchers, policy makers and those data tinkerers out there.

One of the datasets in IDP is the .nz Zone Scan, an effort that's been going on for over 2 years where we scan all .nz domains in the registry and check for a variety of desirable and undesirable configurations at the DNS level.

One of the data points we collect is IP addresses for DNS servers, Web servers and Mail servers of each domain. Using a GeoIP database (MaxMind being our preference), you can map those addresses to a country. Finally the data is aggregated to count how many domains have servers in each country on each zone scan, a quite interesting dataset.

Combining a little bit of Javascript, with Google Charts we can make a dashboard to explore the dataset. The map below shows the percentage of domains with a given type of server on each country. The color bar adjusts to the corresponding maximum when the dataset is changed.

You can drop down different dates and type of server to see the distribution of servers for .nz across the world.

One of the problems with the solution above is that you require a fair amount of Javascript knowledge to get something working. What if you are interested on something quick and attractive? We have also been using Plotly to generate some interactive plots using iPython notebooks, like this about Web servers for a specific date. If you hover over a country, it will show the ISO 3306 Alpha-3 code, the ranking and the percentage of domains with Web servers on that country.

Location of .NZ Webservers

Finally, using D3 we could generate an animated map that shows the changes of concentration across time. Use the play button to roll the time. The changes are really subtle for the distribution of DNS Servers, but you can see how United States change band in May 2014, or Australia increases their market share at the end of 2013.

Month: year

We encourage users to take advantage of this dataset and create beautiful visualizations. This is only a little showcase of the possibilities, looking forward to see some creative work with this!