The State of Open Data

Today I looked into the status of open data around the world, to figure out where open data thrive.

I used latest cleaned data of Global Open Data Index in its 2015 edition as it’s more detailed and reviewed122 countries across 13 categories.

Open data & Hidden Data

The white color indicates open data is nonexistent in free, public, updated, machine readable, digital format; and darker color indicates higher scores

National statistics, government budget and government procurement are the categories with the most countries opening up their data, while land ownership, government expenditures and water quality are seldom revealed.

Open data vs Economy

Do developed & technologically advanced countries always place more emphasis on open data? Not necessarily as seen from the chart below.

Open Data Index on synchronized scale; GDP on free scale by continents

• Colombia, an emerging economy where GDP per capita is well below developed countries, actually led the pack across North & South Americas. This is affirmed by OECD. It’s on par with Australia and Finland! Africa has some countries more progressive in open data too, Rwanda has surpassed Russia.
• Countries with highest GDP per capita in each continent are not necessarily the most open in data either. For example in Europe: Switzerland, Luxembourg and Liechtenstein lag behind UK in open data index.
• Open data in Thailand and Israel are on par with Rwanda, Puerto Rico and Greece.
• Countries that are low ranking include Ethiopia and Malaysia, though the latest year isn’t yet revealed by Open Knowledge, so perhaps they caught up a bit in the last 2 years.

What matters is probably a commitment to data accessibility and transparency.

But one thing to note is that availability of data doesn’t equate usability of data, which doesn’t equate meaningfulness of data. Singapore ranked reasonably highly in APAC, and is on par with Austria. While there are indeed a plethora of easily downloadable data, some of them contain nothing more than two columns of year and value, which can be hard to make sense of.

What I learnt today is that one can manipulate chart axis to create radial heatmap. This is #day80 of my #100dayprojects on data science and visual storytelling. The code can be found on my github. If you like it, please share it. Suggestions of new topics and feedbacks are always welcomed.