Data Friction in Development

My favourite example of publishing is a visualisation shown to us in the Gillian Fuller’s lecture, which shows world development over time. More specifically, it shows the wealth and health of 259 countries from the year 1800 up until 2009. It uses a spot to represent each country, and a colour code to defines the region of the country. The video below is a short tutorial on how to use the visualisation.

You can click the data tab and see the raw data and more specific information, such as unemployment rates, and HIV rates, which is then broken down into age brackets. Another tab reveals a data blog, which publishes new data that has been added and updated, which has had an effect on the visualisation.

The initial purpose of publishing this data was to pursue the development of the Trendalyzer software. Trendalyzer sought to “unveil the beauty of statistical time series by converting boring numbers into enjoyable, animated and interactive graphics” (Gapminder, 2011).

Gapminder.org describe themselves as a modern museum on the Internet – promoting sustainable global development and achievement of the United Nations Millennium Development Goals. They produce videos, flash presentations and PDF charts showing major global development trends with animated statistics in colorful graphics.

As visitors to the site we are able to publish any of this material in books, blogs, newspapers, or exhibitions as long as its purpose is educational, informational or non-commercial and you give the source: “Free material from http://www.gapminder.org”.

The projects Gapminder publishes are sometimes collaborative with universities, UN organisations, public agencies or non-governmental organisations, but we are not able to edit the data. It is simply there to inform us of how we are progressing in alleviating poverty, which is a far more complicated a concept than it sounds. A lot of different data must be taken into account, for example GDP per capita, Life expectancy, education, disease etc. A number of International Organisations, such as World Bank and the International Labor Organisation, are involved in making the global data. The effort involved in making this data is what Paul Edward calls data friction in this weeks reading The Vast Machine. Organisations, researchers and academics build this knowledge infrastructure and we may reproduce this data, thus becoming part of the knowledge infrastructure.

There have been various different approaches to understanding development over the years. Many scholars consider economic factors as crucial to development. In the 60s and 70s neo-liberalists looked at GDP per capita as the sole factor in development. More recently, most believe this completely ignores basic rights, like education, the right to vote, and gender issues. The United Nation’s Millennium Development Goals are the most recent approach to development and aim to achieve the eight development goals by 2015. Unfortunately, it is very likely that the UN will not succeed in achieving these goals by 2015, but they serve as a model for future development planning.

Similarly, Paul Edwards talks about the models of climate change in The Vast Machine and how, despite the fact that they may not be accurate, they are still necessary in gaining a better understanding of the changes occurring and making more educated models in the future. “Everything we know about the world’s climate—past, present, and future—we know through models” (Edwards, P, 2010).

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