29
Oct 14

“Open Development Cambodia” Interview with Executive Director Thy Try

Johannes Kast presents an open data mapping platform in Cambodia and interviews the executive director

ODC

ODC Logo

Open Development Cambodia is a novel, non-commercial open data platform designed to collect data and make it available, e.g. through interactive maps, in order to address environmental, economic and social issues through the unbiased lens of raw information. They also provide important, up-to-date information on natural ressources, laws & regulations, company profiles and more.

It’s the first of its kind in South East Asia and both the software that is used and the methodology are open source, transparent and freely accessible to everyone. Especially in recent years, Cambodia is undergoing constant, fast-pace changes, so the mapping software provides a useful illustration.

The ODC Team

The ODC Team

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27
Oct 14

Is Google predicting the f(l)uture

Charlotta Duse about

Google Flu Trends

By analyzing the big data from our google searches, the Google Flu Trends claims to provide ”near real-time estimates of flu activity for a number of countries and regions around the world”.

The idea of the GFT is that if people feel sick, they will search for medication, expected symptom etc. on Google, and the company will be able to analyze this user data and see how and where the flu is spreading. Kenneth Cukier and Viktor Mayer-Schonberger calls the system ”more useful and [a more] timely indicator than government statistics with their natural reporting lags. Public health officials were armed with valuable information” (2013:pos45).

But voices are also raised for the contrary, voices saying that GFT failed its purpose various times. Some even calls it ”a prime example of what can go wrong when you read too much into your Big Data”

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19
Oct 14

2014 and the Ministry of Truth | Newspeak: Minitrue

Abigail Leffler says Big Data Brother… one bit at a time

ICT (Internet communications technology) enables gathering of digital data derived from our online interactions and other iterations such as those that come from GPS (Global Positioning System)-equipped devices. This interactivity being ‘a necessary condition for social, cultural and political participation’ (Lievrouw: 2013, p. 15) functions as a catalyst for change, development and humanitarian relief.

Just consider that all the tweets, blogposts and Facebook entries generate big data and so do all the ‘likes’ and endorsements and any other information pointing to user connection networks and to activity levels of individuals on the Net.

To give you an idea of how large big data actually is, every minute of every day we create

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11
Oct 14

Big, BIG, Data Warbles

Abigail Leffler perchs on the development branch and broods over the content analysis of multilingual tweets and posts

Any collection of signs systematically arranged (or the absence thereof) can be read and interpreted. Edgar Allan Poe’s A Dream within a Dream, Edvard Munch’s The Scream painting, Ludwig van Beethoven’s Fifth Symphony, a tiger’s territorial markings in the Amur region, mobile phone traffic in the aftermath of the Haiti earthquake and all the electronic footprints we ever leave behind by virtue of our Internet usage are examples of this. The key point is that, in our search for patterns or for elements that maintain or break patterns in a sample, we are searching for clues to predicting behaviour or finding trends and hidden messages.

Now for the sake of simplicity and to keep true to the title of this post, let us alight on the analysis and derivation of meaning (a.k.a. interpretation) of our Internet footprints. Let us, furthermore, focus on blogging and microblogging in the context of communication for development.

How do we analyse data from blogs and microblogs? We could be looking at quantitative methods such as collecting the amount of tweets and posts and the frequency thereof, and further we could be looking at the geographical distribution of such entries or at the speed at which they come during or after an event. We could consider which entries are the most influential within a specific period of time. We could also be looking into the qualitative content of such data, and we could be looking into a keyword analysis to gauge sentiments or determine key topics in discourse. And now let us expand on this last point. What are the caveats we need to bear in mind when the analysis is conducted within a globalised, multicultural environment, and where tweets and posts come in forms as diverse as chatter, clucks, quacks, chirps, hoots, coos and caws?

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