Monthly Archives: November 2022

VIDEO: An introduction to SQL for data journalists

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The database query language SQL pops up in all sorts of places when you’re working with data — especially big data — and can be a very useful way to query data in spreadsheets, APIs and coding. This video, made for students on the MA in Data Journalism at Birmingham City University, explains what SQL is, the different places you will come across it, and how to get started with SQL queries.

You’ll find related resources and tutorials in the repo here.

UPDATE: Thanks to Tony Hirst in the comments for pointing me to his post about browser-based SQL tools.

This video is shared as part of a series of video posts.

VIDEO: Big data, open data, linked data and other big ideas that data journalists need to know about

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Three key terms you might hear used in data journalism circles are “open data“, “linked data” and “big data“. This video, made for students on the MA in Data Journalism at Birmingham City University, explores definitions of the three terms, explains some of the jargon used in relation to them, and the critical and ethical issues to consider in relation to open and big data in particular.

Three other video clips are mentioned in the video, and these are embedded below. First of all, Tim Berners-Lee‘s 2009 call for “raw data now”, where he outlined the potential of open and linked data…

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Here are some great examples of how to use AI and satellite imagery in journalism

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False colour image of the Paraná River near its mouth at the Rio de La Plata, Argentina
False colour image of the Paraná River near its mouth at the Rio de La Plata, Argentina. Image: Copernicus Sentinel data [2022] processed by Sentinel Hub.

In a guest post for OJB, first published on ML Satellites, MA Data Journalism student Federico Acosta Rainis explains what can be learned from some examples of the format.

Satellite imagery is increasingly a key asset for journalists. Looking from above often allows us to put a story into context, take a more interesting perspective or show what some power prefers to keep hidden.

But with hundreds of satellites taking thousands of images of the Earth every day, it is difficult to separate the wheat from the chaff. How can we find relevant stories in this ocean of data?

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What stories can you tell using AI and satellite imagery? Here are some ideas

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In the second of two guest posts for OJB, first published on the ML Satellites blog, MA Data Journalism student Federico Acosta Rainis uses the 8 angles used by data journalists framework to explore satellite image-driven journalism.

Satellite-driven stories don’t have to use using artificial intelligence (AI) — many can be told using satellite data alone, without. The main advantages of AI include quantifying phenomena, identifying patterns, showing changes or finding a “needle in a haystack” across large territories or different time periods.

AI algorithms can also be used to automate a process: since satellites produce recurring data, you can build, for example, a platform that automatically detects changes in the size of forests.

Paul Bradshaw’s framework for data journalism angles recognises eight types of stories: scale, change, ranking, variation, exploration, exploration, relationships, stories about data and stories through data. The same framework can be adopted to generate ideas for satellite journalism, too.

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