Using visualisation on TV and video is very different to using charts and maps online. In video, the audience has very little time to absorb the information contained in the chart — so you need to get them to that information as quickly as possible.
Every bad example of charts in videos forgets this. And every good example uses two essential techniques: keeping things simple, and adding motion.
Continue readingOne of the most powerful ways to generate original journalism is to look at the systems behind stories — particularly the points where those systems fail.
For investigative work, those points are central. Surface-level scandals often stem from deeper systemic problems. So what tools do we have for recognising those patterns?
Donella Meadows’s classic book Thinking in Systems offers one: “system traps” — patterns that explain how systems get stuck, break down, or behave in ways nobody intends. They are “traps” because attempts to escape them often backfire.
In this post I’ll explain each trap, what it looks like in the wild, and how to use it as a lens for story ideas.
Continue readingAt this year’s Dataharvest I delivered a workshop on using Python in QGIS to automate the process of exporting maps for multiple locations. Here’s how to do it (you can find a GitHub repository with materials and links here).
Making a map for a story is cool — but what if you could make a map for every reader? Or if you’re working on a project involving teams in different regions or countries, what if you could give each one of those teams a map centred on their own patch?
Normally you would have to manually move the map to centre it on a key city, and then export an image. Then do it again and again and again for every area.
Luckily, QGIS has the ability to run code. And this is a great excuse to start using it.
From stories about political speech and song lyrics, to street names and social media chatter, data journalists now have a wide range of examples of text-as-data to draw inspiration and guidance from, while tools such as Pinpoint and NotebookLM are making text analysis easier than ever.
I compiled a list of over 200 pieces of data journalism where text or documents were used as sources. Quantification techniques ranged from counting the frequency of a single word and using Google’s ngram viewer, to machine learning and topic modelling.
Looking at those articles it’s clear that, once quantified, journalists tell the same stories about text as any other piece of data: using the seven most common angles.
But how those angles are used — and how often — is where it gets interesting…
Freedom of Information (FOI) requests are not only one of the best ways to get original and exclusive stories that set your reporting apart — they’re also a good way to develop core journalism habits like curiosity, scepticism, and creativity. Here are some tips on how to get started with FOI while developing those qualities.
Curiosity is the first quality I identified in my series on the 7 habits of successful journalists — and FOI is a great way to hone that.
One good way to get started with FOI is to identify an event or problem that you’ve read about, and get curious about it: how many times is that event happening? How much is that problem costing? These are perfect questions for FOI.
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A pirâmide invertida do jornalismo de dados mapeia o processo de utilização de dados na reportagem, desde a geração de ideias, passando pela limpeza, contextualização e combinação, até à comunicação. A fase final — a comunicação — apresenta uma série de opções: desde a visualização e sonificação até à personalização e ferramentas. Mas quais são as melhores práticas para cada uma?
(Também disponível em inglês, alemão e espanhol, russo e ucraniano).
A visualização é normalmente a forma mais rápida de comunicar os resultados do jornalismo de dados: ferramentas gratuitas como Datawrapper e Flourish muitas vezes exigem apenas que você carregue os seus dados e escolha entre várias opções de visualização.
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Os projetos de jornalismo de dados envolvem várias etapas, cada uma apresentando seus próprios desafios. Para ajudar a compreendê-las, criei o que chamei de ‘Pirâmide Invertida do Jornalismo de Dados’. Ela delineia as etapas que precisam ser consideradas à medida que a matéria avança desde a conceção inicial até a comunicação dos resultados, e como elas se relacionam entre si. Abaixo, explico cada etapa, identifico questões a considerar conforme o projeto avança e ofereço conselhos e dicas sobre como enfrentá-las.
(Também disponível em Inglês, Alemão, Espanhol, Finlandês, Russo e Ucraniano.)
O primeiro desafio que um jornalista enfrenta é conceber uma ideia viável para uma matéria baseada em dados.
Continue readingIn the second part of this FAQ (first part here), I respond to more answers to questions from a Turkish PR company (published on LinkedIn here)…
There are three core skills I always begin with: sorting, filtering, and calculating percentages (proportion and change). You can do most data journalism stories with those alone.
Alongside those basic technical skills it’s important to have the basic editorial skills of checking a source against other sources (following up your data by getting quotes or interviews), and being able to communicate what you’ve found clearly for a particular audience.
Continue readingIn the latest FAQ, I’m publishing here answers to some questions from a Turkish PR company (published on LinkedIn here)…
There have been so many major transformations in the last 15 years. Mobile phones in particular have radically transformed both production and consumption — but having been through all those changes, AI feels like a biggest transformation than all the changes that we’ve already been through.
It’s not just playing a role in transforming the way we produce stories, it’s also involved in major changes around what happens with those stories in terms of how they are distributed, consumed, and even how they are perceived: the rise of AI slop and AI-facilitated misinformation is going to radically accelerate the lack of trust in information (not just the media specifically). I’m being careful to say ‘playing a role’ because of course the technology itself doesn’t do anything: it’s how that technology is designed by people and used by people.
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Decisions around what data to visualise and how to visualise it involve a range of ethical considerations and challenges, and it is important to emphasise that data visualisation is an editorial process just as much as any other form of factual storytelling.
Journalists and designers employ a range of rhetorical devices to engage an audience and communicate their story, from the choice of the chart and its default views or comparisons, to the use of colour, text and font, and animations and search suggestions (Segel and Heer 2011; Hullman & Diakopoulos 2011).
The chart that a journalist chooses to visualise data plays a key role in suggesting the type of story that is being told, and what the user might do with the data being displayed.
If a pie chart is chosen then this implies that the story is about composition (parts of a whole). In contrast, if a bar chart is used then the story is likely to be about comparison.
Line charts imply that the reader is being invited to see something changing over time, while histograms (where bars are plotted along a continuum, rather than ranked in order of size) invite us to see how something is distributed across a given scale.
Scatterplots — which plot points against two values (such as the cancer rate in each city against the same city’s air pollution) — invite us to see relationships.
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