Machine learning and maps applied to the ordinary infrastructure of daily life: third places, platform algorithms, neighbourhoods, and the politics of what gets measured.
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What 640 London shopping streets, 4,000 Billboard hits, a million films and synthetic AI personas tell us about why everything is flattening into sameness and how to regulate the algorithms behind it.
I tried to measure the decline of British community life by counting clubs. That was the wrong place to look. What’s disappearing is participation especially where deprivation is high.
Why London keeps losing its independent neighbourhood restaurants, and what a basket lowered from a Lebanese window taught me about what is happening.
How Australia kept its third places alive, what it hid inside them, why Britain’s fix made the problem worse, and what happened when I tried to beat the machine.
I built a machine learning model to find London's divide and you can enter your postcode to see which side you're on. We've been blaming the wrong people for it.
How private equity reshaped the local and the postcode tool that shows the pubs most at risk.
The real reason Airbnb clusters where it does - and 1,500 listings you can book guilt-free if the policymakers won't listen.
I wanted a dinner recommendation and got a research agenda instead. Using 13000+ restaurants, I rebuild its ratings with machine learning and map how algorithmic visibility actually distributes power.
A data-driven verdict on whether we’re living through a genuine AI boom or an over-inflated bubble - from capex and chips to markets, adoption, and a new metric that tracks the gap.
How null results didn’t kill my project - they rewrote it. They revealed a new, measurable form of responsiveness inside the most secretive central bank in the world.
