"Adrian Holovaty published a groundbreaking essay about the fundamental assumption of what local news should be - structured information "that can be sliced-and-diced, in an automated fashion, by computers" instead of "a big blob of text that has no chance of being repurposed." Holovaty imagined a world where a story about a local fire would let readers explore the raw facts: date, time, place, victims, fire station number, response time; then compare those details with previous fires, and subsequent fires, whenever they happen."
"Six years later, Jeff Jarvis wrote a frustrated post on his blog Buzz Machine. Hurricane Sandy had just devastated his New Jersey neighborhood. He needed to know which streets were passable, where power crews were actually working, which gas stations had fuel. His local news outlet published stories about the devastation but left the community on its own to find useful information to get through the day."
A 2006 vision proposed local news as structured, machine-readable data rather than unstructured text, with discrete facts that can be sliced, diced, and repurposed automatically. That approach produced verification and data-driven projects and earned mainstream recognition, but the majority of local newsrooms did not transition. A major storm revealed how lack of structured local information left communities searching for passable streets, power crew locations, and fuel sources. Barriers included the need for developers, custom databases, and budgets. Recent advances in AI could enable news organizations to act as community information utilities, providing searchable meeting records, historical comparisons, and neighborhood connections. Early tools already track local government meetings for reporters and readers.
Read at Nieman Lab
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