> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/getting-started/predicthq-data/ranks.md).

# Ranks

Two scores quantify an event's expected impact, both on a logarithmic 0-100 scale:

* [PHQ Rank](/getting-started/predicthq-data/ranks/phq-rank.md) - global impact, normalized worldwide. Use it to compare events across locations.
* [Local Rank](/getting-started/predicthq-data/ranks/local-rank.md) - impact relative to the surrounding population. Use it to find events that matter locally - a 5,000-person event means more in a small town than a big city.

In practice you rarely set rank thresholds by hand: a Beam Analysis calibrates them per location, and passing `beam.analysis_id` to the Events and Features APIs applies them automatically. For manual starting points, see [Recommended Event Categories and Local Rank Thresholds](/getting-started/guides/industry-specific-event-filters.md).


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