Where is the thru-hiking bubble?

The bubble is the big crowd of long-distance hikers moving along the trail together. Below: where it is on the CDT and PCT right now, then a date-by-date explorer, and an estimate of how many hikers are ahead of and behind you.

Where is the bubble?

Where are you? How many hikers are ahead of you and behind you?

Choose your trail and direction, then set your mile: type it, drag the slider, click the chart, or jump to a town. The chart shows where the crowd walking your way is spread along the trail today, and where you sit in it.

NorthboundSouthboundHeight = how crowded, scaled to each direction's busiest day. Thick bar = middle half of that crowd.
Lines = average day each direction reaches a mile. Shaded = middle 80% of hikers. Faint lines = other hiking years. Click the chart to set the date.Dotted stretches: the crowd’s average moves faster than any person walks. People there are a mix of walkers and people who skipped ahead, and the crowd is spread over hundreds of miles at once.

April 1 snowpack for this hiking year, by region

Each pill is the region’s snowpack on April 1 as a percent of its 2005–2026 median (NOAA SNODAS, sampled along the route). 100% is a normal year; above 100% is more snow than usual. “n/a” means the region normally has almost no snow then, so a percent of normal is not meaningful. How these numbers are used.

This is a statistical model of past hiking years, not a forecast and not a live position. Real bubbles are lumpy, and the year-to-year uncertainty on the average passage date is a few days. See the write-up for how it was built, how it was checked, and where it breaks.

AI and data disclosure. Generative AI was used to help write most of the code developed to create this front end tool.

Data were assembled, cleaned, processed, and verified directly before incorporating them into this tool, and were not generated by an LLM. This tool works differently from LLM-generated text. Results and calculations displayed by this tool are directly calculated using hard-coded methods and a fixed, underlying data set. As such, these results are not subject to hallucination, and remain repeatable.

Accuracy of displayed results depends on the reliability and accuracy of the underlying data and methods that are used.