r/PacificCrestTrail • u/Peaches_offtrail • 2d ago
PCT Data Analysis: where is the bubble?
tl;dr: I’ve looked at hiker comments across the PCT to assess hiker trends. According to the PCTA, around 4,500 hikers are issued permits to start the PCT every year. Only around half of all hikers that start the trail complete the trail in a given year. I estimate that there’s around 200-700 flip-floppers and skippers across sections. This post is an overall summary of key bits of analysis, which I think folks other than myself may also find interesting!
Data Summary
I went ahead and gathered hiker comment data over time. This gives me a log of comments recorded based on trail miles, dates, and unique hiker identifiers. This data set can allow for some interesting analysis that lets you pool statistical relationships together, to estimate different things about the trail!
Where’s the Bubble?
I used a variety of methods to estimate the bubble. This included 1) looking at the pace of hiking, by sampling hikers that made comments on consecutive days, and calculating the distance in miles between those consecutive comments, and 2) looking at the date distribution for comment density as you progress up the trail (different comment dates create the shape of hiker distribution for a given mile). This table is a rough estimate for the expected median date where the bubble might be at any mile in an average year on the PCT, based on the northbound median comment date.
| Mile | Expected Median Date for Bubble |
|---|---|
| 40 | 14-Apr |
| 540 | 20-May |
| 1,040 | 5-Jul |
| 1,540 | 23-Jul |
| 2,040 | 11-Aug |
| 2,540 | 4-Sep |
How does snow impact the bubble
I used NOAA SNODAS data, and pooled different Snow Water Equivalent (SWE) estimates on April 1st to provide an estimated SWE prediction relative to the average SWE for each section in an average year. I incorporated the SWE ratio value to see if this would improve the model's ability to predict a median date for each relevant section compared to the model without the SWE data. I found that snow data had an effect for a few sections of the PCT. The table below shows how much snowpack impacts the median for the 5 relevant sections of trail where it was found to meaningfully improve the model. The results suggest that for a big snow year, the hiker bubble seems to be delayed by around 2 to 4 days relative to an "average" year.
| Stretch | Median-of-other-years error | With snowpack | Days of delay estimate per doubling of snowpack |
|---|---|---|---|
| PCT, mile 700 (8 hiking years) | 6.5 | 4.7 (better) | +3.7 |
| PCT, mile 800 (8 hiking years) | 8.1 | 6.3 (better) | +3.6 |
| PCT, mile 1,000 (8 hiking years) | 11.3 | 7.4 (better) | +3.5 |
| PCT, mile 1,100 (8 hiking years) | 6.8 | 5.6 (better) | +3.8 |
| PCT, mile 1,200 (8 hiking years) | 4.9 | 3.9 (better) | +2.2 |
How many folks skip and flip, and where to?
Because I can estimate the standard hiking pace for hikers, I can look at the median distribution in one part of the trail, and estimate what the distribution should look like further up the trail by using the standard hiking pace. Additionally, as a check, I can also look at how the median distribution of comments moves from one section of trail to another, and look at the time between those two medians, and the number of miles, to estimate how quickly the bubble had to move to exhibit that sort of behavior. For areas of trail where the hiker distribution seems to deviate from the statistically-likely appearance, we have good reason to believe skipping or flipping may be occurring. The table below estimates the number of hikers that flip or skip to a different section of the trail based on conditions in prior sections of trail. The comment data reflects high likelihood of skipping and flipping from the Sierra to Truckee and Lassen in Northern California, and also from Etna toward the Oregon border.
| Stretch | Median date at the near mile (A) | Median date expected at the far mile (B) | Median date actually seen at B | Days ahead of the expected bubble | Extra people ahead of the crowd (area) | Share needed to cross the median to explain the shift | Rough number of flip-floppers per hiking year |
|---|---|---|---|---|---|---|---|
| PCT, Truckee to Drakesbad (miles 1,150 to 1,350) | Jul 10 | Jul 21 | Jul 11 | 10 | 15% | 20% | 190 to 510 |
| PCT, Etna to Callahans Lodge (miles 1,550 to 1,750) | Jul 21 | Jul 31 | Jul 26 | 5 | 7% | 9% | 20 to 210 |
How many people injure off/leave the trail?
To estimate the number of people dropping from trail, I look at the commenters and the frequency of their commenting behavior. Significant changes in their commenting behavior over time (goes from commenting a lot to having no more comments), is a good indicator that something happened (e.g. they injured off trail, or had a serious illness).
To estimate the highest likely rate of attrition, I took everyone who commented near the start of the trail. This population is established by creating a cohort for the northbound-looking people with a comment in the first 60 miles. If a person has a comment at or beyond a certain mile, that person is still considered to be “on trail” and hiking. Someone who never comments again is considered to have left the trail. As such, this bound overestimates attrition.
To estimate a conservative rate of attrition, I looked at a cohort of frequent commenters. This cohort consists of people with at least six comment-days and a typical gap of four days or less between comments across the trail. If one of these “frequent commenters” goes quiet, it implies something changed. This is likely to be indicative of an actual attrition event. Because hikers in this cohort had to be hiking long enough to write six times with gaps of less than 4 days between those comments, this is a conservative estimate of attrition. As such, this metric underestimates the number of people leaving the trail. The table below estimates the likely range of people staying on trail relative to those that initially started.
| Trail, mile | Survivor Rate (people that ever commented) | Survivor Rate (frequent commenters) |
|---|---|---|
| PCT, mile 300 | 83% | 96% |
| PCT, mile 500 | 77% | 91% |
| PCT, mile 1,000 | 66% | 81% |
| PCT, mile 1,500 | 58% | 72% |
| PCT, mile 2,000 | 48% | 64% |
| PCT, mile 2,400 | 33% | 57% |
There is a lot more analysis and discussion available on my very, very long blog post about it. The web tool is much easier to use to give you a quick snapshot of "where is the bubble?" and to estimate how many people might be ahead of or behind you at any given point on trail.
Generative AI Disclosure: the front-end tool I created to explore bubble trends was built largely with the help of generative AI. The underlying data is actual hiking data that were not generated. Any errors or inaccuracies are primarily the result of data quality and suitability of the analysis methods used. The methodology is repeatable and consistent, and several analytical approaches were conducted to assess statistical robustness. All writing was done by me. GenAI was used to help with formatting, graphing, and organizing of the main blog post.
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u/Ashland_Commons 1d ago
Super cool visualization and analysis!
Looking at your two sections "places where people arrive ahead of the crowd" appears to happen
1) around the 4th of July and
2) around Ashland, Oregon, a popular place to take a double zero since it's basically the last 'city' for most NOBO's for hundreds of miles. Ashland was voted fav resupply town in Oregon every single year by PCT hikers, and it's on the border.
I'm surprised I don't see KMS under the same kind of "places where people arrive ahead of the crowd" although potentially I am misinterpreting the data.
As someone who runs the hostel in Ashland, I made a post on August 1 announcing the nobo bubble was fully in Ashland, which was basically peak occupancy for the hostel being 100% sold out literally every single night. The bubble stretches for weeks, with many nobos arriving earlier in July and the stragglers arriving later in mid September.
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u/BackpackBirder NOBO 2018 2d ago
This is a really cool analysis! It also largely confirms my own experience.
The first graph nicely shows the NOBO and SOBO bubbles moving. It also shows attrition (less density towards the end). The only thing that puzzles me a bit is the area around roughly 1000 miles in August. I would have expected less data points there. What is going on there?
The second graph nicely shows that people slow down a bit in the Sierra, and then speed up significantly in Northern California. As you suggest, this may be caused by hikers skipping sections. However, in my experience many hikers also switch gears in NorCal and Oregon and start doing more miles per day, corresponding with the steep parts in the graph. So it could be a combination of both factors.
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u/Peaches_offtrail 1d ago
I think the August 1000 mile bump (from really ~800 to 1200-ish) is caused by flippers shifting back to the Sierra to finish some sections off now that the snow is melted
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u/Cascad1a 1d ago
very cool. so you ripped the data from farout? i noticed that the iOS app has a sqlite database that's relatively easy to open. curious if you got permission?
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u/ChicagoReds 1d ago
Where is your proof that "...half of all hikers that start the trail complete the trail in a given year"? I don't think it's even close to that number. I'd bet money that it's less than 10%. I commend your attempt to gather statistics about PCT hikers, but when it comes to how many, when, where, it's impossible to know because no one is checking off PCTA permits at the Southern Terminus, much less connecting names to a database. Another bet I'd make is that more than 20% of PCT permit holders never step foot on trail.
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u/Peaches_offtrail 1d ago edited 18h ago
Pretty reasonable question, but it's a fairly basic statistics problem.
- I assume that hikers that comment are a representative distribution of all hikers on the trail. This is probably a pretty good assumption.
If that assumption holds, then trends affiliated with that population are generizable to the population as a whole, with a margin of error.
The approach is then fairly straight forward. You look at people that comment early on the trail. If you see a comment from that person further up trail, then they're still on trail. So you assume they've made it at least that far.
However, this sort of counting likely overestimates the rate of attrition, because people don't necessarily always comment. For instance, if they comment twice on the trail -- like at mile 50 and again at mile 800, but never anywhere else, did they leave the trail at mile 800? Or maybe they finished the trail, but just never commented. Under the above approach, you would show them having left the trail at mile 800.
Based on this simple "did you comment again?" approach, we see attrition, across all years of data, at around 70% (which is an overestimate)
To try and get a more representative view, you can look at only the folks that comment a lot. These frequent commenters have a tendency to comment across all sections of trail (because they comment frequently). If one of these commenters stops commenting, then it's quite likely something is up (e.g. they left trail). In fact, at a population level, frequent commenters are likely to have had a comment (87% likely) within any 300 mile stretch or trail.
From the frequent commentor attrition measure, we see about 40% of frequent commenters stop commenting by the end of the trail (40% attrition rate). This likely underestimates attrition, because frequent commenters have to have been around long enough to be considered frequent commenters (so there's a survivorship bias, where folks that weren't frequent commenters weren't frequent commenters because they already left trail).
Given these two statistical populations are likely to be 1) an overestimate, and 2) an underestimate of attrition, the true value of attrition is somewhere between 70% and 40%. So pretty accurate/reasonable to say it's around 50% of hikers complete the trail based on the data. This is the best empirical data I'm aware of for actually measuring completition rates, as it's likely to be a less-biased sample of hikers on the trail than other methods (e.g. PCTA reporting, halfwayanywhere surveys, etc.)
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u/deadflashlights 1h ago
I don’t think it is a good assumption. Sure, some people comment on every single water source, but I found as the trail went on most people stopped commenting as you get into a groove. Saying “probably a good assumption” undermines your data.
That said, super cool graph!
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u/peopleclapping PCT Nobo '25/AT Nobo '23 22h ago
It sounds like your start and finish dates had a wildly different experience than mine. I started April 22 and finished Sept 6, so well in the mix of peak bubble. At the southern terminus, there were definitely over 45 people, quite possibly all 50 permits had set out from cleef that morning. At the northern terminus, during the 30ish minutes I was there, there were 9 people and I probably crossed paths with another 20 people as I approached and similar numbers heading back from the terminus. And it's also possible I missed some people in my count because I skipped the lake blue blaze. So I totally believe roughly half (or even more) make it.
Your estimate of 10% would imply you crossed paths with less than 5 people (including yourself) at the terminus and during the approach, unless you have a different definition of "completion".
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u/deadflashlights 1h ago
If trails are concurrent, FarOut mixes them. So this also includes the JMT hikers
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u/Johannes8 Pez 2023 SOBO - corridor-trails.com 2d ago edited 2d ago
That’s so cool! There are so many cool data mining opportunities! I was actually also thinking about building something similar on top of Corridor once there is enough data, to bring a solid alternative to farout to the community. The data in Corridor will be accessible even more directly not just through comments but by the app tracking actually where each night is spend (after user consent to share that of course). There will be really cool things to analyze from that data like camping heat maps for preservation organizations to see where people sleep a lot and what part of the trail gets the most wear etc.
I’d be curious to hear your ideas of what kinda of other analysis is possible. Feel free to DM me if you want to chat about collaborating or exchange ideas! It’s great to see other people contributing with their ideas to the community! It will be open source so you could even contribute or build on top if you wish. There are so many data mining opportunities once the data is freely available.
Specifically and this is unrelated to this: once the data is collected there is in my mind a huge potential is water report analysis. Comparing what hikers reports with snow/ temperature/ precipitation/ topography data to trying to learn what makes a stream have water at what time of year under which conditions. This would be actually kinda ground breaking in my mind if there could be an actually accurate forecast model of water levels of small streams. But this might also be way too complex to build a model for, I’m not sure.
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u/Peaches_offtrail 1d ago edited 1d ago
I actually did do water analysis. Basically: sentiment analysis + key word filtering of comments, and then looking at water trends over time for each waypoint.
The issue is, however, snow seems to very poorly predict most water sources during the hiking season for the AZT or CDT. Basically, the most variable sources tend to be filled by ranchers and are not reliant on stream conditions, so you can't really predict them very well: https://trailpeaches.com/thru-hiking/cdt-water-data/
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u/Forward_Hand2586 2d ago
Really interesting!
But I really don’t think a third or more of 4500 make it to the end. At least by my rough guesstimation when I counted the amount of entries in the register at the terminus