A heat map is a map on which heavily used trails appear bright, created from the GPS data shared by all users. It’s a great help when searching for new trails or heavily used areas. The catch: In the digital world, hardly anyone makes a decision anymore without considering the opinions of others. Instead, we tend to follow the assessments and opinions of the masses. Researchers Matthew Salganik, Peter Dodds, and Duncan Watts demonstrated this as early as 20 years ago in the so-called Music Lab experiment: 14,341 participants downloaded unknown songs. As soon as they saw the real-time download counts, most based their choices on those numbers rather than on the music itself. As a result, a few songs accounted for the vast majority of all downloads, even though they weren’t necessarily the best in the selection. The same thing happens on the heat map: Every mountain biker first looks at the route choices of others before forming their own opinion. And those who follow along further amplify the effect, regardless of whether the trail is actually better than the ones next to it.

One Color, Two Standards

Two trails with exactly the same number of GPS rides can appear completely different in brightness on the heatmap. Strava doesn’t measure the absolute number of rides; instead, it compares a trail only with the trails in its immediate vicinity, never with all recorded routes. A globally uniform scale would show rural areas as consistently dark, because only the world’s most heavily trafficked locations would be visible at all. 

A trail in a remote valley can therefore appear bright even if only a handful of people ride it, provided the trails around it are even less frequented. Achieving the same color—or visibility—in a busy bike park requires many times as many rides. When you compare two colors on two different maps, you’re actually comparing two different scales. The heat map doesn’t show how many people have actually ridden a trail. It only shows whether more people have ridden here than on the trail next door. 

Where Quality Ends and Chance Begins

Brightness isn’t entirely worthless, though. The same music lab experiment also showed that the best songs practically never flopped, while the worst ones practically never became hits. It was only in the broad middle range that chance played a role. A similar distribution is likely for trails: A technically well-built trail will rarely remain dark, while an unrideable path full of fallen trees will almost never be bright. The problem primarily lies with the many decent trails that fall somewhere in between.

This is where the vicious cycle begins: A trail that happens to collect a few GPS data points first shines brighter on the heatmap than the trails next to it, even if there are only a few rides and the trail isn’t the best in the region. More riders see the bright trail and plan to ride it. Every additional ride makes the trail shine even brighter on the heat map—a self-reinforcing effect. Researchers were able to confirm this mechanism using online comments: A single artificially set positive initial rating increased the likelihood of further positive reviews by 32 percent and ultimately boosted the final score by 25 percent. A negative initial rating, on the other hand, usually corrected itself. Those who are lucky at the start are therefore more likely to stay at the top than those who are unlucky at the start and get stuck at the bottom.

A bright line is therefore not a seal of quality. It may indicate a truly good trail or simply an early lead that has since reinforced itself. The difference isn’t visible on the heat map. Trailforks states that local administrators have trails hidden that haven’t been officially approved, and the system internally logs when a trail reappears. Strava also explains that it regularly removes entries resulting from manipulated location data or incorrectly assigned rides. Both platforms know that the numbers alone aren’t enough and can be misleading. So anyone who blindly follows the brightest route reinforces the effect that highlighted it on the map. Those who instead report a trail’s condition or trust a local manager’s assessment more than the color alone help break the vicious cycle rather than fueling it further. But that’s only half the story.

The rider still has the final say

In addition to people, algorithms also play a decisive role—and the rules for those algorithms are written by the company behind them. In February 2026, Komoot launched voice-controlled route planning via its own AI application; according to Strava, its route assistant prioritizes the most frequently ridden trails in a region. If the AI keeps suggesting the same trail over and over, this is likely to reinforce the vicious cycle: more rides, more tracks, a brighter line on the heat map.

Still, no one has to ride entirely on a hunch. A bright line can indicate a really good trail. In some cases, however, it can also be misleading and fall victim to a self-reinforcing mechanism. Neither the platforms nor the riders benefit from this. But we’re not at the mercy of this effect: Those who are aware of this check the condition report before riding—rather than just the color—and, if necessary, ask someone on-site. Even after the ride, anyone can report their own impressions of the trail—this helps the next rider more than just another GPS track. It takes a few minutes, but it prevents the heatmap from turning into a “luck map.” It should remain a guide to good trails, not a blind judgment.