Desire lines: what the paths people choose reveal about a place
Every worn shortcut across a lawn is a vote. Accumulated over thousands of journeys, those votes tell planners and retailers something no amount of theorising can: where people actually want to go.
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A story often told about Finnish planners goes like this: faced with designing the footpaths in a new park, they did not design any. They waited for the first snowfall of winter, walked the site, and mapped the tracks people had already made in the fresh snow. Those were the natural routes, chosen without signage or instruction, and they became the paths. Whether or not it happened quite that way, the idea holds.
Urban planners call them desire lines: the routes people actually take, as opposed to the routes designers intended them to take. The worn diagonal across a corner of grass is one of the most legible pieces of data a place can generate.
The geometry of least effort
The shortest distance between two points is a straight line, and people are good at finding it. Space syntax research (the academic discipline that analyses how street networks shape movement) describes this as least-angle routing: most pedestrians, consciously or not, minimise the number and severity of turns between origin and destination, even when a slightly longer route exists. In other words, geometry largely predicts where people will walk and where they won’t.
Desire lines are what happens when the designed geometry and the natural geometry diverge. The formal path goes around the edge; the desire line cuts the corner. The official entrance faces the main road; the desire line leads straight to the car park. Neither is wrong, but only one of them reflects what people actually do.
From parks to city squares
Cities that treat desire lines as feedback rather than trespass tend to end up with infrastructure people actually use. The snowfall method is the best-known version: planners are said to visit parks after the first dusting so that footprints reveal flows before any official paths are marked.
Rem Koolhaas took a similar approach indoors: for the McCormick Tribune Campus Center at the Illinois Institute of Technology in Chicago, he had students’ routes across the site mapped and turned the most-used ones into the building’s interior walkways.
Broadway, New York City’s diagonal slash through its grid-bound street plan, began as the Wickquasgeck Trail, a Native American footpath that ran the length of Manhattan.
The desire line outlasted every subsequent plan laid over it.
For city centres and smart city planners, the same logic applies at district scale. Where do pedestrians cross? Which entrances do visitors use? Which public squares are traversed and which are skirted? Read across a whole neighbourhood, these routes are the footfall pulse of the district. Observation from a single vantage point at one time of day can’t answer those questions accurately; that requires data.
The indoor version
Step inside a retail store or a transport hub and the dynamic is identical, just with a roof on top. Shoppers follow sightlines, floor-level cues, and the path of least resistance more than the gondola layout the planners drew. Some zones receive far more traffic than their position on a floor plan would suggest; others sit chronically under-visited despite being physically central.
Retail heatmaps make this visible. A zone-level heatmap, built from anonymous, aggregated position data instead of cameras watching named individuals, shows the dominant flows through a space. Those flows are the desire lines of the indoor environment, and fixtures, signage, and category placement can then follow what people do. For cities and municipalities managing large public buildings or transit spaces, the same data informs decisions from wayfinding placement to cleaning schedules. The Wi-Fi-based people counting approach does this without identifying a single person in the building.
Why planners resist them, and why they shouldn’t
The instinct to block a desire line with a bollard is understandable. The designed path exists for reasons: drainage, safety, sight lines. But a desire line is evidence of a mismatch between the designed environment and human behaviour, and that mismatch has costs: retail dead zones lose revenue, city squares that pedestrians skirt become underused assets, and public spaces with confusing flow patterns create congestion and resentment.
The smarter response is to treat the informal path as a signal and ask what it says: whether the designed route is too long, whether the entrance is not obvious, or whether the corner is more convenient than the official route because of what it connects. Answering those design questions well requires knowing where people actually go.
What data reveals that observation cannot
A person watching from a doorway sees the next few visitors. Movement data sees the pattern across tens of thousands of journeys, across every hour and weather condition. It surfaces things that feel counterintuitive until you see the numbers: the entrance nobody uses at lunchtime, the zone everyone traverses but nobody stops in, the sightline that pulls people reliably off the designed route.
Cellular network analytics works at district scale. From the signals phones exchange with the mobile network, without an app or a camera and without identifying individuals, it shows where those visitors came from in the first place, district by district, per day and season. The routes people take within a place are measured on site with Wi-Fi or sensors, and the data deliverables from those systems amount to a desire-line map: where people go, in what volumes, and at what times.
Following the feet
The desire line is a democratic signal: it records what thousands of people, acting independently, found to be the better route, without anyone asking them.
Urban design and retail planning have long argued between top-down imposition and bottom-up emergence. Desire lines point past that argument to iteration: design, measure, adjust.
A worn patch of grass already answers a question. Good measurement reads that answer at scale, across all conditions, without waiting for the season to change. Anonymous movement data lets you do that (for the park, the shopping street, and the shop floor alike) and it is the only footfall method in Europe approved by a data protection authority.
Sources
- Hillier, B., & Iida, S. (2005). Network and psychological effects in urban movement. In A. G. Cohn & D. M. Mark (Eds.), Spatial Information Theory: International Conference, COSIT 2005, Lecture Notes in Computer Science, 475–490. Springer. https://doi.org/10.1007/11556114_30
- Chicago Architecture Center. McCormick Tribune Campus Center. Buildings of Chicago. https://www.architecture.org/online-resources/buildings-of-chicago/mccormick-tribune-campus-center
- Illinois Institute of Technology. McCormick Tribune Campus Center. https://www.iit.edu/event-services/meeting-spaces/mccormick-tribune-campus-center
- American Planning Association. Broadway: New York, New York. Great Places in America. https://www.planning.org/greatplaces/streets/2014/broadway.htm
- Virginia Tech (2014). Dirt paths on Drillfield to be paved. Virginia Tech News. https://news.vt.edu/articles/2014/08/080514-vpa-drillfieldpaths.html
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Frequently asked questions
What is a desire line in urban planning?
A desire line (also called a desire path) is the track that forms when enough people take the same informal route because it is more direct or convenient than the designed path: across grass, through a gap in a hedge, diagonally across a square. Urban planners use desire lines as evidence of how pedestrians actually navigate a space, as opposed to how designers intended them to.
How do desire lines apply inside buildings and retail stores?
The same principle applies indoors. Shoppers and visitors take the paths that feel natural given the layout, sightlines and cues in front of them, and those routes may or may not match the flow the store designer intended. Heatmap analytics, built from anonymous Wi-Fi or sensor data, makes the indoor equivalent of a desire line visible: a density map of where people actually walk, dwell and turn.
Can desire lines be used to improve urban design?
Yes, and the best planners do. Some campuses have later paved the paths walkers wore themselves, as Virginia Tech did on its Drillfield in 2014. Finnish planners are said to map footprints in fresh snow after the first snowfall, a version of the same idea: let movement reveal the right routes before you commit to infrastructure.
What technology reveals desire lines at scale?
For outdoor districts and city centres, Wi-Fi can map flows across large outdoor areas, while cellular network analytics shows which districts visitors come from, without identifying anyone. For indoor spaces, Wi-Fi-based or camera-sensor systems map routes at the zone level. Either way you get measured evidence to use in place of gut feel.