When looking at a map, drawing a line between point A and point B, or the geometric distance, rarely equates to travel distance. We drive on roads, walk on sidewalks, and cycle in bike lanes. Because our transportation networks navigate around natural and man-made infrastructures, real time travel distances are almost always farther than the shortest distance between two points.
Consider this segment from the famously meandering Hana Highway on Maui between Huelo and Honomanu Bay:
The geometric distance, or radius circle distance, between these two spots is approximately 5 miles. This distance is measured as the crow flies or in a beeline – it’s the shortest distance between two points. If you were to actually drive it, however, your odometer would register about 10 miles due to the twists and turns in the road.
You can imagine that these two methods of reporting distance carry different levels of importance in different contexts – a user searching for the closest gas station when her car’s tank is almost empty has a lower tolerance for inaccuracy than a user looking for the perfect beach near his hotel in Huelo. When to default to radius circle distance and when to default to real travel distance depends on your users’ needs in the context of your application. Choosing the correct way to express distance not only improves the accuracy of the information you’re providing to your users, but also communicates that you understand their priorities when using your service.
Radius circles are useful when you want to communicate what’s nearby, but precise distance isn’t important. In these use cases, willingness to travel is a stronger force than the ability to travel. For example, say you are searching for a coffee shop near your vacation rental in a new town. You select a 1-mile search radius on your cafe-locating app because it’s a decent proxy for finding something you can easily walk to. Three results show up from your search, and when you look up the walking directions to each, you see that two are just under a mile away, and one is just over a mile:
You likely wouldn’t think twice about receiving a result that’s slightly over your 1-mile search limit. The uniformity of the radius circle has already primed you to expect rough estimates of travel distance, which jibes with your priorities as a user. What you’re really looking for is a coffee shop that you can easily walk to, not one that’s precisely 1 mile away from you.
Isodistances, on the other hand, account for roads and sidewalks in calculating travel distance, so they offer a more realistic travel radius from your origin. They are useful when you want to know what’s nearby, and precise distance is very important to you. In these scenarios, ability to travel is a stronger influencer than willingness to travel.
Let’s pretend you are preparing for a 100-mile century bike ride, and have a very specific training schedule in the weeks leading up. You’re looking for turnaround points for your 24-, 40-, 56-, and 72-mile “there and back” preparatory rides. If you were to use radius circles to pick a turnaround point, you may wind up adding on more mileage than you wanted to, and throw your training off. Instead, you need distance bands that account for actualized travel distance:
The non-uniform, calculated appearance of isodistance bands communicate that these estimates are founded on real travel distances rather than on the shortest distance between the origin and destination. In addition to providing you with more accurate options for your rides, they also offer subtle reassurance that your needs have been considered in the map design.
Stay tuned for part 2, in which we’ll do a deep dive into the technical underpinnings of isodistances!