Unlock increased event relevancy with the Suggested Radius API

Andrei Bizovi
Senior Product Manager

What is Suggested Radius?

Suggested Radius is a feature built into our API and web application that you can use to get quick, accurate radius recommendations for your locations.

One of the challenges when you're working with event data is deciding how to select a search radius. How do I identify the sweet spot where I'm getting visibility into just the right amount of events within a given distance of my locations? What radius should I select to yield substantial yet relevant results for events near my business? Put simply, the feature helps you find the signal in the noise.

This new feature automatically suggests an optimal radius for your locations, reducing the amount of time and effort it takes for you to derive value from our event data. Let’s dive into the details of how it works, and how to use it to make more informed business decisions.

Finding the ‘sweet spot’ for your radius is critical when dealing with event data

The optimal radius varies by business and location based on many different factors – but it’s generally the first step in working with event data. You want to identify impactful events, but this is harder when you’re not sure exactly where to draw the line for your catchment area.

If the radius is too small, you're only looking at a very small selection that is nearby your location of interest, but there may be events outside of that radius that you may want to be aware of – specifically, high-volume events that may impact your location, even if they are slightly further away. Conversely, if you zoom out and cover too large of an area it can quickly lead to noise, or irrelevant events, which may pollute your data and forecasting models, and in turn, skew your forecasts.

How Suggested Radius is calculated 

Finding this sweet spot is a task we used to perform manually, which was prone to errors and very time-consuming. We’ve now automated that process via models we trained from machine learning algorithms and our data lake. How does this stack up to before? We compared the results from before and after and the results returned by this new model significantly outperformed the models from before. 

The Suggested Radius returns the optimal radius that gives you just enough relevancy for your location when it comes to events, but not expanded so far as to start including irrelevant junk data. Let’s dive into the details of the different elements factored into the model’s calculations.


To suggest just the right radius for your business, the model factors in how many events will be returned. The outcome is visibility into just enough events so that your search results are relevant and free of noise, while still being substantial enough to analyze and factor into your demand planning.

Population density 

We take population density into consideration because in hyper-urban areas where pedestrian life dominates like Chicago and New York, customers are willing to travel a lot less in order to get to a particular store. These areas have a lot more stores and competition jam-packed into a very small location. For example, in Tokyo, the world’s most condensed city when it comes to population density, customers were only willing to walk 750 meters to a location before considering a competitor within closer proximity. When it comes to a hyper dense city like Tokyo, only a handful of places worldwide can match its population density.

Street network convolution

In addition to population density, urban layout also plays into the whole calculation. How complex the road networks are is a data point that informs traffic congestion and how hard it may be to navigate to particular points of interest. The more complex a street network is, the more traffic and road work you're going to encounter, and directions might be harder to follow, which may lead to navigation troubles. This is why the more convoluted the street network is, the lower we expect those return radii to be. 


Taking a closer look at the suggested radius for different types of businesses reveals that the radii align with how far customers are typically willing to travel to get to and from different types of businesses.

  • For example, the median radius returned by this model for parking garages equates to about a 15-22 minute walk. This makes sense considering when you’re looking for a parking space, you generally want some sort of immediacy and close proximity. The majority of the crowd wants to park as close as possible – even if it means it’ll take longer to get out of slow-moving parking lots after the event. 

  • For restaurants and QSRs, customers are willing to travel a bit further – about a 5 minute drive from the event to the location. For retailers, the typical travel time would be a 9 to 10 minute drive, which could be explained by the fact that if you want to visit a convenience store on the way out to an event, you might be willing to travel a bit more for an important item.

  • The Accommodation industry was interesting because going in, we had this assumption that if you're going to book a hotel to stay during an event, you're most likely going to book the closest one. However, what we actually found was that people who are booking accommodation are more likely to seek the lower-priced hotel stay, even if it means a longer drive to the event they’re in town for. Considering that proximity is not the most important factor for guests when it comes to booking hotels, accommodation by far has the largest suggested radius. Since people are typically willing to shop around for the most affordable deal on a hotel, customers are willing to travel 10 to 15 minutes drive away to the event, meaning accommodation has the largest catchment areas and the largest suggested radius on average.

Suggested Radius by industry

Let’s dive into how the optimal radius size tends to vary for the parking, food and beverage, retail, and accommodation industries by taking a look at the distribution of radius (in kilometers) vs population density:


In the industry graphs above, the blue and red lines represent the outer bounds of the radius size range. Between that range, we modulated by various other factors such as road network convolution and number of events within the area. 

The graphs show that the range for parking is quite flat, indicating the industry is not affected by population density. This makes sense considering if there's a parking lot nearby your destination, people will take it – even if this parking spot happens to be in the middle of nowhere, where population density may be extremely low. For the parking industry, the customer goal is to park as close as possible to their destination, not where the most people happen to be.

Here are the same results plotted on a map to scale:


Both visuals also indicate that the food and beverage, retail, and accommodation industries are much more sensitive to population density. In these cases, the higher the population density is, the lower the radius return radius is going to be. For example, for the food and beverage industry, when there’s about 50,000 people per square kilometer, the radius can go as low as two kilometers. 

When it comes to the accommodation industry, the visuals confirm that it has the largest radius size and the widest range, which is greatly affected by population density. In other words, for accommodation providers, where there’s a lower population density, the radius is larger as people are willing to travel further where there’s less people, and less options for hotels to choose from. But when the population density is higher, people are less willing to travel as far, similar to the Tokyo example from earlier. 

Unlock increased event relevancy now

Suggested Radius reduces the friction required to utilize our events data by accurately handling thousands of locations in quick succession, enabling pace and scale. Instantly get your custom radius profile that you can then use for your searches, location insights, and with our APIs. Log in now to see your suggested radius for each of your locations of interest.