What heatmaps tell us about DPOL [Analyzing the Map #2]


This will serve as the second post in our Analyzing the Map series (the first can be read on Medium). We are posting this second entry on Discourse because we think it might make for an interesting conversation. At the very least we think our dedicated community members will be able to benefit from these kinds of posts living in one place. Let us know what you think!

We just recently pushed out our signal heatmap visualization layer on the FOAM Map. With this filter, we can make insights into how Signals propagate on the FOAM map, and ultimately reinforce our understanding of where the greatest need for decentralized location services lies.

Here are some of the trends we’ve noticed so far:

Signals map the hidden spaces between POIs. Heatmaps let us see this.

As POI-dominate systems of geolocation grow more popular, especially in the urban context, we move away from orienting ourselves around grids, addresses, and cardinal directions, and instead rely on searching for POIs to understand where something is. It’s much easier just to tell a friend to punch in New Lab versus telling them to go to “the intersection of Flushing and Cumberland in Brooklyn, just north of Fort Greene.”

This leaves us with a map that might be designed around roads and highways (not a discrete line segments, but continuous lines) - but is ultimately contextualized around individual points.

It makes sense to assume that a map highlighting the places where DPOL is needed the most, would simply be around where the tightest bunching of POIs are. And in most scenarios we imagine this being true. But there are also important use cases for DPoL that wouldn’t be successfully predicted by POIs alone. Going back to the idea that roads and highways are the arteries of our cities, this might be one of the prime examples of this phenomenon being true. In many of the urban sprawls in the US, millions of people hop in their cars everyday and percolate into mega-highways to commute to work. While these highways harbor millions of people – most of which have their phones and mapping apps open – there aren’t exactly coffee shops of museums on the barrier of the highway that they’re stopping at along the way. They are going from A(home) to B(work), not from A --> B (POI) --> C. Therefore contextualizing this commute through the lens of POIs wouldn’t be too helpful.

Signals were designed as a way to incentive locating areas where demand for decentralized location services is the greatest, whether or not there are POIs nearby. As such they hypothetically should be able to reveal geographical grey areas, such as highways and commuting routes where there is a tremendous demand on geolocation, but relatively few nearby POIs.

We see this confirmed in the Bay Area.

First let’s look at the map with just the POI layer turned on:

If we were to guess where DPoL was needed the most, based purely off where POIs are placed, we would see clear demand where I’ve drawn grey circles.

So then what does this same view look like with the signal heatmap turned on?

It makes sense to see the darkest shading (and thus the highest density of Signals) around downtown San Francisco and San Jose, but what might be surprising is that there is also a clear band that runs along the arterial line that connects these two cities.

Zooming in, we see that this band loosely reflects signals placed along junctures of Route 82 and Highway 101 – key throughways for Bay Area commuters.

Yes, there are plenty of other reasons why these Signals might be advantageous, which is only confirmed by the nearly 500,000 FOAM that is collectively staked in them. But it’s also interesting to consider that Signaling was a mechanism to reveal these important geographical grey areas that exist outside of direct downtown commercial zones.


Signals not only highlight hidden areas outside of cities, they highlight hidden cities themselves

Looking at the entire Signal heatmap of the continental US, a few surprises pop up.

Maybe the biggest surprise, is that it appears as if Phoenix Arizona, alongside NY and San Francisco, is one of the densest Signal locations in the US.

We love it when we are confronted with data like this, because it’s a clear reminder that the map we are building is truly crowdsourced. Signalling specifically was built to enable decentralized cartographers, not us, to determine where DPoL should be provided. The fact that over 20 Signals have been placed in Phoenix is a clear indicator to us that it might be a good place to, let’s say, run a DPoL pilot, even though the city was not necessarily on our radar before.

If you look at the individual Signals in Phoenix, you see that they aren’t as heavily staked as those in NY or San Francisco. This is because if the heatmap layer was visualized this way, it would give too much emphasis to large token holders (there is a 1000000 FOAM Signal in Union Square in NYC for instance). With the current configuration we are able to see mainly geographic density, which allows for a helpful Signaling preview across the entire globe.

Which leads us to our last question for you all - what do you think of the new heatmap layer? Have you been able to find any interesting trends on the FOAM Map with it? Do you have any new ideas for configuring the heatmap density? General feedback?

Let us know below!

Thanks for reading :cowboy_hat_face:

FOAM Map Upgrades Community Call (Mar 7- 12PM EST)

Interesting, great work on the graphics @Bryan.

Feels like a good place to echo this tweet from @cryptozen :slight_smile: