I’m not sure how many people know that the world map in Pokemon Go is based on the same physics equations as the game itself.

But the idea that these maps are somehow analogous is an important one, and I’ll explain why. 

The most basic of physics equations is known as the Maxwell Equations of Motion.

It describes how a moving object’s motion is defined by the amount of mass it exerts on the surrounding space. 

It’s also known as Maxwell’s equations, and it describes a number of physical phenomena. 

For instance, a moving car’s friction with the road can be measured using the Maxwell friction coefficient. 

Similarly, the force of gravity, or the pull of a gravitational force, can be calculated by calculating the force per unit distance from the ground. 

A physics textbook will show you how to calculate the gravitational force for an object, and you’ll know what it’s like to see a body move in space, but this is where the analogy ends. 

Because of the nature of physical maps, the equations underlying Pokemon Go are quite different than the actual game itself, and the mapping process is essentially the same. 

So how does this translate to real-world maps? 

Well, Pokemon Go uses the Maxwell equations as a reference point.

That’s why the map looks so different from the game. 

When you walk around the map, the game calculates the force between the player’s feet and the ground to make sure the player is moving. 

But if you don’t walk around your Pokemon’s territory, the map will simply be empty. 

This is because the map is based entirely on a physics equation. 

In order to create an accurate representation of a physical location, the exact same equations must be used for each object. 

Now, there are some problems with using this kind of mapping.

First of all, there’s the problem of how to represent an accurate location in the physical world. 

Second, you can’t simply add things like buildings and roads to make the map accurate. 

Third, you’d need to figure out how to create a physical model of your Pokemon in the first place, which would require a lot of time and money. 

If you want to be accurate, you need to get a physical representation of your location that’s as close as possible to the game’s physical reality. 

To get around these problems, Pokemon GO uses the physics equations from the real world as its basis. 

However, because these equations are based on real physics, the real-time nature of the map can introduce a number more challenges than simply creating a physical copy of a Pokemon’s location. 

How the map’s equations change depending on where you goA few years ago, a team of researchers at the University of California, Santa Cruz and the University at Albany used a new way to make maps of the Earth. 

They used an algorithm called Dynamic Vector Autocorrelation (DVAC), which uses a combination of information from GPS and satellite data to create maps that look like they are generated by a computer. 

DVac uses GPS signals and satellite images to create accurate maps of Earth’s surface. 

Instead of using GPS data alone, DVAC uses a number known as satellite-based velocity data to estimate the speed of a moving target. 

These data are then combined with GPS data to generate a map of the world.

To get accurate measurements, a number called “velocity-to-velocity” or “V-to/V” is added to the GPS data. 

Once these velocities are calculated, a map is then created using DVAC that looks like this: Dynamical vector autocorrelation (DVC) of moving target, GPS data, and satellite-derived velocity data (click image for a larger view) This way, DVC takes into account the actual speed of the target as well as the speed at which the GPS signal is sending the signal. 

On top of this, GPS signals that are not in the exact right spot can also be added. 

An example of DVAC for the Earth: This DVAC image shows how a GPS signal (orange) can be offset to a certain point in the map. 

(DVC images of a satellite are colored in purple and green.) 

(Image credit: University of Albany) To make the maps look accurate, the team also added a “velocities-to” column in the DVAC output. 

Velocities to points on the map are calculated using GPS and the “vel-to velocity” data. 

 To calculate the velocity, the GPS satellite image is added in as a “source”, and a “destination” (red) is added. 

 (Image Credit: University at Buffalo) (This image was made using the same technique as in the previous example, but it shows the speed versus the direction of the signal.) To convert