A while back I was investigating on how I would build a Twitter mapping application similar to the One Million Tweet Map project. One of the first topics I looked into was how I would plot and display each tweet on a map. I was hesitant on using the Google Maps API and wanted to build the project on top of the HTML5 Canvas.

The main challenge here was implementing an algorithm that would map latitude and longitude information into x and y coordinates onto the canvas. I looked into several map projections and decided to settle on the Mercator projection (arguably the most commonly used map projection in the world). I did a little bit of research into Mercator projections with the help of Wikipedia and here is a concise summary of what I’ve learnt.

The Mercator projection is a cylindrical map projection. This means that meridians are mapped to equally spaced vertical lines and circles of latitudes are mapped to equally spaced horizontal lines. The projection assumes that the Earth is a perfect sphere (in truth the Earth is best modelled by an oblate spheroid). Our map will be a Mercator projection of a scaled down version of the perfectly spherical Earth. This scaled down version of the Earth will be called the globe and it has a radius $R$.

The Mercator projection can be understood using small element geometry. The idea is that if we “unwrap” the globe and spread it flat as best we could, very small elements or sections on the globe can be modelled using a rectangle. The diagram below visualizes this idea ($\lambda$ represents the longitude and $\phi$ represents the latitude).

The perpendicular distance from the axis of rotation to any point on the globe at latitude $\phi$ is $R \cdot \cos\phi$ and so we get $R \cdot \cos\phi \cdot \delta\lambda$ as the horizontal distance between $P$ and $Q$ using the arc length formula.

The vertical distance between $P$ and $Q$ can also be explained using the same logic. Since latitudes are measured from the center of the globe and the difference in latitude between $P$ and $Q$ is $\delta\phi$, using the arc length formula again gives us $R \cdot \delta\phi$.

With this information, we define two scale factors from globe to cylinder:

## Mapping from longitudes to x coordinates

We note that for small $\delta\lambda$, $\delta x = R \cdot \delta\lambda$. From this equation, we can obtain the mapping function from longitudes to x coordinates (one half of the Mercator projection). $\delta x = R \cdot \delta\lambda$ is a very simple rearranged first order linear differential equation and we can solve it quickly.

Here $\lambda_0$ is the longitude of an arbitrary central meridian (this is usually Greenwich).

## Mapping from latitudes to y coordinates

Before starting, it is important to understand that the Mercator projection is conformal. This means that the projection of points from the globe to the cylinder preserves angles. This property can be defined in two ways:

1. Equality of angles $\beta = \alpha$
2. Isotropy of scale factors: $h = k$

From the above, we know that $\delta x = R \cdot \delta\lambda$ and substituting this into the expression for $k(\phi)$ gets us:

Solving this differential equation gives us the mapping function from latitudes to y coordinates:

## Finding the radius of the globe, $R$

But wait, there’s more! All this time we’ve been working with the radius of the globe, $R$, but what exactly is it’s value once we put the projection into practice?

Since the Mercator projection is cylindrical, the scale factor between the globe and the cylinder equals one only at the equator. This means that the circumference of the cylinder will equal the circumference of the globe and so the horizontal length of the physical map will equal $2 \cdot \pi \cdot R$.

## Conclusion

Finally, we can define the Mercator projection with these three equations (where z equals the horizontal length of the physical map):

## Inaccuracies with the projection

Interestingly, despite being one of the most popular projections in the world, the Mercator projection is grossly inaccurate in representing the relative sizes of different land masses. In particular, land masses get bloated the closer to the poles you go up to a point where the projection is not even capable of displaying the poles.

This weird property of the projection can be understood by examining the parallel scale factor ($k(\phi)=\frac{\delta x}{R \cdot \cos\phi \cdot \delta\lambda}$). As we approach the poles, the latitudes approach $\pm 90^o$ and we know that $\cos\phi$ approaches $0$. Since $\cos\phi$ is in the denominator of the scale factor, the factor approaches $\infty$. An accurate projection will require the parallel scale factor to be $1$ everywhere.

This explains why land masses closer to the poles get bloated while the poles cannot be represented since they would technically have infinite width on the projection.

##### Sidenote

We can derive the latitude mapping function a second way.

For small elements, we note that $\measuredangle PKQ$ is a right angle and so we get the two equations:

From $(1)$ equality of angles, we get the equation $\frac{R \cdot \cos\phi \cdot \delta\lambda}{R \cdot \delta\phi} = \frac{\delta x}{\delta y}$. We also know that $\delta x = R \cdot \delta\lambda$ for small elements. Using the latter fact and rearranging the former equation, we again reach $\frac{\delta y}{\delta\phi} = R \cdot \sec\phi$.