# How Calculus III can help us identify Ambiturners.

Derek Zoolander famously was not an ambiturner. He had to go through a hero's journey to be able to turn both left and right. In this post we will take a hero's journey to identify right and left turns from running data using only GPS measurements.

In my last post I described an algorithm to reduce the number of points on a 2D curve that preserves the basic network structure of the curve. This algorithm, the Ramer-Douglas-Peucker (RDP) algorithm helps us to identify abrupt changes in a curve, where abrupt is subjective and up to the user to decide upon.

In this post I present a novel and simple approach to answering the following question: Did I take a left or right turn and how did I take that turn? Smoothly? Roughly? Constantly? The method presented here is new and remarkably simple compared to other methods in the literature. Most methods I saw relied on accelerometer data which needed heavy machinery such as Kalman filters to make similar determinations.

### Calculus III to the rescue

The basic vector description of the plane and 3D space always seems like a bore when teaching. It's hard to get students to be fired up about vector addition and the algebraic structure of euclidean space. So finally, here is an interesting application of the dot product and cross product that goes beyond the ubiquitous force equations from physics.

Consider the map below. Let's assume we have identified the turning points from the RDP algorithm as described in my previous post. Identifying whether it is a right or left turn is extraordinarily simple. Given any three points on a plane we can find the angle that subtends the middle point with our friend the dot product,

$\vec a \cdot \vec b = ||\vec a|| ||\vec b|| \cos\theta$

On the map above we will have something like this,

If the angle is between 0 and 180$^o$ it is a right turn and if it is between -180$^o$ and 0$^o$ it is a left turn. But to solve for the angle we have to use the $\cos^{-1}$ function which only has values in a range for a range of 180$^o$. This is where the vector cross product saves the day. The 2D cross product is simply the determinant. If the determinant is negative, the angle is between 0 and 180, if the determinant is positive it is between -180 and 0. Thus, left turns and right turns.

Finally, our algorithm is the following:

1. Apply the RDP to the dataset of lat/lon's. This leaves us with a network G of vertices and n-1 edges.
2. For i in range(1,n-1) form vectors for the i-1, i, and i+1 vector (as shown above) and compute $\theta$.
3. Calculate the determinant and determine left turn or right turn.

That's it!!!!! Glorious, glorious Calc III. Here is a map of run I took in Gedera and the results of the algorithm applied. The run starts with the left-most point. This algorithm resulted in 100% accuracy.

Type of turn, angle Right turn, 36.9872677438 Left turn, -27.8188864289 Right turn, 55.4872049488 Left turn, -114.980809001 Right turn, 148.508392832 Right turn, 113.14225291 Right turn, 163.80922161 Left turn, -150.035986035 Right turn, 175.043761198 Right turn, 161.791261833 Right turn, 83.6023186647 Right turn, 109.851066119 Left turn, -126.533104243 Right turn, 116.820422235 Left turn, -97.4253631752 Right turn, 160.584337619 Right turn, 93.0049662148 Left turn, -164.456048578 Right turn, 139.971999906 Left turn, -142.289251078 Left turn, -165.463072689 Left turn, -143.612951911 Right turn, 119.067520889 Left turn, -155.864187354

# Turning a GPS route into a Network

Sometimes it may be necessary to simplify a set of points into a network. It may be for qualitative reasons such as determining intersection points and conduits of a network. It may also be for quantitative reasons like shrinking a dataset and speeding up computations. In this post I will go over a case study of GPS data and how I to turn this data into a smaller and more usable dataset.