Oriented Areas and the Shoelace Formula
Here’s a summary of the concept of oriented area and the “shoelace formula”, and some equations I found while playing around with it which are not novel. I wanted to write this article because I think the concept deserves to be better popularized, and it is useful to me to have my own reference on the subject.
Several resources I have found on the subject, including Wikipedia, all cite a 1959 text entitled Computation of Areas of Oriented Figures by A.M. Lopshits, which was originally printed in Russian and translated to English by Massalski and Mills, but is apaprently not available online. I did find a copy via university library, so I thought I would summarize its contents in the process in order to make them available to a casual Internet reader.
I also took this chance to practice making beautiful math diagrams. Which went okay, but god is it ever not worth the effort.
1. Oriented Area
Let \(P\) be an \(n\)_sided polygon with vertices labeled \(\{p_1, p_2, \ldots p_n\}\). Sometimes we’ll refer to \(p_n\) as \(p_0\) as well so that the list wraps around, because it makes formulas cleaner.
Here’s a \(P\) with \(n=5\):
The “signed” or “oriented” area of \(P\) is given by the socalled “shoelace formula”:
\[\text{area}(P) = \frac{1}{2} \sum_{i=0}^{n1} p_i \times p_{i+1} \tag{1}\]where the sum wraps around, thanks to \(p_0\) being the same as \(p_n\). Each term in the shoelace formula is a cross product (e.g. \(a \times b = a_x b_y  a_y b_x\)), which gives the area of the triangle \((\mathcal{O}, p_i, p_{i+1})\), where \(\mathcal{O}\) is the origin. It’s called the shoelace formula because when you write all the coordinates in a column and begin to compute the cross products by multiplying \(x_1 y_2  x_2 y_1\), \(x_2 y_3  x_3 y_2\), etc, it looks like a laced shoe:
The fact that this is a “signed area” means that it can be positive or negative. In particular the area is positive if its vertices go counterclockwise, like radians do, and is negative if its vertices go clockwise.
\[\text{area}(P) = \text{area}(\{p_n, p_{n1}, \ldots p_0\}) = \text{area}(P)\]Which direction has which sign is totally arbitrary, and we could have defined it the other way, but it’s handy to keep it consistent with radians. If you ever just want the unsigned area of a region you can always take an absolute value, so the signed area is strictly more powerful than the unsigned version.
Signed/oriented areas are useful because they are betterbehaved than regular areas in several ways. The signed area of a shape is preserved under any decomposition into component shapes, with negativelyoriented components subtracting from the total area:
I’ve indicating that the circle is negatively oriented and thus has negative area with an arrow that says it is to be traversed clockwise. Its area is subtracted from the total area of the rectangle, giving the area of the composite shape automatically.
This becomes useful in more complicated figures, because it lets us build them out of simple parts very cleanly. For example, the shoelace formula works because of the ability to add these oriented areas without having to specify which ones to subtract; it amounts to decomposing a shape into a list of triangles with the origin as the third vertex and adding their areas. This is totally natural if the origin is fully contained within the polygon:
But signed area mean that this construction works even if the origin is outside the polygon, with the triangles overlapping, because their overlapping parts cancel perfectly:
The coordinateinvariance of this formula (that it works regardless of where \(\mathcal{O}\) is) should be enough to motivate it as mathematically valuable. We like formulas that don’t care about specific coordinate systems.
We’re used to dealing with what are called simple polygons – polygons whose sides never overlap, so they surround a single region of space without any intersections. We can also consider nonsimple polygons, which are allowed to have their vertices or edges overlap or intersect. The shoelace formula continues to work if the polygon is nonsimple, except we must understand that negativelyoriented regions subtract from the total sum instead of adding, which may not be totally intuitive:
2. Oriented Angle
Related to the concept of oriented area is the concept of oriented angle, which is actually a bit more familiar. Oriented angles distinguish between “the angle between \(\b{a}\) and \(\b{b}\)” and “the angle between \(\b{b}\) and \(\b{a}\)”, by insisting that we specify counterclockwise angles (the way radians go) as positive:
This is very much like how the vector \(\bf{ba}\) is the negative of the vector \(\bf{ab}\). In fact it is appealing to think of oriented angles as some kind of curved vectors – we can add and subtract ‘angular’ vectors just like regular vectors. In this example, evidently \(\angle \b{ab} + \angle \b{bc} = \angle \b{ac}\), and this angle addition formula holds whether or not \(\b{c}\) falls between \(\b{a}\) and \(\b{b}\):
We want to think of angles as oriented because we can use them in formulas to get oriented areas as results. The cross product of two vectors \(\b{a}, \b{b}\) gives the signed area of the parallelogram \((\b{0}, \b{a}, \b{a+b}, \b{b})\), regardless of their relative orientation:
\[\b{a} \times \b{b} = ab \sin \phi \\ \b{a} \times \b{b} = ab \sin ( \phi) =  ab \sin \phi\]3. The Shoelace Formula in Displacements
The shoelace formula can be massaged into some other forms. Defining \(\b{d}_i\) as the vector displacements of each side^{1}:
\[\b{d}_i = p_{i+1}  p_i\]Which is just the same polygon labelled differently:
Then we can write the area as:
\[\text{area}(P) = \frac{1}{2} \sum p_i \times \b{d}_{i} \tag{2}\]Where \(\times\) is a cross product. Since \(p_i \times (p_{i+1}  p_i) = p_i \times p_{i+1}  \cancel{p_i \times p_i} = p_i \times p_{i+1}\), this is the same as (1). Essentially we are just referring to our triangles differently, by \(p_i\) and \(\b{d}_i\) instead of \(p_i\) and \(p_{i+1}\):
The \(\b{d}_i\) vectors traverse the polygon starting at \(p_0\):
\[p_j = p_0 + \sum_{i = 0}^{j1} \b{d}_i\]They form a closed loop (\(\sum_{i=0}^{n} \b{d}_i = \b{0}\)). Therefore we can eliminate the \(p_i\) terms from the area formula entirely by writing them as sums of displacements (recalling that \(\sum_{i} p_0 \times \b{d}_i\) cancels out because it equals \(p_0 \times \sum_{i} \b{d}_i = p_0 \times \b{0} = 0\)):
\[\begin{aligned} \text{area}(P) &= \frac{1}{2} \sum_{i} (p_0 + \sum_{j < i} \b{d}_j) \times \b{d}_i \\ &=\frac{1}{2} [ \sum_{i} p_0 \times \b{d}_i + \sum_{i}\sum_{j < i} \b{d}_j \times \b{d}_i ]\end{aligned}\] \[\text{area}(P) = \frac{1}{2} \sum_{i}\sum_{j < i} \b{d}_j \times \b{d}_i \tag{3}\]This is the shoelace formula, rewritten to only use the vector displacements of the figure.
4. The Shoelace formula in Angles
Finally, we can write this in terms of just the sidelengths and exterior vertex angles of the polygon.
We may describe the angles of a polygon in several ways. The first is by the interior angle \(\alpha_i\) at vertex \(p_i\), with the stipulation that this is always the angle measured counterclockwise from the first side in our oriented order, so that it is always the interior on positivelyoriented simple polygons:
Interior angles are probably the most intuitive, but they perform less well in equations than the exterior angle at each vertex, which is the angle between the vectors \(\b{d}_{i1}\) and \(\b{d}_i\). Let’s call them \(\theta_i\), so \(\theta_i\) is the exterior angle at the point \(p_i\):
Evidently \(\alpha_i + \theta_i = \pi\). Also, for a simple polygon, the sum of all the angular displacements at every vertex must add up to \(2 \pi \equiv 0\), because the angles make one complete circle.
\[\sum \theta_i = \b{0}\]In a regular \(N\)gon, each exterior angle must be equal, so \(\theta_i = \frac{2 \pi}{N}\), which means the interior angles are \(\alpha = \pi  \frac{2 \pi}{N}\) and the sum of the interior angles is \(N \alpha = N(\pi  \frac{2 \pi}{N}) = \pi (N2)\).
The area of a regular \(N\)gon is created from \(2N\) copies of the right triangle that has \(\frac{\alpha}{2}\) as one of its angles, which gives various formulas for computing their area depending on what lengths you have:
We can see that these relationships should hold, in case you’d like to go calculating some values from the others:
\[\begin{aligned} \frac{L}{2} &= r \cos \frac{\alpha}{2} \\ s &= r \sin \frac{\alpha}{2} \\ s &= \frac{L}{2} \tan \frac{\alpha}{2} \\ \text{perimeter} &= NL = 2 n r \cos \frac{\alpha}{2} \\ \text{area} &= \frac{1}{2} NsL \\ \text{area} &= \frac{1}{2} s \cdot \text{perimeter} \end{aligned}\]For a simple, positivelyoriented polygon, the exterior angles add up to exactly \(2 \pi\) radians. For nonsimple polygons they may add up to any multiple of \(2 \pi\) depending on how many clockwise or counterclockwise loops there are:
Each adjacent displacement vector \(\b{d}_i\) differs from the previous vector \(\b{d}_{i1}\) by the exterior angle between them \(\theta_i\).
Therefore we can find the angle between any two displacement vectors \(\b{d}_i, \b{d}_j\) by adding up all the exterior angles between them: (with the sum wrapping around if need be, and with addition be modulo \(2 \pi\)):
\[\theta_{ij} = \sum_{i \leq k < j} \theta_k\]We can use this in \((3)\) to get a version of the area formula expressed only in lengths and exterior angles:
\[\begin{aligned} \text{area}(P) &= \frac{1}{2} \sum_{i}\sum_{j < i} \b{d}_j \times \b{d}_i \\ &= \frac{1}{2} \sum_i \sum_{j < i}  \b{d}_j   \b{d}_i  \sin (\theta_{ij}) \\ \end{aligned}\] \[\text{area}(P) = \frac{1}{2} \sum_i \sum_{j < i}  \b{d}_j   \b{d}_i  \sin (\theta_{ij}) \tag{4}\]By labeling the side lengths \(\ \b{d}_i \\) as \(a_{i+1}\) and expanding the sum over \(i\) before \(j\), we can get to a form which which is presented on Wikipedia:
\[\begin{aligned} \text{area}(P) &= \frac{1}{2} (a_1 [a_2 \sin (\theta_1) + a_3 \sin (\theta_1 + \theta_2) + \ldots a_{n1} \sin(\theta_1 + \theta_2 + \ldots + \theta_{n1}) ] \\ &+ a_2 [ a_3 \sin (\theta_2) + a_4 \sin (\theta_2 + \theta_3) + \ldots + a_{n1} \sin (\theta_2 + \theta_3 + \ldots + \theta_{n1})] \\ &+ \dots + a_{n2} \sin (\theta_{n1})) \tag{5} \end{aligned}\]This and (4) are two ways of expressing the same idea: the area of a polygon in terms of scalar lengths and angles. I am not sure when you would ever want to use these, though – these loops have \(O(N^2)\) steps in them, while the original formula (1) involved only \(N\).
5. A Summary of Lopshits
As mentioned in the intro, the Wikipedia article on polygons sources formula (5) from Computation of Areas of Oriented Figures by A.M. Lopshits, published 1959. It turns out some other people have chased that link and also cited this text, but I could not get my hands on a .pdf version, so I got it from the university library.
If you are curious what it contains, here’s an outline. The short version is: not much. I wanted more formulas and ideas in the vein of (5), but much deeper. Turns out, though, that’s it’s a mostly pedagogical text that reaches that formula as its final result after spending 40 pages on the concept of oriented area and related (elementary) geometric proofs.
Outline of Computation of Areas of Oriented Figures by AM Lopshits
Chapter 1. Measurement of the Area of an Oriented Figure
 Oriented triangles are a lot like regular triangles, but oriented.
 Oriented triangles have oriented areas.
 The simplest way to see that this might be useful is this:

Consider a triangle \(ABC\). If \(A'\) is a point on the line segment \(BC\), then \(\text{area}(ABC) = \text{area}(A'AB) + \text{area}(A'CA)\).

If we allow ourselves oriented triangles and areas, this remains true even if \(A'\) is a point on the line \(\overrightarrow{BC}\), but outside the triangle:
 Lopshits makes all of these points arduously, in multiple theorems with elaborate proofs. I suspect the midcentury Russians loved proving things arduously.
 The area of an oriented triangle can be calculate using the shoelace formula for any choice of origin \(\mathcal{O}\).
 this is carefully proven using previous theorems.
 Oriented polygons are oriented collections of points. The shoelace formula gives their area for any choice of \(\mathcal{O}\).
 this is also carefully proven using previous theorems.

Some examples and exercises. The most interesting set is like this (for \(N=8,12,20\)):
“A regular dodecagon \(A_1A_2 \ldots A_{12}\) is inscribed in a circle. The polygon \(A_1 A_6 A_5 A_{10} A_9 A_2\) has three points of selfintersection, \(C_1\), \(C_2\), \(C_3\). Prove that the area of the triangle \(C_1 C_2 C_3\) is three times the area of the triangle \(A_1 A_2 C_1\).”
Chapter 2. The Planimeter
 This chapter entirely describes the workings of a device called the planimeter, which is used to measure [signed] areas of printed curves via, essentially, the shoelace formula acting on a polygonal approximation.
I do not care about planimeters, though I’m sure this was interesting in 1959.
In fact it turns out there are multiple kinds of planimeters out there, each of which the reader is invited to deeply contemplate.
There is one theorem I found interesting, though: 
“Imagine a directed segment \(\overrightarrow{AB}\) in the plane. Let us move it around the plane, finally bringing it back to its original position. In this motion the end \(A\) and \(B\) will of course trace out closed curved \(L_A\) and \(L_B\). Also, \(\overrightarrow{AB}\) will sweep out an oriented area \(S\).”
\[\text{area}(S) = \text{area}(L_A)  \text{area}(L_B)\]
Theorem:This is simple to convince yourself of – as oriented areas, we must have \(\text{area}(S) = \text{area}(L_A) + \text{area}(L_B)\), and one of the areas must be oppositelyoriented so we may pick \(\text{area}(L_B)\) to be negative.
Chapter 3. Computation of the Area of a Polygon Useful in Surveying
 This section primarily produces my formula (5). The argument for its use in surveying is that the formula uses only scalar lengths and angles, meaning that one can find the area of unwieldy regions (say, a plot of land) by measuring all the lengths and angles individually and then computing the result.
 But before getting to that formulation, Lopshits introduces the concept of vectors. I am not understanding what level this text is meant for.
 Derives of equation (4), though without summation formulas.
 Oriented angles work better than regular ones for this calculation.
 Lot of words about computing what I have called \(\theta_{ij}\).
 Derivation of equation (5), for which Lopshits was cited on Wikipedia.
And that’s pretty much… it. I’m a little disappointed.
5. The Shoelace Formnula as an Area Integral
It’s worth discussing how the shoelace formula is related to integral calculus. After all, if anything is the ‘canonical’ way to calculate area, it’s an area integral: \(\text{area}(P) = \iint_P 1 \, dx dy\).
Green’s theorem says how we can translate an area integral over a region into an integral over its boundary. Specifically, it says that an area integral over a region is equivalent to certain a line integral around the boundary of the region. If \(L\) and \(M\) are functions with continuous partial derivatives in a region \(D\) bounded by an (oriented!) curve \(C\), then:
\[\oint_C L \, dx + M \, dy = \iint [ \p_x M  \p_y L ] dx dy\]For the simple case of \(\iint 1 \, dx dy\), we just need to find any \(L,M\) which have \(\p_x M  \p_y L = 1\). This is easily done with either \(M(x,y) = x\), \(L(x,y) = y\), or any combination of them, like \(\frac{1}{2} (M + L)\). Therefore each of these gives an integral for area:
\[\begin{aligned} \text{area}(D) &= \iint_D 1 \, dx dy \\ &= \oint_C x dy \\ &= \oint_C y dx \\ &= \frac{1}{2} \oint_C x \, dy  y \, dx = \frac{1}{2} \oint_C (x,y) \times (dx, dy) \end{aligned}\]The connection is this:
Suppose we are computing \(\frac{1}{2} \oint_P (x,y) \times (dx, dy)\), ie, computing a line integral around the boundary of one of the oriented polygons \(P\) from before. Then, for the entire segment of the integral along the side \(p_i p_{i+1}\), the tangent direction is parallel to \(\b{d}_i\). Obviously these sums of ‘infinitesimal’ triangles along the side should add up to give the finitesized triangle area of \(p_i \times \b{d}_i\):
To be a bit more explicit, we can parameterize the curve along a side \(p_i p_{i+1}\) by \(t \in [0,1]\), so that \((x,y) = p_i + t \b{d}_i\). Then \((dx, dy) =\b{d}_i dt\), and:
\[\int_{p_i}^{p_{i+1}} (x,y) \times (dx, dy) = \int_0^1 (p_i + t \b{d}_i) \times \b{d}_i \, dt = p_i \times \b{d}_i\]Adding the contributions from every side give:
\[\frac{1}{2} \oint_C (x,y) \times (dx,dy) = \frac{1}{2} \sum_i p_i \times \b{d}_i\]While we’re at it, maybe we can come up with an ‘integral form’ of (3) or (4). Naively, it should look something like this, right?
\[\text{area}(D) = \frac{1}{2} \sum_i \sum_{j < i} \b{d}_j \times \b{d}_i \Lra \frac{1}{2} \int_0^1 \int_0^t \; \dot{\vec{\gamma}}(s) \times \dot{\vec{\gamma}} (t) \, ds \, dt\]Where we parameterize the curve \(C\) enclosing our region as \(\vec{\gamma}(t)\) for \(t \in [0, 1]\).
This is just taking the integral formula \(\frac{1}{2} \oint_C \vec{\gamma}(t) \times \dot{\vec{\gamma}}(t) dt\) and replacing \(\vec{\gamma}(t)\) with \(\int_0^t \dot{\vec{\gamma}}(s) ds\), which should be fine as long as [mumble]. If we separate \(\vec{\gamma}(t)\) into \(r(t)\) and \(\theta(t)\) we should get a version of (5), also.
6. The Shoelace Formula in Higher Dimensions
The fact that the shoelace formula is a consequence of Green’s theorem means that there should be a way to take it to higher dimensions. Green’s theorem readily generalizes to Stoke’s Theorem in arbitrary spaces, which says that the integral of a function (or differential form) over a closed surface can be equated to the integral of its derivative through the enclosed volume:
\[\oint_{S} f = \int_V df\]This means that:
 the shoelace formula should continue to calculate the areas of oriented polygons in \(N>2\) dimensions
 there should be an analog to the shoelace formula for computing volumes, 4volumes, etc
For now I will mention how to calculate area in 3D, because it looks a little different than in 2d.
Area of a figure in 3d: Suppose we want to compute the area of the triangle:
\[T = (t_1,0,0), (0,t_2,0), (0,0,t_3)\]We can compute the shoelace answer:
\[\begin{aligned} \text{area}(T) &= \frac{1}{2} \big[ (t_1,0,0) \times (0,t_2,0) + (0,t_2,0) \times (0,0,t_3) + (0,0,t_3) \times (t_1,0,0) \big] \\ &= \frac{1}{2} \big[ (0,0,t_1 t_2) + (t_2 t_3, 0, 0) + (0, t_3 t_1, 0)\big] \\ &= \frac{1}{2} (t_2 t_3, t_3 t_1, t_1 t_2) \end{aligned}\]But that’s… not a scalar. What went wrong?
The answer is that this the area of \(T\) represented as a vector, which is normal to the plane of \(T\). It tells you more than the area – it also tells you what direction the area faces. To get to the scalar area, though, you have to take its magnitude:
\[\text{area}(T) =  \frac{1}{2} (t_2 t_3, t_3 t_1, t_1 t_2)  = \frac{1}{2} \sqrt{t_2^2 t_3^2 + t_3^2 t_1^2 + t_1^2 t_2^2}\]But you lose the sign when taking the magnitude – the direction of that vector was what was telling us the orientation. In 3D, it’s meaningless to say that a surface is ‘positively’ oriented – what if it’s orthogonal to the \(XY\) plane? Should that be positive or negative? What if we flip it over? Orientation cannot be an intrinsic property of a shape if it changes as we rotate things!
Instead we can just talk about how things are oriented relative to each other. A figure on the \(XY\) plane is either oriented in the \(\b{z}\) direction or the \(\b{z}\) direction. In 2D we drop the \(\b{z}\)’s and just call this positive and negative. In 3D, though, volumes have an absolute concept of orientation, but in 4D they would not – they would just end up oriented to the, say, \(+\b{w}\) or \(\b{w}\) axes!
The shoelace formula used on a general polygon in 3D will work the same way, giving a vector. Note that this does require the polygon to be already known to be planar, or the answer you get will be meaningless.
By the way – if you want to compute the surface area of a figure by summing the areas of each side, go right ahead, but make sure you normalize each side’s area to be positive first. The surface area vectors of a closed figure in 3D will always cancel out to \(\b{0}\). (Why? Because of Stoke’s theorem: \(\iint_{S} 1 \, d\b{S} = \iiint_V d(1) dV = 0\).)
As for volumes – that will have to wait for another article. Higherdimensional shapes like polyhedra are much harder to deal with than polygons, primarily because it’s just hard to represent them. It’s not enough to supply a list of vertices \(\{p_i\}\), because you also needs to specify which sets of vertices make each face, and to ensure that the faces are oriented consistently: if the edge \(\overrightarrow{uv}\) appears on one face, then \(\overrightarrow{vu}\) must appear on the other face that shares that edge.
I will say only that if you do have a list of oriented polygonal faces \(\{F_i\} = \{\{f_{ij}\}\}\), then the volume of the pyramid created by a face with the origin is given by
\[\begin{aligned} \text{volume}(F_i) &= \frac{1}{6} f_{i0} \cdot \sum_j f_{ij} \times f_{i,j+1} \\ &= \frac{1}{3} f_{i0} \cdot \text{area}(F_i) \end{aligned}\](\(f_{i0}\) just has to be any point on the face – we’re summing the volumes of the tetrahedrons \((\mathcal{O}, f_{i0}, f_{ij}, f_{i,j+1})\), and we need any third point in order to turn areas into volumes. \(\text{area}(F_i)\) in this case is the area vector, not the scalar.)
Then the volume of the whole figure is given by:
\[\text{volume}(F) = \sum \text{volume}(F_i)\]7. Conclusion
That’s all I’ve got, for now. Hope it’s useful, interesting, or otherwise not a total waste of time. I hope to revisit higherdimensional shoelacetype formulas at some point, but it’ll have to wait. This was exhausting (well, making it pretty was).
I made the diagrams using Tikz, which is what all those fancy diagrams in LaTeX documents and textbooks are made in, and it was a lot of work but I’m glad I’ve started to learn how to do it. The process of conveniently importing Tikz images into websites, though, is … not enjoyable. I would be so excited if there was a project like Mathjax that extracted a subset of Tikz for inline webdocument creation.

I like to use boldface \(\b{d}\) to refer to things that are definitely vectors, so adding them together is meaningful, as opposed to points \(p_i\) which cannot be meaningfully added. ↩