# Mean distance between 2 points in a ball

I have found an answer on this site to the question of determining the mean straight-line distance between 2 randomly chosen points in a disc of radius r. (See Average distance between two points in a circular disk) I'm now trying to find an answer to the same question except involving a ball of radius r rather than a disc. Any guidance on this question would be appreciated.

• Most of the times (i am pretty sure in this case too) these proofs generalize straight forward to higher dimensions. Can you point out your solution for the disk? Do you understand it and did you already try on your own to generalize it? Jul 7 '12 at 17:56
• It should be much easier on a sphere: Due to symmetry, you may as well keep one point fixed, say the south pole of the sphere. Rescaling so the radius is 1 (just multiply the answer by $r$ afterwards) and assuming the sphere is centered at the origin, the distance from a given point on the sphere to the south pole will be $\sqrt{x^2+y^2+(z+1)^2}=\sqrt{2+2z}$. This should be straightforward to integrate over the sphere. (I just now noted the word “within” in the question title. Are you looking at points in the ball rather than the sphere?) Jul 7 '12 at 17:57
• @HaraldHanche-Olsen. I've found solutions to similar questions involving points on a circle and points on the surface of a sphere. But now my question involves points within a sphere. Yes, sorry, the ball might be the better word. Jul 7 '12 at 18:01
• @Listing, if the random simplex volume problem is an indicator, there's no easy way to generalize to higher dimensions. For example already the expression for the area of a random triangle (ie, with vertices independently and uniformly distributed in the unit square) is significantly more complex than the length of a random line segment in [0,1], to say nothing about the volume of a random tetrahedron. Jul 7 '12 at 18:18
• Using the two points and the center of the ball, you could form a plane on which is a disc with the two points in question and thus reduces to the same problem. Though there's the issue if the 3 points are co-linear... kind of an edge case to iron out EDIT: actually just choose any 3rd point in ball such that its not co-linear with the 2 random points of interest, from that you can get your plane, don't have to use the center...
– Russ
Jul 7 '12 at 18:22

(a) Two random points on the unit sphere $S^2$:

Assume the first point at the north pole ($\theta=0$) of $S^2$. Then the distance to a point at latitude $\theta$ is $2\sin{\theta\over2}$. Therefore the mean distance between the north pole and the second point is given by $${1\over 4\pi}\int_0^\pi 2\sin{\theta\over2}\cdot 2\pi\sin\theta\ d\theta={4\over3}\ .$$ 

(b) Two random points in the unit ball of ${\mathbb R}^3\$:

Let ${\bf X}$ and ${\bf Y}$ be the two random points. Then $R:=|{\bf X}|$, $\ S:=|{\bf Y}|$, and $\Theta:=\angle({\bf X},{\bf Y})$ are independent random variables with densities $$f_R(r)=3r^2\quad (0\leq r\leq 1)\ ,\qquad f_S(s)=3s^2\quad(0\leq s\leq 1)\ ,$$ and $$f_\Theta(\theta)={1\over2}\sin\theta\quad(0\leq\theta\leq\pi)\ .$$ (Concerning $f_R$ and $f_S$ note that the volume included between $r$ and $r+dr$ is proportional to $r^2$. For $f_\Theta$ you may assume ${\bf X}$ pointing due north. The abstract surface area between $\theta$ and $\theta+d\theta$ is then proportional to $\sin\theta$, as in (a).)

It follows that the mean distance $\delta$ between ${\bf X}$ and ${\bf Y}$ is given by $$\delta=\int_0^1\int_0^1\int_0^\pi \sqrt{r^2+s^2-2rs\cos\theta}\ f_R(r) f_S(s)f_\Theta(\theta) d\theta\ ds\ dr\ .$$ The innermost integral computes to \eqalign{{1\over2}\int_0^\pi \sqrt{r^2+s^2-2rs\cos\theta}\ \sin\theta\ d\theta&={1\over 6rs}\bigl(r^2+s^2-2r s\cos\theta\bigr)^{3/2}\Biggr|_0^\pi \cr &={1\over 6rs}\bigl((r+s)^3-|r-s|^3\bigr)\ . \cr} In the sequel we assume $s\leq r$ and compensate this by a factor of $2$. We are then left with $$\delta=\int_0^1\int_0^r 3r s(6r^2 s +2s^3)\ ds\ dr={36\over35}\ .$$ It should not be too difficult to set a similar computation up that is valid for a ball in any ${\mathbb R}^n$, $\ n\geq 2$.

• Yes, @ChristianBatter, that was the value I got. Your solution is much more concise than mine, so thank you for providing it. For the similar problem of two points on a circle of radius 1 I found that the average distance is 4/pi, which makes me wonder if this upward trend continues as we continue to go up in dimensions, and whether or not there is some limit as n -> infinity. Jul 7 '12 at 22:29
• Perfect! Thank you so much. Jul 8 '12 at 17:24

The average distance can be calculated as a triple integral in polar coordinates:

We ran into it some years back when doing research on proteins (http://www.ncbi.nlm.nih.gov/pubmed/9514112).

(a) UNIT SPHERE

Let us recall the Laplace's multipole expansion formula into Legendre polynomials: $$\frac{1}{|\vec{r}'-\vec{r}|}=\frac{1}{r}\sum_{n=0}^\infty \left(\frac{r'}{r}\right)^n P_n(Y)\qquad ;r\geq r',$$

where I have denoted $$Y:=\hat{r}\bullet\hat{r}'$$. If we assume that $$r=r'=1$$, then $$\frac{1}{|\hat{r}'-\hat{r}|}=\sum_{n=0}^\infty P_n(Y)$$

The average distance between two uniformly distributed points on the unit sphere we denote as $$\bar{L}$$. Since $$|\vec{r}'-\vec{r}|=(\vec{r}'-\vec{r})^2/|\vec{r}'-\vec{r}|=(r'^2+r^2-2rr'Y)/|\vec{r}'-\vec{r}|,$$

we have for $$r=r'=1$$: $$|\hat{r}'-\hat{r}|=2(1-Y)/|\hat{r}'-\hat{r}|$$

and via Laplace's expansion therefore

$$\bar{L} = \mathbb{E}\left[|\hat{r}'-\hat{r}|\right]=\frac{1}{(4\pi)^2}\oint \oint |\hat{r}'-\hat{r}| d\Omega' d\Omega=\frac{1}{8\pi^2}\sum_{n=0}^\infty\oint \oint \left(1-Y\right) P_n(Y) d\Omega' d\Omega.$$

Due to orthogonality, we know as well that $$\oint P_n(Y)P_m(Y) d\Omega = \frac{4\pi}{2n+1} \delta_{nm}.$$

Since $$1=P_0(Y)$$ and $$Y = P_1(Y)$$, we immediately get

$$\bar{L} = \frac{1}{8\pi^2}\sum_{n=0}^\infty\oint \oint \left(1-Y\right) P_n(Y) d\Omega' d\Omega = \frac{1}{2\pi} \oint \left(1-\frac{1}{3}\right) d\Omega = \frac{1}{2\pi}\frac{2}{3}4\pi = \frac{4}{3}$$

(b) UNIT BALL

The average distance between uniformly distrubuted two random points in the unit ball is given by$$\bar{L} = \mathbb{E}\left[|\vec{r}'-\vec{r}|\right]$$. Since the problem is symmetric with respect of labeling of the two points and changing their place, we can define a random variable $$L' := |\vec{r}'-\vec{r}|\theta(r-r')$$ which vanishes when $$r'>r$$. Clearly, we miss exactly half of the points, therefore

$$\bar{L} = 2\bar{L}' = 2\mathbb{E}\left[|\vec{r}'-\vec{r}|\theta(r-r')\right]=2\left(\frac{3}{4\pi}\right)^2\int \int |\vec{r}'-\vec{r}|\theta(r-r') dV' dV$$,

which can be expanded by the same trick as before. One gets $$\bar{L} = \frac{9}{8\pi^2}\int_0^1 \int_0^r \oint \oint \left(r'^2+r^2-2rr'Y\right) \frac{1}{r} \sum_{n=0}^\infty \left(\frac{r'}{r}\right)^n P_n(Y) r^2r'^2 d\Omega' d\Omega dr' dr$$

Due to orthogonality, we get $$\bar{L} = 18\int_0^1 \int_0^r\frac{1}{r}\left(r'^2+r^2-\frac{2}{3}rr'\frac{r'}{r}\right) r^2r'^2 dr'dr = 18\int_0^1 r^6\left(\frac{1}{5}+\frac{1}{3}-\frac{2}{3}\frac{1}{5}\right)dr = \frac{36}{35}$$

(c) BALL DISTRIBUTION FUNCTION

Denote the probablity density of $$L$$ as a function $$g(\lambda)$$, for this function one has $$g(\lambda) = \left(\frac{3}{4\pi}\right)^2\int\int \delta\left(|\vec{r}'-\vec{r}|-\lambda\right)dV'dV.$$

We expand the $$\delta(L-\lambda)$$ as a sum of Legendre polynomials. Note that $$\delta(L-\lambda) \neq 0$$ only when $$|r'-r|<\lambda since $$|\vec{r}'-\vec{r}|\in (|r'-r|,r'+r)$$. Since $$L = |\vec{r}'-\vec{r}|=\sqrt{r'^2 + r^2 - 2rr'Y}$$ and $$\delta(f(x))=\delta(x-x_0)/|f'(x_0)|$$ with $$f(x_0)=0$$, we get

$$\delta(L-\lambda) = \frac{\sqrt{r'^2 + r^2 - 2rr' Y}}{rr'}\delta\left(Y-\frac{r'^2+r^2-\lambda^2}{2rr'}\right)=\frac{\lambda}{rr'}\delta\left(Y-\frac{r'^2+r^2-\lambda^2}{2rr'}\right).$$

Without loss of generality, we assume $$r' (symmetry), $$r-r'<\lambda (vanishment), then

$$\delta(L-\lambda) = \sum_{n=0}^\infty A_n P_n(Y)$$

Integrating with $$P_m(Y)$$ over $$d\Omega$$ and using the orthogonality relation, we get

$$A_n = \frac{2n+1}{2} \int_{-1}^1 \delta(L - \lambda) P_n(Y) dY = \frac{2n+1}{2} \int_{-1}^1 \frac{\lambda}{rr'}\delta\left(Y-\frac{r'^2+r^2-\lambda^2}{2rr'}\right) P_n(Y) dY,$$

therefore for $$|r-r'|<\lambda: $$\delta(L-\lambda)= \frac{\lambda}{2rr'} \sum_{n=0}^\infty (2n+1)P_n\left(\frac{r'^2+r^2-\lambda^2}{2rr'}\right)P_n(Y).$$

For the probability density therefore, with help of symmetry $$g(\lambda) = 2\lambda\left(\frac{3}{4\pi}\right)^2 \sum_{n=0}^\infty \underset{0

which is due to orthogonality (only $$n=0$$ survives) $$g(\lambda) = 9\lambda \int_{\lambda/2}^{1} \int_{\lambda-r}^{r}\!\! r'r\, dr' dr = \frac{9}{2}\lambda^2 \int_{\lambda/2}^{1} \!\! 2r^2\!-\!\lambda r\, dr = \frac{3}{16}\lambda^2 (2-\lambda)^2(4+\lambda)$$

This isn't a complete answer, but a start, a rough one at that.

Given two points $p_0$ and $p_1$ find a third point $p_2$ such that $N=(p_0-p_2)\times(p_1-p_2) \neq \varnothing$ Where $\varnothing$ is the null vector. Define $n = \frac{N}{|N|}$ that is normalize the vector $N$, without normalization the distance won't be correct below.

Now we have a plane where $p$ is any point on it defined as: $$n\cdot(p-p_2)=0 \\ n\cdot p-n\cdot p_2=0 \\ n\cdot p = n\cdot p_2\\$$ Recall the general equation of a plane is $ax+by+cz=d$ where $d$ is the distance from the origin to the plane. Expanding out the above equation gives us $$a=n_x,b=n_y,c=n_z \\ d=n_xp_{2x}+n_yp_{2y}+n_zp_{2z}$$ Radius of the resulting circle in the plane with a sphere of radius $r$ is given as $R = \sqrt{r^2-d^2}$ You may want to negate $n$ if $d$ is negative as that will allow you to have the center of the circle at $(da,db,dc)$

We haven't defined an axes for our plane so its difficult to map from our 3D points onto it. This is where I'm stopping for now. (I might come back and finish this up) I have a feeling this approach is more of a hassle than just computing $p_0-p_1$ and calculating its length. Namely because we have to normalize a vector ($N$) just to get a distance and thus the radius.

Wiki pages I used for this: Plane and Plane-sphere intersection

• It's not clear that restricting to this plane is going to help any. If the points are chosen uniformly in the original ball, they won't be uniformly distributed when we look only at the plane they define. (Intuitively, the problem is that the possible planes are "closer together" near the center at the ball, so an inifinitesimal area of each plane will be represent less of an infinitesimal volume than a similar area closer to the periphery). Jul 7 '12 at 21:30
• Yeah when I set out to write this answer I thought you just needed to use the same method for a disc but in the ball. I should have noticed the probability distributions tag and that you are going to have a large number of points
– Russ
Jul 7 '12 at 22:34