Balloons in scattered across a morning sky. Sand grains strewn on the ground. Seeds blown over a forest floor. Each of these phenomena can be represented mathematically as an object called a point process or random point field. Although it has a couple of mathematical interpretations, a point process is essentially just a collection of points randomly scattered on some mathematical space*, such as the real line, the Cartesian plane, a sphere, or more abstract spaces.
*The underlying mathematical space is sometimes called the carrier space or the state space, but the second term refers to something different from the state space used in the theory of stochastic processes.
The most important point process is the Poisson point process, which is one of the two most fundamental and studied mathematical objects in probability. (The other is the Wiener process or Brownian motion process, which is a type of random process or stochastic process, and it has been suggested by mathematicians such as John Kingman that the Poisson point process does not attract as much research attention as it should.) This point process can be defined on very general mathematical spaces, but usually the plane gives sufficient intuition. In this setting, each randomly located point can represent, for example, a star, a sand grain or a seed.
The most important defining property of the Poisson point process is that the numbers of points of the point process located in two (or more) non-overlapping (that is, disjoint) regions are two or more independent random variables. This property, sometimes called independent scattering or complete independence, explains the tremendous tractability of this point process, and it is used alongside the property that the random variables have Poisson distributions to define the Poisson point process.
To define a Poisson point process on some mathematical space, only a single mathematical object is needed. This object is applied to a region (or subset) of the underlying space on which the Poisson point process is defined, and returns a non-negative number. This object is a type of measure from measure theory, so it is called the mean measure or intensity measure. The mean measure can be interpreted as the mean or average number of points of a Poisson point process being located in a region of the underlying space.
A point process \(N\) defined on some underlying space \(S\) is a Poisson point process with intensity measure \(\Lambda\) if it has the two following properties:
1 The number of points in a bounded Borel set \(B \subset S\) is a Poisson random variable with mean \(\Lambda(B)\).
2 The number of points in \(n\) disjoint Borel sets forms \(n\) independent random variables.
A simple example of a mean measure of a Poisson point process is when the mean measure is given by the product of a non-negative constant and the area or volume of the region. The constant, often denoted by \(\lambda\), is known as the intensity or rate, which is often can be interpreted as the average density of points. In this setting, the average density does not vary over the underlying space, so the resulting point process is called a homogeneous Poisson point process or uniform Poisson point process, which is the simplest example of a Poisson point process.
If the intensity does change over the underlying space, meaning it is spatially dependent, then the terms inhomogeneous Poisson point process or nonhomogeneous Poisson point process are used. It is usually assumed that the intensity measure \(\Lambda\) has a derivative \(\lambda\), so it can be written as an integral:
$$\Lambda(B)=\int_B \lambda(x) dx, $$
where the set \(B\) is some subregion of the underlying state space \(S\). (As per standard probability assumptions, the set \(B\) has to be Borel measurable, but we do not focus on such details here.)
The Poisson point process is the cornerstone of fields where randomness meets geometry, such as spatial statistics, geometric probability and stochastic geometry. Researchers, scientists, and engineers have proposed using the Poisson point process to model various objects randomly positioned. In recent years, it has been used extensively to mathematically model the locations of transmitters and receivers in wireless communication networks such as cellular or mobile phone networks.
As a mathematical model, the Poisson point process should be used to represent objects or phenomena that have little or, ideally, zero interaction among the points. If that’s not the case, then the Poisson point process can also serve as a null-hypothesis model in statistics, whose rejection implies there is sufficiently strong interaction among the points. For example, the stars influence each other, undoubtedly, through gravity, and trees rely upon absorbing water in their vicinity through root systems, suggesting that non-Poisson models would be more suitable for representing these two examples. Other more sophisticated point processes that incorporate such point interaction have been developed. Many of these point processes build off the Poisson point process.
The Poisson point process is often called simply the Poisson process, where it can be confused with the related stochastic process of the same name. This Poisson process is a continuous-time discrete-valued stochastic process. The points in time where this stochastic process changes (or jumps) form the points of a Poisson point process on the real line. Depending on the literature, interpretation and preference, the Poisson point process is also called the Poisson random field and Poisson random measure.
The Poisson point process is a highly useful and used random object. But we now need to simulate it on a computer, which will be the subject of a future post.
The Wikipedia article is a good starting point. The best book on the Poisson point process is the monograph Poisson processes by Kingman. A recent and more theoretical book is Lecture Notes on the Poisson Process by Last and Penrose (the manuscript is freely available online here). More applied books include the classic Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Mecke, and Statistical Inference and Simulation for Spatial Point Processes by Møller and Waagepetersen.
In recent years, there have been various books applying the Poisson point process to wireless networks, which include the two-volume Stochastic geometry and Wireless Networks by Baccelli and Błaszczyszyn, and Stochastic geometry for Wireless Networks by Haenggi. Much more recently, some colleagues and I contributed to this literature with Stochastic Geometry Analysis of Cellular Networks by Błaszczyszyn, Haenggi, Keeler, and Mukherjee.