The Annals of Probability

From random lines to metric spaces

Wilfrid S. Kendall

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Consider an improper Poisson line process, marked by positive speeds so as to satisfy a scale-invariance property (actually, scale-equivariance). The line process can be characterized by its intensity measure, which belongs to a one-parameter family if scale and Euclidean invariance are required. This paper investigates a proposal by Aldous, namely that the line process could be used to produce a scale-invariant random spatial network (SIRSN) by means of connecting up points using paths which follow segments from the line process at the stipulated speeds. It is shown that this does indeed produce a scale-invariant network, under suitable conditions on the parameter; in fact, it then produces a parameter-dependent random geodesic metric for $d$-dimensional space ($d\geq2$), where geodesics are given by minimum-time paths. Moreover, in the planar case, it is shown that the resulting geodesic metric space has an almost everywhere unique-geodesic property that geodesics are locally of finite mean length, and that if an independent Poisson point process is connected up by such geodesics then the resulting network places finite length in each compact region. It is an open question whether the result is a SIRSN (in Aldous’ sense; so placing finite mean length in each compact region), but it may be called a pre-SIRSN.

Article information

Ann. Probab., Volume 45, Number 1 (2017), 469-517.

Received: March 2014
Revised: April 2014
First available in Project Euclid: 26 January 2017

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60D05: Geometric probability and stochastic geometry [See also 52A22, 53C65]
Secondary: 90B15: Network models, stochastic 90B20: Traffic problems 46E35: Sobolev spaces and other spaces of "smooth" functions, embedding theorems, trace theorems

Fiber process line-space Lipschitz path marked line process orientation field perpetuity Poisson line process pre-SIRSN random upper-semicontinuous function $\Pi$-geodesic $\Pi$-path scale invariance SIRSN Sobolev space spatial network stochastic geometry weak SIRSN


Kendall, Wilfrid S. From random lines to metric spaces. Ann. Probab. 45 (2017), no. 1, 469--517. doi:10.1214/14-AOP935.

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