Gezer, F orcid.org/0000-0002-8287-2763, Aykroyd, RG orcid.org/0000-0003-3700-0816 and Barber, S orcid.org/0000-0002-7611-7219 (2021) Statistical properties of Poisson-Voronoi tessellation cells in bounded regions. Journal of Statistical Computation and Simulation, 91 (5). pp. 915-933. ISSN 0094-9655
Abstract
Many spatial statistics methods require neighbourhood structures such as the one determined by a Voronoi tessellation, so understanding statistical properties of Voronoi cells is crucial. While distributions of cell properties when data locations follow an unbounded homogeneous Poisson process have been studied, little attention has been given to how these properties change when a boundary is imposed. This is important when geographical data are gathered within a restricted study area, such as a national boundary or a coastline. We study the effects of imposing a boundary on the cell properties of a Poisson Voronoi tessellation. The area, perimeter and number of edges of individual cells with and without boundary conditions are investigated by simulation. Distributions of these properties differ substantially when boundaries are imposed, and these differences are affected by proximity to the boundary. We also investigate how changes in such properties when boundaries are imposed vary over two-dimensional space.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of a paper published in Journal of Statistical Computation and Simulation. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Voronoi tessellation; Poisson point process; bounded regions; spatial statistics; generalized gamma distribution |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2020 13:34 |
Last Modified: | 05 Jul 2022 07:54 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/00949655.2020.1836184 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166810 |