Using weight functions in spatial point pattern analysis with application to plant ecology data

Lai Ping Ho, Sung Nok Chiu

Research output: Contribution to journalArticlepeer-review

Abstract

A very common way of analyzing different and complicated plant behaviors is to use spatial point pattern analysis, which allows us to assess whether there is any structure present. To test the complete spatial randomness hypothesis, Diggle (1979Diggle , P. J. ( 1979 ). On parameter estimation and goodness-of-fit testing for spatial point patterns . Biometrics 35 : 87 – 101 .[CrossRef], [Web of Science ®], [Google Scholar]) proposed a Monte Carlo test whose test statistic is the discrepancy between the estimated and the theoretical form of some summary function, such as the Ripley K-function. In this article, we improve this test by adding various weight functions and get more powerful tests if decreasing and increasing weight functions are used for processes with short and long, respectively, range of interaction.

Original languageEnglish
Pages (from-to)269-287
JournalCommunications in Statistics - Simulation and Computation
Volume38
Issue number2
DOIs
Publication statusPublished - Jan 2009

User-Defined Keywords

  • Complete spatial randomness
  • Edge-correction
  • K-function
  • Monte Carlo simulation

Fingerprint

Dive into the research topics of 'Using weight functions in spatial point pattern analysis with application to plant ecology data'. Together they form a unique fingerprint.

Cite this