Non-Parametric Analysis of Spatial and Spatio-Temporal Point Patterns

Abstract

The analysis of spatial and spatio-temporal point patterns is becoming increasingly necessary, given the rapid emergence of geographically and temporally indexed data in a wide range of fields. Non-parametric point pattern methods are a highly adaptable approach to answering questions about the real-world using complex data in the form of collections of points. Several methodological advances have been introduced in the last few years. This paper examines the current methodology, including the most recent developments in estimation and computation, and shows how various R packages can be combined to run a set of non-parametric point pattern analyses in a guided and intuitive way. An example of non-specific gastrointestinal disease reports in Hampshire, UK, from 2001 to 2003 is used to illustrate the methods, procedures and interpretations.

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

“…The outcomes of this investigation align with the findings of previous studies conducted (El-Shaarawi & Piegorsch, 2002;Llambi et al, 2004;Vasudevan et al, 2007;Bust, 2008;Smith & Brown, 2009;;O'Sullivan & Unwin, 2010d, 2010a, 2010b, 2010cDixon et al, 2011Diggle et al, 2013;Smith et al, 2015;Smith, 2015;Sadahiro, 2016, Müller et al, 2016González et al, 2016;De Paor et al, 2017;Bilbao et al, 2019;Heyne & Fhoteringham, 2020;Sadahiro & Liu, 2020;Elrahman Yassien et al, 2022;González & Moraga, 2023;Diggle, 2023;Soltisz et al, 2024;Sadahiro & Matsumoto, 2024).…”

“…It evaluates whether the spatial arrangement observed deviates significantly from what could be anticipated under spatial randomness. A p-value below 0.05 implies a statistically significant spatial arrangement, pointing towards the existence of spatial autocorrelation (Wang et al, 2019;González & Moraga, 2023;Westerholt, 2023).…”