Spatial logistic regression and change-of-support in Poisson point processes

By Adrian Baddeley, Mark Berman, Nicholas I. Fisher, Andrew Hardegen, Robin K. Milne, Dominic Schuhmacher, Rohan Shah, Rolf Turner. 2010

91 citations recorded by the source corpus.

In Geographical Information Systems, spatial point pattern data are often analysed by dividing space into pixels, recording the presence or absence of points in each pixel, and fitting a logistic regression. We study weaknesses of this approach, propose improvements, and demonstrate an application to prospective geology in Western Australia. Models based on different pixel grids are incompatible (a ‘change-of-support’ problem) unless the pixels are very small. On a fine pixel grid, a spatial logistic

Subfields: Applied Mathematics, Economics and Econometrics, Epidemiology

View Paper: https://www.semanticscholar.org/paper/2ef70be2f87b51f4f1960d107df93a36b8ac1abe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Adrian Baddeley
2. Rolf Turner
3. Ya-Mei Chang
4. Yong Song
5. Mevin B. Hooten
6. T. Hefley
7. Thomas Opitz
8. G. Aarts

In Economics and Econometrics

1. Mevin B. Hooten
2. Adrian Baddeley
3. T. Hefley
4. Rolf Turner
5. Ya-Mei Chang
6. Yong Song
7. Thomas Opitz
8. G. Aarts

Citations in the corpus

• Nonparametric estimation of the dependence of a spatial point process on spatial covariates (2012, methodology): “We estimated the parameters α, β by maximum likelihood [9].”
• A comparison of Maxlike and Maxent for modelling species distributions (2014, methodology): “Rather than assuming random sampling of space, as Maxlike does, IPPs assume that presence records represent a random sample of individuals, and thus, records occur in proportion to population density (‘density estimation’; Dudik, Phillips & Schapire 2004; Baddeley et al. 2010).”
• Hierarchical Species Distribution Models (2016, methodology): “there has been a focus on understanding how generalized linear models with Poisson or Bernoulli response distributions can be used to approximate the Poisson point process model [1, 3, 32, 99].”
• Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, methodology): “Owing to work performed by others (Diggle 1990; Cressie 1993; Diggle & Rowlingson 1994; Baddeley & Turner 2000; Lele & Keim 2006; Lele 2009; Baddeley et al. 2010; Diggle, Kaimi, & Abellana 2010a; Warton & Shepherd 2010), it is possible to show that the Poisson likelihood function used for count data”
• Spatiotemporal wildfire modeling through point processes with moderate and extreme marks (2021, methodology): “, the Berman–Turner 1992 device); see Baddeley et al. (2010). 3.”