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

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

[1]  W. S. Robinson,et al.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[2]  Rasmus Waagepetersen,et al.  Estimating functions for inhomogeneous spatial point processes with incomplete covariate data , 2008 .

[3]  D. Stoyan,et al.  Statistical Analysis and Modelling of Spatial Point Patterns , 2008 .

[4]  Aihua Xia,et al.  A new metric between distributions of point processes , 2007, Advances in Applied Probability.

[5]  Jon Wakefield,et al.  Disease mapping and spatial regression with count data. , 2007, Biostatistics.

[6]  Stephen L. Rathbun,et al.  Modelling the effects of partially observed covariates on Poisson process intensity , 2007 .

[7]  Claude Grasland,et al.  Modifiable Area Unit Problem , 2006 .

[8]  William J. Elliot,et al.  Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis , 2006, Trans. GIS.

[9]  Adrian Baddeley,et al.  Modelling Spatial Point Patterns in R , 2006 .

[10]  Jürgen Symanzik,et al.  Statistical Analysis of Spatial Point Patterns , 2005, Technometrics.

[11]  P. Diggle Applied Spatial Statistics for Public Health Data , 2005 .

[12]  Adrian Baddeley,et al.  spatstat: An R Package for Analyzing Spatial Point Patterns , 2005 .

[13]  Dominic Schuhmacher Estimation of distances between point process distibutions , 2005 .

[14]  Jeffrey D. Scargle,et al.  An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods , 2004, Technometrics.

[15]  Jonathan Wakefield,et al.  A critique of statistical aspects of ecological studies in spatial epidemiology , 2004, Environmental and Ecological Statistics.

[16]  David R. Brillinger,et al.  Empirical examination of the threshold model of neuron firing , 1979, Biological Cybernetics.

[17]  J. Møller,et al.  Statistical Inference and Simulation for Spatial Point Processes , 2003 .

[18]  John C. Davis,et al.  Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .

[19]  Aihua Xia,et al.  Stein’s method and Poisson process approximation , 2003 .

[20]  Y. LindaJ. Combining Incompatible Spatial Data , 2003 .

[21]  Eric R. Ziegel,et al.  An Introduction to Generalized Linear Models , 2002, Technometrics.

[22]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[23]  Adrian Baddeley,et al.  Nonparametric measures of association between a spatial point process and a random set, with geological applications , 2002 .

[24]  Adrian Baddeley,et al.  Bivariate J-function and other graphical statistical methods help select the best predictor variables as inputs for a neural network method of mineral prospectivity mapping , 2002 .

[25]  Mark Gillings,et al.  Spatial Technology and Archaeology: The Archaeological Applications of GIS , 2002 .

[26]  A. Gelfand,et al.  Prediction, interpolation and regression for spatially misaligned data , 2002 .

[27]  Gary King,et al.  Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data , 2008, Political Analysis.

[28]  D. Groves,et al.  Late-kinematic timing of orogenic gold deposits and significance for computer-based exploration techniques with emphasis on the Yilgarn Block, Western Australia , 2000 .

[29]  James K. Lindsey,et al.  Applying Generalized Linear Models , 2000 .

[30]  Ron Goldman,et al.  Poisson approximation , 2000, Proceedings Geometric Modeling and Processing 2000. Theory and Applications.

[31]  J. Wakefield,et al.  Spatial epidemiology: methods and applications. , 2000 .

[32]  Peter Guttorp,et al.  Robustness for Inhomogeneous Poisson Point Processes , 1999 .

[33]  C. Chung,et al.  Probabilistic prediction models for landslide hazard mapping , 1999 .

[34]  Y. Kutoyants,et al.  Statistical Inference for Spatial Poisson Processes , 1998 .

[35]  A. Baddeley,et al.  Practical Maximum Pseudolikelihood for Spatial Point Patterns , 1998, Advances in Applied Probability.

[36]  P. Atkinson,et al.  Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy , 1998 .

[37]  D. Groves,et al.  Gold prospectivity mapping using a geographic information system (GIS), with examples from the Yilgarn Block of Western Australia , 1997 .

[38]  B. Ripley,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[39]  Willis L. Owen,et al.  Modelling Frequency and Count Data , 1996 .

[40]  Noel A Cressie,et al.  Change of support and the modifiable areal unit problem , 1996 .

[41]  Peter J. Diggle,et al.  A Conditional Approach to Point Process Modelling of Elevated Risk , 1994 .

[42]  N. Cressie,et al.  Asymptotic Properties of Estimators for the Parameters of Spatial Inhomogeneous Poisson Point Processes , 1994, Advances in Applied Probability.

[43]  James K. Lindsey,et al.  The Analysis of Stochastic Processes using GLIM , 1992 .

[44]  Mark Berman,et al.  Approximating Point Process Likelihoods with Glim , 1992 .

[45]  Merlise A. Clyde,et al.  Logistic regression for spatial pair-potential models , 1991 .

[46]  Nakahiro Yoshida,et al.  On the robust estimation in poisson processes with periodic intensities , 1990 .

[47]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[48]  Geoffrey J. McLachlan,et al.  Bias associated with the discriminant analysis approach to the estimation of mixing proportions , 1989, Pattern Recognit..

[49]  A. Albert,et al.  On the existence of maximum likelihood estimates in logistic regression models , 1984 .

[50]  O. Kallenberg Random Measures , 1983 .

[51]  Kenneth L. Kvamme,et al.  Computer processing techniques for regional modeling of archaeological site locations , 1983 .

[52]  J. Besag,et al.  Point process limits of lattice processes , 1982, Journal of Applied Probability.

[53]  K. Krickeberg,et al.  Processus ponctuels en statistique , 1982 .

[54]  F. Agterberg,et al.  Regression models for estimating mineral resources from geological map data , 1980 .

[55]  O. Barndorff-Nielsen Information and Exponential Families in Statistical Theory , 1980 .

[56]  D. Brillinger Comparative Aspects of the Study of Ordinary Time Series and of Point Processes , 1978 .

[57]  W. Hauck,et al.  Wald's Test as Applied to Hypotheses in Logit Analysis , 1977 .

[58]  Change Detection Metrics And Mixels (mixed Picture Elements) For Computer Analysis Of Low Resolution Remote Sensing Imagery , 1977 .

[59]  R. W. Wedderburn,et al.  On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models , 1976 .

[60]  F. Agterberg,et al.  Automatic contouring of geological maps to detect target areas for mineral exploration , 1974 .

[61]  M. Woodbury A missing information principle: theory and applications , 1972 .

[62]  P. Lewis Recent results in the statistical analysis of univariate point processes , 1971 .

[63]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .