Analysing spatial point patterns in R - CSIRO

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224 Further information on spatstat 40 Further information on spatstat Help files For information on a particular command in spatstat, consult the online help file by typing help(command). The help files are detailed and extensive. The complete manual is over 700 pages long. For examples of the use of a particular command, read the examples section in the help file, or type example(command) to see the examples executed. Quick reference Typehelp(spatstat) for a quick-reference overview of all the functions available in the package. For a demonstration of many of the capabilities of spatstat, type demo(spatstat). For a visual display of all the datasets supplied in spatstat, type demo(data). Website The websitewww.spatstat.org contains information on recent updates to the package, frequentlyasked questions, bug fixes, literature and other developments. Updates Spatstat is updated approximately once a month. Descriptions of the updates are given on the spatstat.org website, and can also be accessed from within the package, by typing latest.news() or news(package="spatstat"). Modelling For more spatstat examples on fitting point process models, see [11]. For more discussion on point process modelling strategies, see [3]. Citation If you use spatstat in a research publication, it would be much appreciated if you could cite the paper [10], or mention spatstat in the acknowledgements. In doing so, you will help us to justify the expenditure of time and effort on maintaining and developing the package. Citation details are also available in the package by typingcitation(package="spatstat"). Queries and requests If you have difficulty in getting the package to do what you want, or if you have a suggestion for additional features that could be added, please contact the package authors,Adrian.Baddeley@csiro.au andr.turner@auckland.ac.nz, or email the R special interest group in spatial and geographical statistics, r-sig-geo@stat.math.ethz.ch. CopyrightCSIRO 2010
REFERENCES 225 References [1] F.P. Agterberg. Automatic contouring of geological maps to detect target areas for mineral exploration. Journal of the International Association for Mathematical Geology, 6:373–395, 1974. [2] L. Anselin. Local indicators of spatial association – LISA. Geographical Analysis, 27:93–115, 1995. [3] A. Baddeley. Modelling strategies. In A.E. Gelfand, P.J. Diggle, M. Fuentes, and P. Guttorp, editors, Handbook of Spatial Statistics, chapter 20, pages 339–369. CRC Press, Boca Raton, 2010. [4] A. Baddeley, M. Berman, N.I. Fisher, A. Hardegen, R.K. Milne, D. Schuhmacher, R. Shah, and R. Turner. Spatial logistic regression and change-of-support for spatial Poisson point processes. Electronic Journal of Statistics, 4:1151–1201, 2010. doi: 10.1214/10-EJS581. [5] A. Baddeley, Y.-M. Chang, Y. Song, and R. Turner. Diagnostics for transformation of covariates in spatial Poisson point process models. Submitted for publication. [6] A. Baddeley, J. Møller, and A.G. Pakes. Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics, 60:627–649, 2008. [7] A. Baddeley, J. Møller, and R. Waagepetersen. Non- and semiparametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54(3):329–350, November 2000. [8] A. Baddeley, E. Rubak, and J. Moller. Score, pseudo-score and residual diagnostics for goodness-of-fit of spatial point process models. Submitted for publication. [9] A. Baddeley and R. Turner. Practical maximum pseudolikelihood for spatial point patterns (with discussion). Australian and New Zealand Journal of Statistics, 42(3):283–322, 2000. [10] A. Baddeley and R. Turner. Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software, 12(6):1–42, 2005. URL: www.jstatsoft.org, ISSN: 1548- 7660. [11] A. Baddeley and R. Turner. Modelling spatial point patterns in R. In A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan, editors, Case Studies in Spatial Point Pattern Modelling, number 185 in Lecture Notes in Statistics, pages 23–74. Springer-Verlag, New York, 2006. ISBN: 0-387-28311-0. [12] A. Baddeley, R. Turner, J. Møller, and M. Hazelton. Residual analysis for spatial point processes (with discussion). Journal of the Royal Statistical Society, series B, 67(5):617–666, 2005. [13] A.J. Baddeley. Spatial sampling and censoring. In O.E. Barndorff-Nielsen, W.S. Kendall, and M.N.M. van Lieshout, editors, Stochastic Geometry: Likelihood and Computation, chapter 2, pages 37–78. Chapman and Hall, London, 1999. [14] A.J. Baddeley and J. Møller. Nearest-neighbour Markov point processes and random sets. International Statistical Review, 57:89–121, 1989. CopyrightCSIRO 2010
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Analysing spatial point patterns in
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CONTENTS 3 Contents PART I. OVERVIE
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CONTENTS 5 PART I. OVERVIEW The fir
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130 1.1 Types of data 7 The mark co
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1.2 Typical scientific questions 9
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1.2 Typical scientific questions 11
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13 We’ll cover both classical and
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2.4 Marks and covariates 15 Data ar
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2.4 Marks and covariates 17 example
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3.3 Contributed libraries for R 19
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4.4 Licence 21 The response will be
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0.005 4.6 Exploratory data analysis
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4.7 Models 25 E K(r) 0 500 1000 150
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4.8 Multitype point patterns 27 4.8
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4.8 Multitype point patterns 29 e.g
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4.9 Installed datasets 31 PART II.
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5.2 Classes in spatstat 33 Point pa
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5.3 Return values 35 density(swedis
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5.3 Return values 37 Many of the fu
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6.2 Creating a ppp object 39 To rea
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6.4 Categorical marks 41 longleaf T
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6.6 Checking data 43 > par(mfrow =
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45 7 Converting from GIS formats Th
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8.1 Making windows by hand 47 8.1.3
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8.2 Converting from GIS formats 49
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8.5 Creating a point pattern in any
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53 9 Manipulating point patterns Be
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9.2 Operations on ppp objects 55 [1
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9.2 Operations on ppp objects 57 ma
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9.3 Example 59 You may also notice
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9.5 List of operations on point pat
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63 10 Pixel images in spatstat An o
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10.2 Inspecting an image 65 > f w
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10.2 Inspecting an image 67 > plot(
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10.3 Manipulating images 69 0 200 6
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71 > W V 3) > U 3, 42, Z)) Other
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11.3 Operations involving a tessell
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12.2 Inhomogeneous intensity 79 12.
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12.2 Inhomogeneous intensity 81 den
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13.3 Relative distribution estimate
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1 1 2 4 4 1 2 13.4 Distance map 85
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13.4 Distance map 87 PART IV. POISS
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14.2 Quadrat counting tests for CSR
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14.4 Kolmogorov-Smirnov test of CSR
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14.5 Using covariate data 93 Becaus
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95 15 Maximum likelihood for Poisso
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15.3 Fitting Poisson processes in s
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15.4 Fitted models 99 > ppm(bei, ~s
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15.4 Fitted models 101 > predict(fi
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15.4 Fitted models 103 effectfun(fi
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15.5 Simulating the fitted model 10
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16.2 Validation using residuals 107
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113 PART V. INTERACTION Part V of t
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115 independent + + + + + + + + + +
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19.2 Empty space distances 117 Anot
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19.2 Empty space distances 119 cell
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19.2 Empty space distances 121 Fest
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19.3 Nearest neighbour distances 12
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19.4 Pairwise distances and the K f
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19.4 Pairwise distances and the K f
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19.6 Manipulating and plotting summ
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19.7 Caveats 131 Kest(X) X K(r) 0.0
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20.1 Envelopes and Monte Carlo test
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139 21 Spatial bootstrap methods Sp
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22.2 Cox processes 141 22.2 Cox pro
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22.4 Sequential models 143 rSSI(0.0
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23.1 Fitting a cluster process 145
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23.4 Generic algorithm for minimum
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149 In data sharpening [27] the poi
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25.2 Inhomogeneous cluster models 1
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25.4 Local scaling 153 > data(bei)
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25.4 Local scaling 155 PART VI. GIB
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26.3 Pairwise interaction models 15
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26.4 Higher-order interactions 159
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26.6 Simulating Gibbs models 161 St
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27.2 Fitting Gibbs models in spatst
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27.3 Interpoint interactions 165 27
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27.4 Fitted point process models 16
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27.6 Dealing with nuisance paramete
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171 Stationary Strauss process Firs
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Page 177 and 178: 28.3 New methods 177 PART VII. MARK
Page 179 and 180: 29.3 Methodological issues 179 A ma
Page 181 and 182: 181 Chorley−Ribble Data For furth
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Page 185 and 186: 30.3 Manipulating data 185 > data(l
Page 187 and 188: 187 31 Exploratory tools for multit
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Page 197 and 198: 31.4 Randomisation tests 197 31.4 R
Page 199 and 200: 31.4 Randomisation tests 199 > plot
Page 201 and 202: 32.1 Numeric marks: distribution an
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Page 205 and 206: 33.2 Simulation 205 33.2 Simulation
Page 207 and 208: 33.3 Fitting Poisson models 207 33.
Page 209 and 210: 33.3 Fitting Poisson models 209 whe
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Page 217 and 218: 217 There are also the usual method
Page 219 and 220: 219 lty col key label meaning iso 1
Page 221 and 222: 221 38 Replicated data and hyperfra
Page 223: 223 H H(r) 0.0 0.2 0.4 0.6 0.8 km h
Page 227 and 228: REFERENCES 227 [32] P.J. Diggle. Bi
Page 229 and 230: Index analysis of deviance, 103 are
Page 231 and 232: INDEX 231 models, 25, 224 Monte Car
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