Analysing spatial point patterns in R - CSIRO

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20 Introduction to spatstat Instructions on how to install a package are given atcran.r-project.org. If you are running Windows, first start an R session. Then try the pull-down menu item Packages — Install packages. If this menu item is available, then you will be able to download and install any desired packages by simply selecting the package name from the pulldown list. If this menu item is not available (for internet security reasons), you can manually download packages by going to the CRAN website under Contributed packages --- Windows binaries and downloading the desired zip files of Windows binary files. To perform step 2, start an R session and use the menu item Packages — Install from local zip files to install. If you are running Linux, step 1 is performed manually by going to the CRAN website under Contributed Packages and downloading the tar filepackagename.tar.gz. Step 2 is performed by issuing the command R CMD INSTALL packagename.tar.gz. 4 Introduction to spatstat 4.1 The spatstat package Spatstat is a contributed R package for analysing spatial data, written by Adrian Baddeley and Rolf Turner. Current versions of spatstat deal mainly with spatial point patterns in two dimensions. The package supports creation, manipulation and plotting of point patterns exploratory data analysis simulation of point process models parametric model-fitting hypothesis tests residual plots, model diagnostics Spatstat is one of the largest contributed packages available for R, containing over 1000 user-level functions and a 750-page manual. It has its own web domain, www.spatstat.org, offering information about the package. Spatstat can be downloaded from cran.r-project.org (under ‘Contributed packages — spatstat’). To install spatstat you will also need to download the package deldir (some other packages are also recommended but not compulsory). 4.2 Please acknowledge spatstat If you use spatstat for research that leads to publications, it would be much appreciated if you could acknowledge spatstat in your publications, preferably citing [10]. Citations help us to justify the expenditure of time and effort on maintaining and developing the package. 4.3 Getting started Here is a quick demonstration of spatstat in action. You can follow the demonstration by typing the commands into R. To begin any analysis using spatstat, first start the R system, and type > library(spatstat) CopyrightCSIRO 2010
4.4 Licence 21 The response will be something like this: deldir 0.0-12 Please note: The process for determining duplicated points has changed from that used in version 0.0-9 (and previously). spatstat 1.21-2 Type help(spatstat) for an overview of spatstat latest.news() for news on latest version licence.polygons() for licence information on polygon calculations The printout shows that, before loadingspatstat, the system has loaded the packagedeldir that is required by spatstat. Then it loads spatstat, showing the version number of the package. For a list of the commands available in spatstat, type > help(spatstat) To get information on a particular command, type help(command). To gain an impression of what is available in spatstat, you can run the package demonstration by typing demo(spatstat). 4.4 Licence The spatstat package is free open source software, under the GNU Public Licence. However, some of the facilities in spatstat depend on a polygon geometry package called gpclib, and this has a restricted licence, that forbids commercial use. For details, type licence.polygons(). By default, the gpclib package is disabled when you start spatstat. If you are doing non-commercial work, please enable the polygon clipping library by typing > spatstat.options(gpclib = TRUE) 4.5 Inspecting data For our first demonstration, we’ll use one of the standard point pattern datasets that is installed with the package. The ‘Swedish Pines’ dataset represent the positions of 71 trees in a forest plot 9.6 by 10.0 metres. > data(swedishpines) To avoid typing ‘swedishpines’ all the time, let us copy the data to another dataset with a shorter name: > X plot(X) CopyrightCSIRO 2010
Page 1 and 2: Analysing spatial point patterns in
Page 3 and 4: CONTENTS 3 Contents PART I. OVERVIE
Page 5 and 6: CONTENTS 5 PART I. OVERVIEW The fir
Page 7 and 8: 130 1.1 Types of data 7 The mark co
Page 9 and 10: 1.2 Typical scientific questions 9
Page 11 and 12: 1.2 Typical scientific questions 11
Page 13 and 14: 13 We’ll cover both classical and
Page 15 and 16: 2.4 Marks and covariates 15 Data ar
Page 17 and 18: 2.4 Marks and covariates 17 example
Page 19: 3.3 Contributed libraries for R 19
Page 23 and 24: 0.005 4.6 Exploratory data analysis
Page 25 and 26: 4.7 Models 25 E K(r) 0 500 1000 150
Page 27 and 28: 4.8 Multitype point patterns 27 4.8
Page 29 and 30: 4.8 Multitype point patterns 29 e.g
Page 31 and 32: 4.9 Installed datasets 31 PART II.
Page 33 and 34: 5.2 Classes in spatstat 33 Point pa
Page 35 and 36: 5.3 Return values 35 density(swedis
Page 37 and 38: 5.3 Return values 37 Many of the fu
Page 39 and 40: 6.2 Creating a ppp object 39 To rea
Page 41 and 42: 6.4 Categorical marks 41 longleaf T
Page 43 and 44: 6.6 Checking data 43 > par(mfrow =
Page 45 and 46: 45 7 Converting from GIS formats Th
Page 47 and 48: 8.1 Making windows by hand 47 8.1.3
Page 49 and 50: 8.2 Converting from GIS formats 49
Page 51 and 52: 8.5 Creating a point pattern in any
Page 53 and 54: 53 9 Manipulating point patterns Be
Page 55 and 56: 9.2 Operations on ppp objects 55 [1
Page 57 and 58: 9.2 Operations on ppp objects 57 ma
Page 59 and 60: 9.3 Example 59 You may also notice
Page 61 and 62: 9.5 List of operations on point pat
Page 63 and 64: 63 10 Pixel images in spatstat An o
Page 65 and 66: 10.2 Inspecting an image 65 > f w
Page 67 and 68: 10.2 Inspecting an image 67 > plot(
Page 69 and 70: 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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28.2 Residuals for Gibbs processes
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28.2 Residuals for Gibbs processes
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28.3 New methods 177 PART VII. MARK
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29.3 Methodological issues 179 A ma
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181 Chorley−Ribble Data For furth
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30.2 Inspecting a marked point patt
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30.3 Manipulating data 185 > data(l
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187 31 Exploratory tools for multit
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31.2 Simple summaries of neighbouri
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31.3 Distance methods and summary f
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31.4 Randomisation tests 197 31.4 R
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31.4 Randomisation tests 199 > plot
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32.1 Numeric marks: distribution an
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32.3 Reverse conditional moments 20
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33.2 Simulation 205 33.2 Simulation
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33.3 Fitting Poisson models 207 33.
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33.3 Fitting Poisson models 209 whe
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34.1 Gibbs models 211 34.1.3 Pairwi
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34.3 Fitting Gibbs models to multit
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34.3 Fitting Gibbs models to multit
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217 There are also the usual method
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219 lty col key label meaning iso 1
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221 38 Replicated data and hyperfra
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223 H H(r) 0.0 0.2 0.4 0.6 0.8 km h
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REFERENCES 225 References [1] F.P.
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REFERENCES 227 [32] P.J. Diggle. Bi
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Index analysis of deviance, 103 are
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INDEX 231 models, 25, 224 Monte Car
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