Analysing spatial point patterns in R - CSIRO

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38 Entering point pattern data into spatstat Recommended range of argument r: [0, 24] Available range of argument r: [0, 24] Unit of length: 0.1 metres > plot(u) u K(r) 0 500 1000 1500 iso trans border theo 0 5 10 15 20 r (one unit = 0.1 metres) 6 Entering point pattern data into spatstat To analyse your own point pattern data in spatstat, you’ll need to read the data into R and convert them into an object of class "ppp". This section explains how to handle ‘raw’ data in a text file. Section 7 explains how to handle data files in other formats (such as ESRI shapefiles). 6.1 Reading raw data into R It’s good practice to keep a copy of your original data in a text file (where it is not dependent on changes to software, data formats etc). The data can then be loaded into R using standard operations. Two common formats for the data are a comma-separated values (csv) file, generated by many spreadsheet packages. To read data from a csv file into R, use the command read.csv. a table format file. The data are arranged in rows and columns, one row for each spatial point, something like this: Easting Northing Diameter 176.111 32.105 10.4 175.989 31.979 7.6 .... .... .... The first line of the file is an (optional) header. To read these data into R, use the command read.table. CopyrightCSIRO 2010
6.2 Creating a ppp object 39 To read these datafiles, type either > mydata mydata east east [1] 176.111 175.989 176.786 176.394 176.501 175.480 175.041 175.909 176.955 [10] 175.232 176.842 176.752 176.166 175.778 175.176 175.124 175.853 175.866 You can also use scan() to read a stream of numbers that you type at the keyboard, or scan(file="filename") to read a stream of numbers from a file. 6.2 Creating a ppp object Here is a simple recipe to create a point pattern object from raw data in R. 1. store the x and y coordinates for the points in two vectors x and y. 2. create two vectors xrange, yrange of length 2 giving the x and y dimensions of a rectangle that contains all the points. 3. create the point pattern object by > ppp(x, y, xrange, yrange) The value returned by the function ppp is an object of class "ppp" representing a point pattern inside a rectangle. If the natural window for the point pattern is not a rectangle, then you need to use a command like > ppp(x, y, window = W) where W is a window object. See Section 8.5 for details on how to do this. For example, the following code reads raw data from a text file in table format, and creates a point pattern: > mydata east north X mydata attach(mydata) > X
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 and 20: 3.3 Contributed libraries for R 19
Page 21 and 22: 4.4 Licence 21 The response will be
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: 5.3 Return values 37 Many of the fu
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
Page 71 and 72: 71 > W V 3) > U 3, 42, Z)) Other
Page 73 and 74: 11.3 Operations involving a tessell
Page 79 and 80: 12.2 Inhomogeneous intensity 79 12.
Page 81 and 82: 12.2 Inhomogeneous intensity 81 den
Page 83 and 84: 13.3 Relative distribution estimate
Page 85 and 86: 1 1 2 4 4 1 2 13.4 Distance map 85
Page 87 and 88: 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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Analysing spatial point patterns in R - CSIRO

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