第3章地理空間分布パターン



In [1]:

    
library(splancs)
 data(cardiff)
 polymap(cardiff$poly, xlab = "Easting", ylab = "Northing")
 points(cardiff$x, cardiff$y, pch = 19)









    



Loading required package: sp

Spatial Point Pattern Analysis Code in S-Plus
 
 Version 2 - Spatial and Space-Time analysis







    



Error in file(con, "rb"):  コネクションを開くことができません



In [2]:

    
polymap(cardiff$poly, xlab = "Easting", ylab = "Northing")
points(csr(cardiff$poly, length(cardiff$x)), pch = 1)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [3]:

    
help(cardiff)









    Out[3]:





cardiff {splancs} R Documentation

Locations of homes of juvenile offenders

Description

Locations of homes of 168 juvenile offenders on a Cardiff housing estate



Usage

data(cardiff)


Format

A list corresponding to a Venables and Ripley point object with 168 observations




 
    x   numeric   grid eastings


 
    y    numeric   grid northings


 
    area 	list	  bounding box with xl, xu, yl, yu


 
    poly  array  polygon boundary with columns x and y


 
  






Source

Herbert, 1980, - Bailey and Gatrell 1995, ch. 3.



References

Bailey, T. C. and Gatrell, A. C. 1995, Interactive spatial data analysis. Longman, Harlow.


[Package splancs version 2.01-38 ]



In [4]:

    
data(cardiff)
s <- seq(2, 30, 2)
UL.khat <- Kenv.csr(length(cardiff$x), cardiff$poly, 
nsim = 99, s = s)
plot(s, sqrt(khat(as.points(cardiff), cardiff$poly, s = s)/pi) - 
s, type = "l", xlab = "h", ylim = c(-1, 1.5), 
ylab = "Estimated L function and simulation envelope")
lines(s, sqrt(UL.khat$upper/pi) - s, lty = 2)
lines(s, sqrt(UL.khat$lower/pi) - s, lty = 2)









    



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In [5]:

    
data(okwhite)
data(okblack)
okpoly <- list(x = c(okwhite$x, okblack$x), y = c(okwhite$y, okblack$y))
okpoly.bb <- bboxx(bbox(as.points(okpoly)))
s <- seq(5, 80, 5)
K1.hat <- khat(as.points(okwhite), okpoly.bb, s)
K2.hat <- khat(as.points(okblack), okpoly.bb, s)
K.diff <- K1.hat - K2.hat
plot(s, K.diff, xlab = "distance", ylab = expression(hat(K)[1] - hat(K)[2]), ylim = c(-11000, 7000), type = "l",main = "Simulation envelopes, random labelling")
env.lab <- Kenv.label(as.points(okwhite), as.points(okblack), okpoly.bb, nsim = 99, s = s)
lines(s, env.lab$upper, lty = 2)
lines(s, env.lab$lower, lty = 2)









    



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In [6]:

    
library(sp)
library(maptools)
pj <- CRS("+proj=longlat +datum=WGS84")
f <- system.file("shapes/sids.shp", package = "maptools")[1]
nc <- readShapePoly(f, IDvar = "FIPSNO", proj4string = pj)









    



Checking rgeos availability: TRUE

Attaching package: ‘maptools’

The following object is masked from ‘package:sp’:

    nowrapSpatialLines



In [7]:

    
library(spdep)
 nc.nb <- poly2nb(nc)









    



Loading required package: Matrix



In [8]:

    
nb2listw(nc.nb, style = "B")









    Out[8]:





Characteristics of weights list object:
Neighbour list object:
Number of regions: 100 
Number of nonzero links: 490 
Percentage nonzero weights: 4.9 
Average number of links: 4.9 

Weights style: B 
Weights constants summary:
    n    nn  S0  S1    S2
B 100 10000 490 980 10696



In [9]:

    
nc.c <- coordinates(nc)
nc.tri.nb <- tri2nb(nc.c)









    



     PLEASE NOTE:  The components "delsgs" and "summary" of the
 object returned by deldir() are now DATA FRAMES rather than
 matrices (as they were prior to release 0.0-18).
 See help("deldir").
 
     PLEASE NOTE: The process that deldir() uses for determining
 duplicated points has changed from that used in version
 0.0-9 of this package (and previously). See help("deldir").



In [10]:

    
nc.knn.nb <- knn2nb(knearneigh(nc.c, k = 3))



In [11]:

    
nc.dn.nb <- dnearneigh(nc.c, d1 = 0, d2 = 40, longlat = TRUE)



In [12]:

    
plot(nc, border = "grey")
plot(nc.tri.nb, nc.c, col = "red", add = TRUE)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [13]:

    
plot(nc, border = "grey")
plot(nc.tri.nb, nc.c, col = "red", add = TRUE)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [14]:

    
plot(nc, border = "grey")
plot(nc.knn.nb, nc.c, col = "red", add = TRUE)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [15]:

    
plot(nc, border = "grey")
 plot(nc.dn.nb, nc.c, col = "red", add = TRUE)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [16]:

    
diffs <- diffnb(nc.nb, nc.knn.nb)









    



Warning message:
In diffnb(nc.nb, nc.knn.nb): region.id differ; using ids of first list



In [17]:

    
plot(nc, border = "grey")
plot(diffs, nc.c, col = "red", add = TRUE)









    



Error in file(con, "rb"):  コネクションを開くことができません



In [18]:

    
s <- nc@data$SID74/nc@data$BIR74 * 1e+05
moran.test(s, nb2listw(nc.knn.nb))









    Out[18]:





	Moran I test under randomisation

data:  s  
weights: nb2listw(nc.knn.nb)  

Moran I statistic standard deviate = 3.0285, p-value = 0.001229
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance 
      0.217763140      -0.010101010       0.005660983



In [19]:

    
geary.test(s, nb2listw(nc.nb, style = "B"))









    Out[19]:





	Geary C test under randomisation

data:  s 
weights: nb2listw(nc.nb, style = "B") 

Geary C statistic standard deviate = 3.0989, p-value = 0.0009711
alternative hypothesis: Expectation greater than statistic
sample estimates:
Geary C statistic       Expectation          Variance 
       0.67796679        1.00000000        0.01079878



In [20]:

    
lmo <- localmoran(s, nb2listw(nc.nb, style = "B"))
head(lmo)









    Out[20]:





Ii E.Ii Var.Ii Z.Ii Pr(z > 0)

	37001 -0.38296680 -0.06060606  5.66778683 -0.13540522  0.55385425
	37003  2.84892433 -0.04040404  3.77639636  1.48681905  0.06853130
	37005  1.98692859 -0.03030303  2.83039504  1.19903605  0.11525696
	37007 -3.68378017 -0.04040404  3.77639636 -1.87484437  0.96959293
	37009  1.89322430 -0.03030303  2.83039504  1.14333852  0.12644903
	37011  3.20602844 -0.05050505  4.72219363  1.49859284  0.06698965



In [21]:

    
moran.plot(s, nb2listw(nc.nb, style = "B"))









    



Error in file(con, "rb"):  コネクションを開くことができません



In [22]:

    
library(DCluster)









    



Loading required package: boot
Loading required package: MASS



In [23]:

    
Observed <- nc@data$SID74
Expected <- nc@data$BIR74 * sum(nc@data$SID74)/sum(nc@data$BIR74)
Population <- nc@data$BIR74
dat <- data.frame(Observed, Expected, Population)
summary(dat)









    Out[23]:





    Observed        Expected         Population   
 Min.   : 0.00   Min.   : 0.5013   Min.   :  248  
 1st Qu.: 2.00   1st Qu.: 2.1771   1st Qu.: 1077  
 Median : 4.00   Median : 4.4078   Median : 2180  
 Mean   : 6.67   Mean   : 6.6700   Mean   : 3300  
 3rd Qu.: 8.25   3rd Qu.: 7.9564   3rd Qu.: 3936  
 Max.   :44.00   Max.   :43.6390   Max.   :21588



In [24]:

    
xy <- coordinates(nc)
d <- spDistsN1(xy, xy[1, ], longlat = TRUE)
i <- order(d)



In [25]:

    
kullnagar.stat(dat[i, ], fractpop = 0.5)









    Out[25]:





	value
		1.83669260512487
	size
		1



In [26]:

    
xy1 <- as.data.frame(xy)
names(xy1) <- c("x", "y")
kn.p <- opgam(cbind(dat, xy1), thegrid = xy1, alpha = 0.02, iscluster = kn.iscluster, fractpop = 0.15, R = 999, 
model = "poisson", mle = calculate.mle(dat, model = "poisson"))
kn.p









    Out[26]:





x y statistic cluster pvalue size

	37007 -80.10407 34.9752 106625.2 1 0.001 1
	37013 -76.83809 35.58468 8869.925 1 0.001 24
	37015 -76.98481 36.0619 28388.07 1 0.001 27
	37017 -78.56013 34.60932 333.1234 1 0.002 2
	37019 -78.25073 34.07663 1782.277 1 0.001 9
	37029 -76.23435 36.4012 7360.441 1 0.001 27
	37041 -76.61648 36.14893 7360.441 1 0.001 27
	37047 -78.65492 34.26323 51695.41 1 0.001 4
	37049 -77.17801 35.23062 236.7854 1 0.004 16
	37053 -76.05211 36.39714 7360.441 1 0.001 27
	37055 -75.86483 35.76263 8241.024 1 0.002 25
	37065 -77.59819 35.90722 144050.6 1 0.001 14
	37073 -76.70448 36.44423 11269.29 1 0.001 25
	37079 -77.67889 35.4823 220.8674 1 0.006 14
	37083 -77.65628 36.25305 45417.54 1 0.001 17
	37091 -76.99478 36.36145 26739.67 1 0.001 9
	37093 -79.23592 35.01168 1438.994 1 0.001 4
	37095 -76.25562 35.54225 8241.024 1 0.001 25
	37103 -77.35634 35.01854 58.75584 1 0.014 15
	37107 -77.64549 35.23284 82.17384 1 0.007 11
	37117 -77.10766 35.83635 92296.62 1 0.001 17
	37127 -77.98667 35.96233 155.1066 1 0.005 7
	37129 -77.89578 34.26166 71.04705 1 0.008 11
	37131 -77.41056 36.42228 197884.6 1 0.001 5
	37137 -76.76878 35.13564 118.28 1 0.007 21
	37139 -76.3146 36.31237 7360.441 1 0.001 27
	37141 -77.91628 34.52686 137.0173 1 0.004 12
	37143 -76.45461 36.20488 7360.441 1 0.001 27
	37147 -77.3779 35.58916 150692.5 1 0.001 17
	37153 -79.74231 35.00146 4442.8 1 0.001 9
	37155 -79.10111 34.63874 6426.299 1 0.001 3
	37165 -79.4772 34.83949 152040.2 1 0.001 6
	37177 -76.21982 35.8096 8241.024 1 0.001 25
	37185 -78.11043 36.39697 406.239 1 0.004 9
	37187 -76.58298 35.82505 22824.25 1 0.001 26
	37195 -77.92273 35.69914 301.1284 1 0.001 11



In [27]:

    
x.glm <- glm(Observed ~ 1 + offset(log(Expected)), data = dat, 
 family = "poisson")
x.nb <- glm.nb(Observed ~ 1 + offset(log(Expected)), 
 data = dat)
test.nb.pois(x.nb, x.glm)









    Out[27]:





	Likelihood ratio test for overdispersion

data:  x.nb : x.glm
LR = 36.421, = 1, p-value = 1.589e-09
sample estimates:
       zscore  p.mayor.modZ 
-6.033594e+00  1.603523e-09



In [28]:

    
kn.nb <- opgam(cbind(dat, xy1), thegrid = xy1, alpha = 0.02, 
iscluster = kn.iscluster, fractpop = 0.15, R = 999, 
model = "negbin", mle = calculate.mle(dat, model = "negbin"))



In [29]:

    
class(kn.nb)









    Out[29]:




'data.frame'



In [30]:

    
coordinates(kn.nb) <- ~x + y
class(kn.nb)









    Out[30]:




'SpatialPointsDataFrame'



In [31]:

    
k <- which.min(kn.nb$pvalue)
 k









    Out[31]:




3



In [32]:

    
x = c(0.5, 0.5, 1.0, 1.5)
y = c(1.5, 0.5, 0.5, 0.5)
xy = cbind(x,y)
dimnames(xy)[[1]] = c("a", "b", "c", "d")
pts = SpatialPoints(xy)
xpol = c(0,1,1,0,0)
ypol = c(0,0,1,1,0)
pol = SpatialPolygons(list(
 Polygons(list(Polygon(cbind(xpol-1.05,ypol))), ID="x1"),
 Polygons(list(Polygon(cbind(xpol,ypol))), ID="x2"),
 Polygons(list(Polygon(cbind(xpol,ypol - 1.0))), ID="x3"), Polygons(list(Polygon(cbind(xpol + 1.0, ypol))), ID="x4"),Polygons(list(Polygon(cbind(xpol+.4, ypol+.1))), ID="x5")))



In [33]:

    
j <- over(kn.nb[k, ], nc)
j









    



Error: identicalCRS(x, y) is not TRUE






    



Error in eval(expr, envir, enclos):  オブジェクト 'j' がありません



In [34]:

    
nc









    Out[34]:





An object of class "SpatialPolygonsDataFrame"
Slot "data":
       AREA PERIMETER CNTY_ CNTY_ID         NAME  FIPS FIPSNO CRESS_ID BIR74
37001 0.111     1.392  1904    1904     Alamance 37001  37001        1  4672
37003 0.066     1.070  1950    1950    Alexander 37003  37003        2  1333
37005 0.061     1.231  1827    1827    Alleghany 37005  37005        3   487
37007 0.138     1.621  2096    2096        Anson 37007  37007        4  1570
37009 0.114     1.442  1825    1825         Ashe 37009  37009        5  1091
37011 0.064     1.213  1892    1892        Avery 37011  37011        6   781
37013 0.203     3.197  2004    2004     Beaufort 37013  37013        7  2692
37015 0.180     2.151  1905    1905       Bertie 37015  37015        8  1324
37017 0.225     2.107  2162    2162       Bladen 37017  37017        9  1782
37019 0.212     2.024  2241    2241    Brunswick 37019  37019       10  2181
37021 0.168     1.995  1988    1988     Buncombe 37021  37021       11  7515
37023 0.134     1.755  1958    1958        Burke 37023  37023       12  3573
37025 0.091     1.321  2042    2042     Cabarrus 37025  37025       13  4099
37027 0.122     1.516  1932    1932     Caldwell 37027  37027       14  3609
37029 0.062     1.547  1834    1834       Camden 37029  37029       15   286
37031 0.125     2.868  2156    2156     Carteret 37031  37031       16  2414
37033 0.114     1.352  1838    1838      Caswell 37033  37033       17  1035
37035 0.106     1.444  1986    1986      Catawba 37035  37035       18  5754
37037 0.180     2.142  1973    1973      Chatham 37037  37037       19  1646
37039 0.120     1.686  2088    2088     Cherokee 37039  37039       20  1027
37041 0.044     1.158  1887    1887       Chowan 37041  37041       21   751
37043 0.051     1.096  2109    2109         Clay 37043  37043       22   284
37045 0.118     1.506  2032    2032    Cleveland 37045  37045       23  4866
37047 0.240     2.365  2232    2232     Columbus 37047  37047       24  3350
37049 0.177     2.916  2119    2119       Craven 37049  37049       25  5868
37051 0.172     1.835  2090    2090   Cumberland 37051  37051       26 20366
37053 0.070     2.968  1831    1831    Currituck 37053  37053       27   508
37055 0.094     3.640  2000    2000         Dare 37055  37055       28   521
37057 0.145     1.791  1951    1951     Davidson 37057  37057       29  5509
37059 0.069     1.201  1948    1948        Davie 37059  37059       30  1207
37061 0.204     1.871  2100    2100       Duplin 37061  37061       31  2483
37063 0.077     1.271  1908    1908       Durham 37063  37063       32  7970
37065 0.131     1.521  1928    1928    Edgecombe 37065  37065       33  3657
37067 0.108     1.483  1900    1900      Forsyth 37067  37067       34 11858
37069 0.128     1.554  1897    1897     Franklin 37069  37069       35  1399
37071 0.091     1.470  2068    2068       Gaston 37071  37071       36  9014
37073 0.091     1.284  1835    1835        Gates 37073  37073       37   420
37075 0.078     1.202  2056    2056       Graham 37075  37075       38   415
37077 0.143     1.663  1840    1840    Granville 37077  37077       39  1671
37079 0.070     1.105  2016    2016       Greene 37079  37079       40   870
37081 0.170     1.680  1903    1903     Guilford 37081  37081       41 16184
37083 0.190     2.204  1846    1846      Halifax 37083  37083       42  3608
37085 0.154     1.680  2030    2030      Harnett 37085  37085       43  3776
37087 0.144     1.690  1996    1996      Haywood 37087  37087       44  2110
37089 0.095     1.471  2047    2047    Henderson 37089  37089       45  2574
37091 0.097     1.670  1833    1833     Hertford 37091  37091       46  1452
37093 0.098     1.262  2097    2097         Hoke 37093  37093       47  1494
37095 0.167     2.709  2099    2099         Hyde 37095  37095       48   338
37097 0.155     1.781  1947    1947      Iredell 37097  37097       49  4139
37099 0.125     1.601  2039    2039      Jackson 37099  37099       50  1143
37101 0.207     1.851  1989    1989     Johnston 37101  37101       51  3999
37103 0.121     1.978  2091    2091        Jones 37103  37103       52   578
37105 0.065     1.093  2026    2026          Lee 37105  37105       53  2252
37107 0.104     1.548  2065    2065       Lenoir 37107  37107       54  3589
37109 0.078     1.384  2034    2034      Lincoln 37109  37109       55  2216
37111 0.116     1.664  1964    1964     McDowell 37111  37111       56  1946
37113 0.131     1.677  2082    2082        Macon 37113  37113       57   797
37115 0.118     1.601  1946    1946      Madison 37115  37115       58   765
37117 0.118     1.899  1937    1937       Martin 37117  37117       59  1549
37119 0.143     1.887  2041    2041  Mecklenburg 37119  37119       60 21588
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[19,] -81.45081 36.19062
[20,] -81.49932 36.13604
[21,] -81.54610 36.11139
[22,] -81.65900 36.11759
[23,] -81.80622 36.10456



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -81.69129  36.22480

Slot "ID":
[1] "37189"

Slot "area":
[1] 0.08142148


[[96]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -78.00720  35.35737

Slot "area":
[1] 0.1424129

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -78.16319 35.18229
 [2,] -78.16518 35.19322
 [3,] -78.25740 35.22095
 [4,] -78.31013 35.28028
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[10,] -78.14597 35.42899
[11,] -78.06533 35.58204
[12,] -78.06049 35.59467
[13,] -78.00214 35.57600
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[15,] -77.82629 35.57426
[16,] -77.83058 35.42363
[17,] -77.80411 35.40180
[18,] -77.80518 35.36459
[19,] -77.83006 35.34235
[20,] -77.83658 35.17174
[21,] -77.88767 35.15496
[22,] -77.89416 35.14418
[23,] -77.91398 35.15997
[24,] -77.94469 35.16823
[25,] -77.96393 35.16402
[26,] -78.00216 35.18648
[27,] -78.03651 35.18569
[28,] -78.08753 35.17019
[29,] -78.16319 35.18229



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -78.00720  35.35737

Slot "ID":
[1] "37191"

Slot "area":
[1] 0.1424129


[[97]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -81.15963  36.20160

Slot "area":
[1] 0.1991111

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -81.02057 36.03493
 [2,] -81.08408 36.02077
 [3,] -81.12406 36.03128
 [4,] -81.15746 36.02098
 [5,] -81.23601 36.02382
 [6,] -81.32187 35.98933
 [7,] -81.34735 36.01536
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 [9,] -81.39815 36.05606
[10,] -81.42963 36.06566
[11,] -81.45444 36.08306
[12,] -81.51731 36.09543
[13,] -81.54610 36.11139
[14,] -81.49932 36.13604
[15,] -81.45081 36.19062
[16,] -81.47276 36.23436
[17,] -81.45289 36.23959
[18,] -81.43104 36.26072
[19,] -81.41233 36.26729
[20,] -81.40639 36.28505
[21,] -81.36745 36.27870
[22,] -81.35413 36.29972
[23,] -81.36569 36.33905
[24,] -81.36137 36.35316
[25,] -81.32899 36.36350
[26,] -81.26424 36.35241
[27,] -81.23989 36.36536
[28,] -81.17667 36.41544
[29,] -81.15337 36.42474
[30,] -81.13840 36.41763
[31,] -81.12938 36.42633
[32,] -81.11331 36.42285
[33,] -81.09856 36.43115
[34,] -81.08425 36.42992
[35,] -81.04280 36.41034
[36,] -81.02464 36.37783
[37,] -81.00278 36.36668
[38,] -80.98284 36.37183
[39,] -80.97795 36.39138
[40,] -80.95639 36.40380
[41,] -80.92457 36.37275
[42,] -80.88892 36.35443
[43,] -80.87086 36.32462
[44,] -80.87438 36.23388
[45,] -80.87741 36.05241
[46,] -80.98895 36.05335
[47,] -81.02057 36.03493



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -81.15963  36.20160

Slot "ID":
[1] "37193"

Slot "area":
[1] 0.1991111


[[98]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -77.92273  35.69914

Slot "area":
[1] 0.09402531

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -78.06533 35.58204
 [2,] -78.12490 35.59752
 [3,] -78.16245 35.68298
 [4,] -78.16180 35.70929
 [5,] -78.18693 35.72511
 [6,] -77.87305 35.84471
 [7,] -77.84492 35.83558
 [8,] -77.83015 35.85450
 [9,] -77.76714 35.83686
[10,] -77.75500 35.82484
[11,] -77.75749 35.79810
[12,] -77.73315 35.73955
[13,] -77.67122 35.67027
[14,] -77.69833 35.65400
[15,] -77.82629 35.57426
[16,] -77.82718 35.58290
[17,] -78.00214 35.57600
[18,] -78.06049 35.59467
[19,] -78.06533 35.58204



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -77.92273  35.69914

Slot "ID":
[1] "37195"

Slot "area":
[1] 0.09402531


[[99]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -80.66411  36.15479

Slot "area":
[1] 0.08646725

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -80.49554 36.04327
 [2,] -80.68958 36.04548
 [3,] -80.87741 36.05241
 [4,] -80.87438 36.23388
 [5,] -80.78270 36.24858
 [6,] -80.76630 36.26184
 [7,] -80.75256 36.25830
 [8,] -80.73437 36.26476
 [9,] -80.72404 36.25847
[10,] -80.69666 36.25909
[11,] -80.66744 36.24610
[12,] -80.62431 36.27310
[13,] -80.59060 36.26828
[14,] -80.55415 36.27843
[15,] -80.54501 36.27666
[16,] -80.53688 36.25674
[17,] -80.47639 36.25473
[18,] -80.45634 36.24256
[19,] -80.44081 36.21949
[20,] -80.44481 36.12331
[21,] -80.49198 36.10770
[22,] -80.50499 36.09494
[23,] -80.50825 36.07088
[24,] -80.49554 36.04327



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -80.66411  36.15479

Slot "ID":
[1] "37197"

Slot "area":
[1] 0.08646725


[[100]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -82.30771  35.89220

Slot "area":
[1] 0.07988339

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
           [,1]     [,2]
 [1,] -82.27921 35.69756
 [2,] -82.28158 35.72020
 [3,] -82.32185 35.73985
 [4,] -82.34185 35.76351
 [5,] -82.34540 35.80519
 [6,] -82.37525 35.81640
 [7,] -82.40582 35.81397
 [8,] -82.44148 35.88223
 [9,] -82.48709 35.90488
[10,] -82.48395 35.94761
[11,] -82.50694 35.97254
[12,] -82.47520 35.99318
[13,] -82.40842 36.07532
[14,] -82.38518 36.07851
[15,] -82.34450 36.07024
[16,] -82.34026 36.02540
[17,] -82.32968 36.01426
[18,] -82.30969 36.01147
[19,] -82.30170 36.02933
[20,] -82.25886 35.98568
[21,] -82.24062 35.98420
[22,] -82.23210 36.00388
[23,] -82.19905 36.00177
[24,] -82.19577 35.94738
[25,] -82.18461 35.93488
[26,] -82.14957 35.92145
[27,] -82.14101 35.90027
[28,] -82.14665 35.81848
[29,] -82.11885 35.81853
[30,] -82.15459 35.79837
[31,] -82.16429 35.76052
[32,] -82.21178 35.71699
[33,] -82.27921 35.69756



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -82.30771  35.89220

Slot "ID":
[1] "37199"

Slot "area":
[1] 0.07988339



Slot "plotOrder":
  [1]  82  24  78   9  92  71  10  51  31   7  76  97  67  42  63  19   8  25
 [19]  26  41  11  74  48  90  49  43  79  66  81  29  44  60  39  86  96  64
 [37]  87   4  80  12  33  57  62  35  50  16  85  14  52  77  20  93  59  58
 [55]  23  56   5  17   1  73  34  18  68  54  84  94  89  47  88  46  45  98
 [73]  28  13  36  37  99  69  95 100  83  55  38  32  91  40  27  30   2  53
 [91]   6  72  15   3  75  61  70  22  21  65

Slot "bbox":
        min       max
x -84.32385 -75.45698
y  33.88199  36.58965

Slot "proj4string":
CRS arguments: +proj=longlat +datum=WGS84



In [68]:

    
xy = cbind(kn.nb[k, ]["x"], kn.nb[k, ]["y"])



In [70]:

    
pts = SpatialPoints(xy)



In [80]:

    
kn.nb[k, ]









    Out[80]:





x y statistic cluster pvalue size

	37117 -77.10766 35.83635 -77.10766 35.83635 -77.10766 35.83635



In [82]:

    
class(pts)









    Out[82]:




'SpatialPoints'



In [77]:

    
class(nc)









    Out[77]:




'SpatialPolygonsDataFrame'



In [ ]:



In [33]:

    
nc@data$NAME[j]









    



Error in NextMethod("["):  オブジェクト 'j' がありません



In [34]:

    
i <- rank(spDistsN1(xy, xy[j, ], longlat = TRUE))
 plot(nc)
 plot(nc[i < kn.nb$size[k], ], col = "red", add = TRUE)
 plot(kn.nb[k, ], pch = 19, cex = 2, add = TRUE)









    



Error in inherits(pt, "SpatialPoints"):  オブジェクト 'j' がありません 






    



Error in file(con, "rb"):  コネクションを開くことができません



In [35]:

    
v <- kn.nb$pvalue
 v[which.min(v)] <- NA
 k <- which.min(v)
 k









    Out[35]:




4



In [36]:

    
j <- overlay(kn.nb[k, ], nc)
 nc@data$NAME[j]









    



Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘overlay’ for signature ‘"SpatialPointsDataFrame"’






    



Error in NextMethod("["):  オブジェクト 'j' がありません



In [37]:

    
i <- rank(spDistsN1(xy, xy[j, ], longlat = TRUE))
 plot(nc)
 plot(nc[i < kn.nb$size[k], ], col = "red", add = TRUE)
 plot(kn.nb[k, ], pch = 19, cex = 2, add = TRUE)









    



Error in inherits(pt, "SpatialPoints"):  オブジェクト 'j' がありません 






    



Error in file(con, "rb"):  コネクションを開くことができません



In [46]:

    
kn.nb









    Out[46]:





                coordinates  statistic cluster pvalue size
37015  (-76.98481, 36.0619)  28388.070       1  0.019   27
37053 (-76.05211, 36.39714)   7360.441       1  0.018   27
37065 (-77.59819, 35.90722) 144050.649       1  0.017   14
37091 (-76.99478, 36.36145)  26739.675       1  0.016    9
37131 (-77.41056, 36.42228) 197884.647       1  0.012    5
37187 (-76.58298, 35.82505)  22824.250       1  0.014   26



In [35]:

    
j <- over(kn.nb, nc)









    



Error: identicalCRS(x, y) is not TRUE



In [39]:

    
find.row <- function(j, s) {
     i <- rank(spDistsN1(xy, xy[j, ], longlat = TRUE))
     return(i < s)
 }



In [40]:

    
clst <- mapply(find.row, j, as(kn.nb, "data.frame")[, 6])
 clst2 <- as.logical(rowSums(clst))









    



Error in mapply(find.row, j, as(kn.nb, "data.frame")[, 6]):  オブジェクト 'j' がありません 






    



Error in rowSums(clst):  オブジェクト 'clst' がありません



In [41]:

    
cols <- rep("white", length(clst2))
 cols[clst2] <- "red"
 plot(nc, col = cols)









    



Error in eval(expr, envir, enclos):  オブジェクト 'clst2' がありません 






    



Error in cols[clst2] <- "red":  オブジェクト 'cols' がありません 






    



Error in plot.SpatialPolygons(x, ...):  オブジェクト 'cols' がありません 






    



Error in file(con, "rb"):  コネクションを開くことができません



In [42]:

    
##==============================================================
 p1 <- kn.nb[order(kn.nb@data$pvalue), ]
 plot(p1, pch = 19, col = "white", cex = 1.8, add = TRUE)
 plot(p1, pch = as.character(1:nrow(p1)), add = TRUE)
##==============================================================
 library(splancs)
 data(southlancs)
##==============================================================
 d <- dsquare(southlancs.pts, old.incinerator)
##==============================================================
 larynx <- sum(southlancs.cc)
 lung <- length(southlancs.cc) - larynx
 r <- larynx/lung
##==============================================================
 result <- tribble(ccflag = southlancs.cc, vars = d, alphas = 10, 
     betas = 10, rho = r)
##==============================================================
 print.ribfit(result)
##==============================================================
 library(DCluster)
 dat2 <- cbind(dat, xy1)
 i <- which(as(nc, "data.frame")["NAME"] == "Robeson")
 stone.stat(dat2, region = i, lambda = 1)
##==============================================================
 stone.test(Observed ~ offset(log(Expected)), data = dat2, 
     model = "poisson", R = 99, region = i, lambda = 1)









    



Error in plot.xy(xy.coords(x, y), type = type, ...): plot.new has not been called yet






    



Error in plot.xy(xy.coords(x, y), type = type, ...): plot.new has not been called yet






    



Spatial Point Pattern Analysis Code in S-Plus
 
 Version 2 - Spatial and Space-Time analysis







    



[1] 3
Call:
tribble(ccflag = southlancs.cc, vars = d, alphas = 10, betas = 10, 
    rho = r)
Kcode = 2

Distance decay parameters:
        Alpha      Beta
[1,] 53.60319 3.724e-06

rho parameter : 0.0584454548176267

     log-likelihood : -211.514500683159
null log-likelihood : -217.085135422081

        D = 2(L-Lo) : 11.1412694778443






    Out[42]:





	1
		2.00345676037607
	region
		2








    Out[42]:





Stone's Test for raised incidence around locations 

	Type of boots.: parametric 
	Model used when sampling: Poisson 
	Number of simulations: 99 
	Statistic:  2.003457 
	p-value :  0.01



In [ ]:



In [ ]:



In [ ]:



In [ ]:

x	numeric	grid eastings
y	numeric	grid northings
area	list	bounding box with xl, xu, yl, yu
poly	array	polygon boundary with columns x and y

	Ii	E.Ii	Var.Ii	Z.Ii	Pr(z > 0)
37001	-0.38296680	-0.06060606	5.66778683	-0.13540522	0.55385425
37003	2.84892433	-0.04040404	3.77639636	1.48681905	0.06853130
37005	1.98692859	-0.03030303	2.83039504	1.19903605	0.11525696
37007	-3.68378017	-0.04040404	3.77639636	-1.87484437	0.96959293
37009	1.89322430	-0.03030303	2.83039504	1.14333852	0.12644903
37011	3.20602844	-0.05050505	4.72219363	1.49859284	0.06698965

	x	y	statistic	cluster	pvalue	size
37007	-80.10407	34.9752	106625.2	1	0.001	1
37013	-76.83809	35.58468	8869.925	1	0.001	24
37015	-76.98481	36.0619	28388.07	1	0.001	27
37017	-78.56013	34.60932	333.1234	1	0.002	2
37019	-78.25073	34.07663	1782.277	1	0.001	9
37029	-76.23435	36.4012	7360.441	1	0.001	27
37041	-76.61648	36.14893	7360.441	1	0.001	27
37047	-78.65492	34.26323	51695.41	1	0.001	4
37049	-77.17801	35.23062	236.7854	1	0.004	16
37053	-76.05211	36.39714	7360.441	1	0.001	27
37055	-75.86483	35.76263	8241.024	1	0.002	25
37065	-77.59819	35.90722	144050.6	1	0.001	14
37073	-76.70448	36.44423	11269.29	1	0.001	25
37079	-77.67889	35.4823	220.8674	1	0.006	14
37083	-77.65628	36.25305	45417.54	1	0.001	17
37091	-76.99478	36.36145	26739.67	1	0.001	9
37093	-79.23592	35.01168	1438.994	1	0.001	4
37095	-76.25562	35.54225	8241.024	1	0.001	25
37103	-77.35634	35.01854	58.75584	1	0.014	15
37107	-77.64549	35.23284	82.17384	1	0.007	11
37117	-77.10766	35.83635	92296.62	1	0.001	17
37127	-77.98667	35.96233	155.1066	1	0.005	7
37129	-77.89578	34.26166	71.04705	1	0.008	11
37131	-77.41056	36.42228	197884.6	1	0.001	5
37137	-76.76878	35.13564	118.28	1	0.007	21
37139	-76.3146	36.31237	7360.441	1	0.001	27
37141	-77.91628	34.52686	137.0173	1	0.004	12
37143	-76.45461	36.20488	7360.441	1	0.001	27
37147	-77.3779	35.58916	150692.5	1	0.001	17
37153	-79.74231	35.00146	4442.8	1	0.001	9
37155	-79.10111	34.63874	6426.299	1	0.001	3
37165	-79.4772	34.83949	152040.2	1	0.001	6
37177	-76.21982	35.8096	8241.024	1	0.001	25
37185	-78.11043	36.39697	406.239	1	0.004	9
37187	-76.58298	35.82505	22824.25	1	0.001	26
37195	-77.92273	35.69914	301.1284	1	0.001	11

第3章 地理空間分布パターン

Locations of homes of juvenile offenders

Description

Usage

Format

Source

References

第3章地理空間分布パターン