patron de puntos
Santiago Colorado y Sebastian Herrera
1/2/2022
set.seed(1000579855)
Seqlat<-seq(from=-73.3, to=-73.25, by=.001)
Seqlong<-seq(from=5.54,to=5.58,by=.001)
Latitude<- sample(Seqlat,size=100,replace=TRUE)
Longitude<-sample(Seqlong,size=100,replace=TRUE)
xy<-data.frame(x=Longitude,y=Latitude)
plot(xy$x,xy$y)
SMI=sort.int(runif(100,0.7,0.95),partial =10)
NDVI=sort.int(rnorm(100,0.45,0.06), partial = 10)
LST=sort.int(26*rbeta(100,shape1=0.87,shape2=0.91),partial = 10)
crop=factor(ifelse(rgamma(n=100,rate=0.8,shape=0.5)<0.5,0,1))
df1=data.frame(xy,SMI,NDVI,LST,crop)
df1## x y SMI NDVI LST crop
## 1 5.577 -73.270 0.7110707 0.3378694 0.42589995 1
## 2 5.575 -73.298 0.7045101 0.3466406 0.41286542 1
## 3 5.557 -73.288 0.7087018 0.3729947 0.42622543 0
## 4 5.546 -73.280 0.7080012 0.3568273 0.09257365 0
## 5 5.545 -73.264 0.7159930 0.3655546 0.81252482 0
## 6 5.547 -73.271 0.7019546 0.3735510 0.56147691 0
## 7 5.556 -73.268 0.7152981 0.3733371 0.75609694 0
## 8 5.563 -73.286 0.7169576 0.3788267 0.52093506 0
## 9 5.553 -73.279 0.7188258 0.3803645 1.19552604 0
## 10 5.540 -73.257 0.7190025 0.3810685 1.19658615 0
## 11 5.549 -73.264 0.7190369 0.3814067 1.34499416 1
## 12 5.580 -73.273 0.7338021 0.3972094 1.38168675 1
## 13 5.572 -73.291 0.7347987 0.3871763 6.35020169 1
## 14 5.543 -73.285 0.7202338 0.3908236 11.04652599 0
## 15 5.559 -73.282 0.7320041 0.3942129 6.21859948 1
## 16 5.547 -73.277 0.7294736 0.3935539 4.30681980 0
## 17 5.555 -73.250 0.7386254 0.3920445 14.18158307 1
## 18 5.547 -73.254 0.7374480 0.3906205 14.49186671 0
## 19 5.545 -73.296 0.7416434 0.3946744 10.21982328 1
## 20 5.577 -73.253 0.7380110 0.3964335 11.20813874 1
## 21 5.545 -73.298 0.7584693 0.3884163 13.66946826 0
## 22 5.561 -73.300 0.7697956 0.4491871 6.29672710 0
## 23 5.551 -73.289 0.7567680 0.4700328 9.70089049 0
## 24 5.550 -73.294 0.7770173 0.4488691 7.69773606 1
## 25 5.549 -73.252 0.7482631 0.4625960 2.09936555 0
## 26 5.563 -73.296 0.7433650 0.4084605 11.55034137 1
## 27 5.580 -73.287 0.7469922 0.4313955 7.22300236 1
## 28 5.546 -73.273 0.7691206 0.4135666 3.24344481 1
## 29 5.568 -73.261 0.7566106 0.4054611 3.46134301 1
## 30 5.565 -73.282 0.7423937 0.4441127 5.64214584 0
## 31 5.545 -73.264 0.7467557 0.4596761 10.99215288 0
## 32 5.560 -73.290 0.8835611 0.4185906 9.89531118 0
## 33 5.548 -73.263 0.9363114 0.4229683 6.05606421 1
## 34 5.571 -73.290 0.8327370 0.4684080 7.13610701 0
## 35 5.575 -73.260 0.9487781 0.4393326 3.86150726 0
## 36 5.551 -73.267 0.9202944 0.4184583 3.43661932 1
## 37 5.577 -73.289 0.8957445 0.4419551 8.35327481 1
## 38 5.545 -73.298 0.9428688 0.4239987 9.01921544 1
## 39 5.573 -73.264 0.9426985 0.4525001 8.09327091 0
## 40 5.551 -73.254 0.8360254 0.4291286 10.94965588 1
## 41 5.569 -73.265 0.8474294 0.4645766 7.83494604 0
## 42 5.549 -73.265 0.8593841 0.4554685 2.91986904 0
## 43 5.580 -73.267 0.9024785 0.4518823 9.09754992 0
## 44 5.573 -73.262 0.7935286 0.4335222 2.70033511 0
## 45 5.563 -73.253 0.9071142 0.3984627 8.42126515 1
## 46 5.546 -73.258 0.8058787 0.4644484 9.89877747 1
## 47 5.574 -73.254 0.7936852 0.4622095 6.98432405 1
## 48 5.554 -73.299 0.8109977 0.4062288 2.77108840 0
## 49 5.540 -73.299 0.8000597 0.4367658 4.89101267 1
## 50 5.570 -73.279 0.8538442 0.4613475 13.63556628 1
## 51 5.554 -73.251 0.9035943 0.4398300 6.93182457 1
## 52 5.552 -73.276 0.8813359 0.4234928 5.41928287 1
## 53 5.547 -73.280 0.8683766 0.4638765 7.73024695 0
## 54 5.545 -73.252 0.8195909 0.4126734 2.66443029 0
## 55 5.578 -73.260 0.9096037 0.4342406 3.14084693 0
## 56 5.540 -73.270 0.8005186 0.4018448 14.04958141 0
## 57 5.575 -73.289 0.8843469 0.4176299 7.16174132 0
## 58 5.574 -73.270 0.7862926 0.4436692 15.80485938 1
## 59 5.544 -73.294 0.8814684 0.4504637 21.75577526 1
## 60 5.546 -73.290 0.7931877 0.4634360 17.91751894 0
## 61 5.576 -73.268 0.8419918 0.4709403 17.21845353 0
## 62 5.568 -73.256 0.8096942 0.4564409 19.65261254 0
## 63 5.558 -73.285 0.8770435 0.4690268 17.86453803 1
## 64 5.557 -73.278 0.9052334 0.4670000 21.71478240 0
## 65 5.573 -73.260 0.8082419 0.4478923 17.95328560 1
## 66 5.553 -73.287 0.8737961 0.4231107 20.00049780 0
## 67 5.566 -73.284 0.9399988 0.5413283 21.91175245 1
## 68 5.567 -73.266 0.8579748 0.4845414 16.45308889 0
## 69 5.580 -73.287 0.8456122 0.5123793 19.55470052 1
## 70 5.576 -73.294 0.8996512 0.5298505 17.58907590 0
## 71 5.555 -73.259 0.9458732 0.5327747 20.80298240 0
## 72 5.541 -73.258 0.8792382 0.5414217 17.07974429 1
## 73 5.574 -73.297 0.8855610 0.4749562 21.11227030 0
## 74 5.545 -73.299 0.9108187 0.5150462 17.06846987 0
## 75 5.560 -73.267 0.8426215 0.5782634 16.79374111 0
## 76 5.556 -73.269 0.7853216 0.4812471 20.68221081 1
## 77 5.555 -73.291 0.9027559 0.4751730 19.79326771 0
## 78 5.559 -73.252 0.8687392 0.5435544 21.94185458 1
## 79 5.561 -73.259 0.9428625 0.5136234 21.75379109 0
## 80 5.562 -73.254 0.8472902 0.4726600 20.08445494 0
## 81 5.552 -73.292 0.8623980 0.5928488 15.72687230 0
## 82 5.557 -73.268 0.7898995 0.5103821 20.55782129 0
## 83 5.574 -73.281 0.9186096 0.4860201 21.25512167 0
## 84 5.559 -73.286 0.9359754 0.5458797 17.89271229 1
## 85 5.575 -73.278 0.8354318 0.5101896 16.68875759 1
## 86 5.558 -73.285 0.8324613 0.5651807 24.00791902 1
## 87 5.542 -73.270 0.8787350 0.4863785 25.25284123 0
## 88 5.577 -73.272 0.8490814 0.4798723 23.76197107 1
## 89 5.556 -73.270 0.9077144 0.5505521 23.74986918 1
## 90 5.568 -73.299 0.8852120 0.5271529 22.24167996 0
## 91 5.548 -73.266 0.8119120 0.5325476 24.68434904 0
## 92 5.543 -73.253 0.9451603 0.5561369 23.63501988 1
## 93 5.555 -73.267 0.9407233 0.5561518 23.78582354 1
## 94 5.572 -73.282 0.8153107 0.4864211 23.95958793 0
## 95 5.565 -73.290 0.8871847 0.4960460 23.36287041 0
## 96 5.579 -73.273 0.8283922 0.5318362 23.60495650 1
## 97 5.545 -73.267 0.8502388 0.5014892 22.05131932 0
## 98 5.561 -73.290 0.8216787 0.4713482 25.67151845 1
## 99 5.540 -73.256 0.7929413 0.5011147 25.59871889 1
## 100 5.560 -73.253 0.8251805 0.4937176 25.32399297 1#1
#xy<-xy[!duplicated(xy),]
df1<-df1[!duplicated(df1),]
plot(xy$x,xy$y)
#2
dataxy=ppp(xy$x,xy$y,xrange=c(min(xy$x),max(xy$x)),yrange=c(min(xy$y),max(xy$y)))## Warning: data contain duplicated pointsany(duplicated(dataxy))## [1] TRUEY<-unique(dataxy)
any(duplicated(Y))## [1] FALSE#3
dataxy1=ppp(xy$x,xy$y,xrange=c(min(xy$x),max(xy$x)),yrange=c(min(xy$y),max(xy$y)),marks=df1$crop)## Warning: data contain duplicated pointsany(duplicated(dataxy1))## [1] TRUEY1<-unique(dataxy1)
any(duplicated(Y1))## [1] FALSE#4
dataxy2=ppp(xy$x,xy$y,xrange=c(min(xy$x),max(xy$x)),yrange=c(min(xy$y),max(xy$y)),marks=df1$NDVI)
any(duplicated(dataxy2))## [1] FALSEY2<-unique(dataxy2)#5
plot(Y,size=0.7,main = "Grafico Marca xy", cols= "green")
plot(Y1,size=0.8,main = "Grafico Marca crop", cols= "red")
plot(Y2,size=0.00155,main = "Grafico Marca NDVI",cols = "blue")
#6
dataxy3=ppp(xy$x,xy$y,xrange=c(min(xy$x),max(xy$x)),yrange=c(min(xy$y),max(xy$y)),marks=df1[,c(3:6)])
Y3<-unique(dataxy3)
par(mfrow=c(1,2))
plot(Y3,which.marks = "NDVI",size=0.0009)
plot(Y3,which.marks="crop",size=0.7) 
par(mfrow=c(1,1))
plot(Y3,size=0.002,main = "GrƔfica de todas las marcas")
windo1=owin(c(4.456,4.528),c(-73.289,-73.277));windo1## window: rectangle = [4.456, 4.528] x [-73.289, -73.277] units#7
npoints(Y3) ## [1] 100marks(Y3) ## SMI NDVI LST crop
## 1 0.7110707 0.3378694 0.42589995 1
## 2 0.7045101 0.3466406 0.41286542 1
## 3 0.7087018 0.3729947 0.42622543 0
## 4 0.7080012 0.3568273 0.09257365 0
## 5 0.7159930 0.3655546 0.81252482 0
## 6 0.7019546 0.3735510 0.56147691 0
## 7 0.7152981 0.3733371 0.75609694 0
## 8 0.7169576 0.3788267 0.52093506 0
## 9 0.7188258 0.3803645 1.19552604 0
## 10 0.7190025 0.3810685 1.19658615 0
## 11 0.7190369 0.3814067 1.34499416 1
## 12 0.7338021 0.3972094 1.38168675 1
## 13 0.7347987 0.3871763 6.35020169 1
## 14 0.7202338 0.3908236 11.04652599 0
## 15 0.7320041 0.3942129 6.21859948 1
## 16 0.7294736 0.3935539 4.30681980 0
## 17 0.7386254 0.3920445 14.18158307 1
## 18 0.7374480 0.3906205 14.49186671 0
## 19 0.7416434 0.3946744 10.21982328 1
## 20 0.7380110 0.3964335 11.20813874 1
## 21 0.7584693 0.3884163 13.66946826 0
## 22 0.7697956 0.4491871 6.29672710 0
## 23 0.7567680 0.4700328 9.70089049 0
## 24 0.7770173 0.4488691 7.69773606 1
## 25 0.7482631 0.4625960 2.09936555 0
## 26 0.7433650 0.4084605 11.55034137 1
## 27 0.7469922 0.4313955 7.22300236 1
## 28 0.7691206 0.4135666 3.24344481 1
## 29 0.7566106 0.4054611 3.46134301 1
## 30 0.7423937 0.4441127 5.64214584 0
## 31 0.7467557 0.4596761 10.99215288 0
## 32 0.8835611 0.4185906 9.89531118 0
## 33 0.9363114 0.4229683 6.05606421 1
## 34 0.8327370 0.4684080 7.13610701 0
## 35 0.9487781 0.4393326 3.86150726 0
## 36 0.9202944 0.4184583 3.43661932 1
## 37 0.8957445 0.4419551 8.35327481 1
## 38 0.9428688 0.4239987 9.01921544 1
## 39 0.9426985 0.4525001 8.09327091 0
## 40 0.8360254 0.4291286 10.94965588 1
## 41 0.8474294 0.4645766 7.83494604 0
## 42 0.8593841 0.4554685 2.91986904 0
## 43 0.9024785 0.4518823 9.09754992 0
## 44 0.7935286 0.4335222 2.70033511 0
## 45 0.9071142 0.3984627 8.42126515 1
## 46 0.8058787 0.4644484 9.89877747 1
## 47 0.7936852 0.4622095 6.98432405 1
## 48 0.8109977 0.4062288 2.77108840 0
## 49 0.8000597 0.4367658 4.89101267 1
## 50 0.8538442 0.4613475 13.63556628 1
## 51 0.9035943 0.4398300 6.93182457 1
## 52 0.8813359 0.4234928 5.41928287 1
## 53 0.8683766 0.4638765 7.73024695 0
## 54 0.8195909 0.4126734 2.66443029 0
## 55 0.9096037 0.4342406 3.14084693 0
## 56 0.8005186 0.4018448 14.04958141 0
## 57 0.8843469 0.4176299 7.16174132 0
## 58 0.7862926 0.4436692 15.80485938 1
## 59 0.8814684 0.4504637 21.75577526 1
## 60 0.7931877 0.4634360 17.91751894 0
## 61 0.8419918 0.4709403 17.21845353 0
## 62 0.8096942 0.4564409 19.65261254 0
## 63 0.8770435 0.4690268 17.86453803 1
## 64 0.9052334 0.4670000 21.71478240 0
## 65 0.8082419 0.4478923 17.95328560 1
## 66 0.8737961 0.4231107 20.00049780 0
## 67 0.9399988 0.5413283 21.91175245 1
## 68 0.8579748 0.4845414 16.45308889 0
## 69 0.8456122 0.5123793 19.55470052 1
## 70 0.8996512 0.5298505 17.58907590 0
## 71 0.9458732 0.5327747 20.80298240 0
## 72 0.8792382 0.5414217 17.07974429 1
## 73 0.8855610 0.4749562 21.11227030 0
## 74 0.9108187 0.5150462 17.06846987 0
## 75 0.8426215 0.5782634 16.79374111 0
## 76 0.7853216 0.4812471 20.68221081 1
## 77 0.9027559 0.4751730 19.79326771 0
## 78 0.8687392 0.5435544 21.94185458 1
## 79 0.9428625 0.5136234 21.75379109 0
## 80 0.8472902 0.4726600 20.08445494 0
## 81 0.8623980 0.5928488 15.72687230 0
## 82 0.7898995 0.5103821 20.55782129 0
## 83 0.9186096 0.4860201 21.25512167 0
## 84 0.9359754 0.5458797 17.89271229 1
## 85 0.8354318 0.5101896 16.68875759 1
## 86 0.8324613 0.5651807 24.00791902 1
## 87 0.8787350 0.4863785 25.25284123 0
## 88 0.8490814 0.4798723 23.76197107 1
## 89 0.9077144 0.5505521 23.74986918 1
## 90 0.8852120 0.5271529 22.24167996 0
## 91 0.8119120 0.5325476 24.68434904 0
## 92 0.9451603 0.5561369 23.63501988 1
## 93 0.9407233 0.5561518 23.78582354 1
## 94 0.8153107 0.4864211 23.95958793 0
## 95 0.8871847 0.4960460 23.36287041 0
## 96 0.8283922 0.5318362 23.60495650 1
## 97 0.8502388 0.5014892 22.05131932 0
## 98 0.8216787 0.4713482 25.67151845 1
## 99 0.7929413 0.5011147 25.59871889 1
## 100 0.8251805 0.4937176 25.32399297 1coords(Y3) ## x y
## 1 5.577 -73.270
## 2 5.575 -73.298
## 3 5.557 -73.288
## 4 5.546 -73.280
## 5 5.545 -73.264
## 6 5.547 -73.271
## 7 5.556 -73.268
## 8 5.563 -73.286
## 9 5.553 -73.279
## 10 5.540 -73.257
## 11 5.549 -73.264
## 12 5.580 -73.273
## 13 5.572 -73.291
## 14 5.543 -73.285
## 15 5.559 -73.282
## 16 5.547 -73.277
## 17 5.555 -73.250
## 18 5.547 -73.254
## 19 5.545 -73.296
## 20 5.577 -73.253
## 21 5.545 -73.298
## 22 5.561 -73.300
## 23 5.551 -73.289
## 24 5.550 -73.294
## 25 5.549 -73.252
## 26 5.563 -73.296
## 27 5.580 -73.287
## 28 5.546 -73.273
## 29 5.568 -73.261
## 30 5.565 -73.282
## 31 5.545 -73.264
## 32 5.560 -73.290
## 33 5.548 -73.263
## 34 5.571 -73.290
## 35 5.575 -73.260
## 36 5.551 -73.267
## 37 5.577 -73.289
## 38 5.545 -73.298
## 39 5.573 -73.264
## 40 5.551 -73.254
## 41 5.569 -73.265
## 42 5.549 -73.265
## 43 5.580 -73.267
## 44 5.573 -73.262
## 45 5.563 -73.253
## 46 5.546 -73.258
## 47 5.574 -73.254
## 48 5.554 -73.299
## 49 5.540 -73.299
## 50 5.570 -73.279
## 51 5.554 -73.251
## 52 5.552 -73.276
## 53 5.547 -73.280
## 54 5.545 -73.252
## 55 5.578 -73.260
## 56 5.540 -73.270
## 57 5.575 -73.289
## 58 5.574 -73.270
## 59 5.544 -73.294
## 60 5.546 -73.290
## 61 5.576 -73.268
## 62 5.568 -73.256
## 63 5.558 -73.285
## 64 5.557 -73.278
## 65 5.573 -73.260
## 66 5.553 -73.287
## 67 5.566 -73.284
## 68 5.567 -73.266
## 69 5.580 -73.287
## 70 5.576 -73.294
## 71 5.555 -73.259
## 72 5.541 -73.258
## 73 5.574 -73.297
## 74 5.545 -73.299
## 75 5.560 -73.267
## 76 5.556 -73.269
## 77 5.555 -73.291
## 78 5.559 -73.252
## 79 5.561 -73.259
## 80 5.562 -73.254
## 81 5.552 -73.292
## 82 5.557 -73.268
## 83 5.574 -73.281
## 84 5.559 -73.286
## 85 5.575 -73.278
## 86 5.558 -73.285
## 87 5.542 -73.270
## 88 5.577 -73.272
## 89 5.556 -73.270
## 90 5.568 -73.299
## 91 5.548 -73.266
## 92 5.543 -73.253
## 93 5.555 -73.267
## 94 5.572 -73.282
## 95 5.565 -73.290
## 96 5.579 -73.273
## 97 5.545 -73.267
## 98 5.561 -73.290
## 99 5.540 -73.256
## 100 5.560 -73.253as.owin(Y3) ## window: rectangle = [5.54, 5.58] x [-73.3, -73.25] unitsas.data.frame(Y3) ## x y SMI NDVI LST crop
## 1 5.577 -73.270 0.7110707 0.3378694 0.42589995 1
## 2 5.575 -73.298 0.7045101 0.3466406 0.41286542 1
## 3 5.557 -73.288 0.7087018 0.3729947 0.42622543 0
## 4 5.546 -73.280 0.7080012 0.3568273 0.09257365 0
## 5 5.545 -73.264 0.7159930 0.3655546 0.81252482 0
## 6 5.547 -73.271 0.7019546 0.3735510 0.56147691 0
## 7 5.556 -73.268 0.7152981 0.3733371 0.75609694 0
## 8 5.563 -73.286 0.7169576 0.3788267 0.52093506 0
## 9 5.553 -73.279 0.7188258 0.3803645 1.19552604 0
## 10 5.540 -73.257 0.7190025 0.3810685 1.19658615 0
## 11 5.549 -73.264 0.7190369 0.3814067 1.34499416 1
## 12 5.580 -73.273 0.7338021 0.3972094 1.38168675 1
## 13 5.572 -73.291 0.7347987 0.3871763 6.35020169 1
## 14 5.543 -73.285 0.7202338 0.3908236 11.04652599 0
## 15 5.559 -73.282 0.7320041 0.3942129 6.21859948 1
## 16 5.547 -73.277 0.7294736 0.3935539 4.30681980 0
## 17 5.555 -73.250 0.7386254 0.3920445 14.18158307 1
## 18 5.547 -73.254 0.7374480 0.3906205 14.49186671 0
## 19 5.545 -73.296 0.7416434 0.3946744 10.21982328 1
## 20 5.577 -73.253 0.7380110 0.3964335 11.20813874 1
## 21 5.545 -73.298 0.7584693 0.3884163 13.66946826 0
## 22 5.561 -73.300 0.7697956 0.4491871 6.29672710 0
## 23 5.551 -73.289 0.7567680 0.4700328 9.70089049 0
## 24 5.550 -73.294 0.7770173 0.4488691 7.69773606 1
## 25 5.549 -73.252 0.7482631 0.4625960 2.09936555 0
## 26 5.563 -73.296 0.7433650 0.4084605 11.55034137 1
## 27 5.580 -73.287 0.7469922 0.4313955 7.22300236 1
## 28 5.546 -73.273 0.7691206 0.4135666 3.24344481 1
## 29 5.568 -73.261 0.7566106 0.4054611 3.46134301 1
## 30 5.565 -73.282 0.7423937 0.4441127 5.64214584 0
## 31 5.545 -73.264 0.7467557 0.4596761 10.99215288 0
## 32 5.560 -73.290 0.8835611 0.4185906 9.89531118 0
## 33 5.548 -73.263 0.9363114 0.4229683 6.05606421 1
## 34 5.571 -73.290 0.8327370 0.4684080 7.13610701 0
## 35 5.575 -73.260 0.9487781 0.4393326 3.86150726 0
## 36 5.551 -73.267 0.9202944 0.4184583 3.43661932 1
## 37 5.577 -73.289 0.8957445 0.4419551 8.35327481 1
## 38 5.545 -73.298 0.9428688 0.4239987 9.01921544 1
## 39 5.573 -73.264 0.9426985 0.4525001 8.09327091 0
## 40 5.551 -73.254 0.8360254 0.4291286 10.94965588 1
## 41 5.569 -73.265 0.8474294 0.4645766 7.83494604 0
## 42 5.549 -73.265 0.8593841 0.4554685 2.91986904 0
## 43 5.580 -73.267 0.9024785 0.4518823 9.09754992 0
## 44 5.573 -73.262 0.7935286 0.4335222 2.70033511 0
## 45 5.563 -73.253 0.9071142 0.3984627 8.42126515 1
## 46 5.546 -73.258 0.8058787 0.4644484 9.89877747 1
## 47 5.574 -73.254 0.7936852 0.4622095 6.98432405 1
## 48 5.554 -73.299 0.8109977 0.4062288 2.77108840 0
## 49 5.540 -73.299 0.8000597 0.4367658 4.89101267 1
## 50 5.570 -73.279 0.8538442 0.4613475 13.63556628 1
## 51 5.554 -73.251 0.9035943 0.4398300 6.93182457 1
## 52 5.552 -73.276 0.8813359 0.4234928 5.41928287 1
## 53 5.547 -73.280 0.8683766 0.4638765 7.73024695 0
## 54 5.545 -73.252 0.8195909 0.4126734 2.66443029 0
## 55 5.578 -73.260 0.9096037 0.4342406 3.14084693 0
## 56 5.540 -73.270 0.8005186 0.4018448 14.04958141 0
## 57 5.575 -73.289 0.8843469 0.4176299 7.16174132 0
## 58 5.574 -73.270 0.7862926 0.4436692 15.80485938 1
## 59 5.544 -73.294 0.8814684 0.4504637 21.75577526 1
## 60 5.546 -73.290 0.7931877 0.4634360 17.91751894 0
## 61 5.576 -73.268 0.8419918 0.4709403 17.21845353 0
## 62 5.568 -73.256 0.8096942 0.4564409 19.65261254 0
## 63 5.558 -73.285 0.8770435 0.4690268 17.86453803 1
## 64 5.557 -73.278 0.9052334 0.4670000 21.71478240 0
## 65 5.573 -73.260 0.8082419 0.4478923 17.95328560 1
## 66 5.553 -73.287 0.8737961 0.4231107 20.00049780 0
## 67 5.566 -73.284 0.9399988 0.5413283 21.91175245 1
## 68 5.567 -73.266 0.8579748 0.4845414 16.45308889 0
## 69 5.580 -73.287 0.8456122 0.5123793 19.55470052 1
## 70 5.576 -73.294 0.8996512 0.5298505 17.58907590 0
## 71 5.555 -73.259 0.9458732 0.5327747 20.80298240 0
## 72 5.541 -73.258 0.8792382 0.5414217 17.07974429 1
## 73 5.574 -73.297 0.8855610 0.4749562 21.11227030 0
## 74 5.545 -73.299 0.9108187 0.5150462 17.06846987 0
## 75 5.560 -73.267 0.8426215 0.5782634 16.79374111 0
## 76 5.556 -73.269 0.7853216 0.4812471 20.68221081 1
## 77 5.555 -73.291 0.9027559 0.4751730 19.79326771 0
## 78 5.559 -73.252 0.8687392 0.5435544 21.94185458 1
## 79 5.561 -73.259 0.9428625 0.5136234 21.75379109 0
## 80 5.562 -73.254 0.8472902 0.4726600 20.08445494 0
## 81 5.552 -73.292 0.8623980 0.5928488 15.72687230 0
## 82 5.557 -73.268 0.7898995 0.5103821 20.55782129 0
## 83 5.574 -73.281 0.9186096 0.4860201 21.25512167 0
## 84 5.559 -73.286 0.9359754 0.5458797 17.89271229 1
## 85 5.575 -73.278 0.8354318 0.5101896 16.68875759 1
## 86 5.558 -73.285 0.8324613 0.5651807 24.00791902 1
## 87 5.542 -73.270 0.8787350 0.4863785 25.25284123 0
## 88 5.577 -73.272 0.8490814 0.4798723 23.76197107 1
## 89 5.556 -73.270 0.9077144 0.5505521 23.74986918 1
## 90 5.568 -73.299 0.8852120 0.5271529 22.24167996 0
## 91 5.548 -73.266 0.8119120 0.5325476 24.68434904 0
## 92 5.543 -73.253 0.9451603 0.5561369 23.63501988 1
## 93 5.555 -73.267 0.9407233 0.5561518 23.78582354 1
## 94 5.572 -73.282 0.8153107 0.4864211 23.95958793 0
## 95 5.565 -73.290 0.8871847 0.4960460 23.36287041 0
## 96 5.579 -73.273 0.8283922 0.5318362 23.60495650 1
## 97 5.545 -73.267 0.8502388 0.5014892 22.05131932 0
## 98 5.561 -73.290 0.8216787 0.4713482 25.67151845 1
## 99 5.540 -73.256 0.7929413 0.5011147 25.59871889 1
## 100 5.560 -73.253 0.8251805 0.4937176 25.32399297 1marks(Y3)=seq(1,100)
coords(Y3)=matrix(seq(6.54,6.58,0.001),seq(-72.3,-72.25,0.001),ncol = 2,nrow = 100) ## Warning in matrix(seq(6.54, 6.58, 0.001), seq(-72.3, -72.25, 0.001), ncol =
## 2, : la longitud de los datos [41] no es un submĆŗltiplo o mĆŗltiplo del nĆŗmero de
## filas [100] en la matriz## Warning: 100 points were rejected as lying outside the specified windowY3[windo1]## Marked planar point pattern: 0 points
## marks are numeric, of storage type 'integer'
## window: rectangle = [4.456, 4.528] x [-73.289, -73.277] units#8
dataxy3=ppp(xy$x,xy$y,xrange=c(min(xy$x),max(xy$x)),yrange=c(min(xy$y),max(xy$y)),marks = df1[,3:6])
Y3=unique(dataxy3)#9
plot(density(Y))
contour(density(Y))
#10
hist(xy$x,xlab = "x",ylab="Frecuencia",main = "Histograma frecuencia de X",
col = "yellow") # Histograma de la longitud.
hist(xy$y,xlab = "y",ylab="Frecuencia",main = "Histograma frecuencia de Y",
col = "pink") #Histograma de la latitud.
#11
plot(density(Y))
plot(Y,add=TRUE)
#12
sep=split(df1,crop,drop = TRUE)
dividir=split(df1,crop,drop = TRUE)
ausen=dividir$"1";ausen## x y SMI NDVI LST crop
## 1 5.577 -73.270 0.7110707 0.3378694 0.4258999 1
## 2 5.575 -73.298 0.7045101 0.3466406 0.4128654 1
## 11 5.549 -73.264 0.7190369 0.3814067 1.3449942 1
## 12 5.580 -73.273 0.7338021 0.3972094 1.3816868 1
## 13 5.572 -73.291 0.7347987 0.3871763 6.3502017 1
## 15 5.559 -73.282 0.7320041 0.3942129 6.2185995 1
## 17 5.555 -73.250 0.7386254 0.3920445 14.1815831 1
## 19 5.545 -73.296 0.7416434 0.3946744 10.2198233 1
## 20 5.577 -73.253 0.7380110 0.3964335 11.2081387 1
## 24 5.550 -73.294 0.7770173 0.4488691 7.6977361 1
## 26 5.563 -73.296 0.7433650 0.4084605 11.5503414 1
## 27 5.580 -73.287 0.7469922 0.4313955 7.2230024 1
## 28 5.546 -73.273 0.7691206 0.4135666 3.2434448 1
## 29 5.568 -73.261 0.7566106 0.4054611 3.4613430 1
## 33 5.548 -73.263 0.9363114 0.4229683 6.0560642 1
## 36 5.551 -73.267 0.9202944 0.4184583 3.4366193 1
## 37 5.577 -73.289 0.8957445 0.4419551 8.3532748 1
## 38 5.545 -73.298 0.9428688 0.4239987 9.0192154 1
## 40 5.551 -73.254 0.8360254 0.4291286 10.9496559 1
## 45 5.563 -73.253 0.9071142 0.3984627 8.4212651 1
## 46 5.546 -73.258 0.8058787 0.4644484 9.8987775 1
## 47 5.574 -73.254 0.7936852 0.4622095 6.9843240 1
## 49 5.540 -73.299 0.8000597 0.4367658 4.8910127 1
## 50 5.570 -73.279 0.8538442 0.4613475 13.6355663 1
## 51 5.554 -73.251 0.9035943 0.4398300 6.9318246 1
## 52 5.552 -73.276 0.8813359 0.4234928 5.4192829 1
## 58 5.574 -73.270 0.7862926 0.4436692 15.8048594 1
## 59 5.544 -73.294 0.8814684 0.4504637 21.7557753 1
## 63 5.558 -73.285 0.8770435 0.4690268 17.8645380 1
## 65 5.573 -73.260 0.8082419 0.4478923 17.9532856 1
## 67 5.566 -73.284 0.9399988 0.5413283 21.9117525 1
## 69 5.580 -73.287 0.8456122 0.5123793 19.5547005 1
## 72 5.541 -73.258 0.8792382 0.5414217 17.0797443 1
## 76 5.556 -73.269 0.7853216 0.4812471 20.6822108 1
## 78 5.559 -73.252 0.8687392 0.5435544 21.9418546 1
## 84 5.559 -73.286 0.9359754 0.5458797 17.8927123 1
## 85 5.575 -73.278 0.8354318 0.5101896 16.6887576 1
## 86 5.558 -73.285 0.8324613 0.5651807 24.0079190 1
## 88 5.577 -73.272 0.8490814 0.4798723 23.7619711 1
## 89 5.556 -73.270 0.9077144 0.5505521 23.7498692 1
## 92 5.543 -73.253 0.9451603 0.5561369 23.6350199 1
## 93 5.555 -73.267 0.9407233 0.5561518 23.7858235 1
## 96 5.579 -73.273 0.8283922 0.5318362 23.6049565 1
## 98 5.561 -73.290 0.8216787 0.4713482 25.6715184 1
## 99 5.540 -73.256 0.7929413 0.5011147 25.5987189 1
## 100 5.560 -73.253 0.8251805 0.4937176 25.3239930 1Y4=unique(ppp(ausen$x,ausen$y,xrange=c(min(ausen$x),max(ausen$x)),yrange=c(min(ausen$y),
max(ausen$y)),marks = ausen[,6]))## Warning: data contain duplicated pointsany(duplicated(Y4)) ## [1] FALSEplot(Y4,cols = "blue",main = "Ausencia de cultivos")
#13
plot(density(Y4))
contour(density(Y4))
#14
x=seq(-4,4,length=110)
y=seq(-4,4,length=110)
cono=function(x,y) sqrt(x^2+y^2)
z=outer(x, y, cono)
persp(x,y,z) 
parab=function(x,y) x^2+y^2
z=outer(x, y, parab)
persp(x,y,z)
#15
ml=as.im(Y4)
plot(ml)
plot(Y4,add = TRUE)
#16
summary(Y3)## Marked planar point pattern: 100 points
## Average intensity 50000 points per square unit
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Mark variables: SMI, NDVI, LST, crop
## Summary:
## SMI NDVI LST crop
## Min. :0.7020 Min. :0.3379 Min. : 0.09257 0:54
## 1st Qu.:0.7545 1st Qu.:0.4079 1st Qu.: 5.58643 1:46
## Median :0.8304 Median :0.4522 Median :11.12733
## Mean :0.8249 Mean :0.4538 Mean :12.47099
## 3rd Qu.:0.8846 3rd Qu.:0.4864 3rd Qu.:20.20280
## Max. :0.9488 Max. :0.5928 Max. :25.67152
##
## Window: rectangle = [5.54, 5.58] x [-73.3, -73.25] units
## (0.04 x 0.05 units)
## Window area = 0.002 square unitsset.seed(1000579855)
seqlat=seq(from=0,to=2000,by=2) #cm
seqlong=seq(from=0,to=500,by=2)#cm
lat=sample(seqlat,size = 150,replace = TRUE)
lon=sample(seqlong,size=150,replace=TRUE)
xyn=data.frame(x=lon,y=lat)
SMI=sort.int(runif(150,0.7,0.95),partial=10)
NDVI=sort.int(rnorm(150,0.45,0.06),partial=10)
LST=sort.int(26*rbeta(150,shape1=0.87,shape2=0.91),partial=10)
crop=factor(ifelse(rgamma(n=150,rate=0.8,shape=0.5)<0.5,0,1))
df2=data.frame(xyn,SMI,NDVI,LST,crop)
library(spatstat)
dataxyn=ppp(xyn$x,xyn$y,xrange=c(min(xyn$x),max(xyn$x)),yrange=(c(min(xyn$y),max(xyn$y))))
any(duplicated(dataxyn))## [1] FALSEyn=unique(dataxyn)
plot(yn,size=0.5)
summary(yn)## Planar point pattern: 150 points
## Average intensity 0.0001510574 points per square unit
##
## Coordinates are integers
## i.e. rounded to the nearest unit
##
## Window: rectangle = [0, 500] x [4, 1990] units
## (500 x 1986 units)
## Window area = 993000 square units#17
dens=summary(yn)$intensity*(1000)^2;dens ## [1] 151.0574lambda=summary(yn)$intensity*(1000)^2;lambda ## [1] 151.0574#18
quadratcount(yn, nx = 4, ny = 4) ## x
## y [0,125) [125,250) [250,375) [375,500]
## [1.49e+03,1.99e+03] 11 8 7 11
## [997,1.49e+03) 12 15 7 6
## [500,997) 8 9 5 10
## [4,500) 9 19 5 8Q <- quadratcount(yn, nx = 4, ny = 4)
plot(yn,size=0.7)
plot(Q, add = TRUE)
#19
dens =density(yn)
plot(dens)
plot(yn,add=TRUE)
persp(dens) 
#20
contour(dens)
#21
#Grafico covariable Ćndice de vegetaciĆ³n de diferencia normalizado.
par(mfrow = c(1, 2))
plot(yn)
plot(NDVI)
par(mfrow = c(1, 1))
#Grafico de la temperatura en el suelo
par(mfrow = c(1, 2))
plot(yn)
plot(LST)
par(mfrow = c(1, 1))
#Existencia de vegetaciĆ³n.
par(mfrow = c(1, 2))
plot(yn)
plot(crop)
par(mfrow = c(1, 1))
#humedad del suelo.
par(mfrow = c(1, 2))
plot(yn)
plot(SMI)
par(mfrow = c(1, 1))#22\(H_0:\) Es aleatorea la distribucion de los cuadrantes (Distribucion poisson) \(H_0:\) Los cuadrantes no son aleatorios
H = quadrat.test(yn, nx = 4, ny = 4); H##
## Chi-squared test of CSR using quadrat counts
##
## data: yn
## X2 = 21.733, df = 15, p-value = 0.23
## alternative hypothesis: two.sided
##
## Quadrats: 4 by 4 grid of tilesplot(yn,size=0.01)
plot(H, add = TRUE, cex = 0.9)
\(No \thinspace existen \thinspace pruebas \thinspace suficientes \thinspace para \thinspace rechazar \thinspace la \thinspace hipotesis \thinspace nula\)
#23#24
A <- quantile(NDVI, probs = (0:4)/4)
NDVI_cortado <- cut(NDVI, breaks = A, labels = 1:4)
#Z <- tess(image= NDVI_cortado) Genera un error que dice, "Error in as.im.default(image) : Can't convert X to a pixel image"
#quadrat.test(yn, tess = Z)#25
PoisModel= ppm(yn, ~1)
PoisModel## Stationary Poisson process
## Intensity: 0.0001510574
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## log(lambda) -8.797851 0.08164966 -8.957881 -8.63782 *** -107.7512#26
Model_Lat=ppm(yn~y)
Model_Lat## Nonstationary Poisson process
##
## Log intensity: ~y
##
## Fitted trend coefficients:
## (Intercept) y
## -8.785105e+00 -1.281177e-05
##
## Estimate S.E. CI95.lo CI95.hi Ztest
## (Intercept) -8.785105e+00 0.1632721269 -9.105112145 -8.4650971682 ***
## y -1.281177e-05 0.0001424211 -0.000291952 0.0002663284
## Zval
## (Intercept) -53.80651813
## y -0.08995701#27
Model_Long=ppm(yn~x)
Model_Long## Nonstationary Poisson process
##
## Log intensity: ~x
##
## Fitted trend coefficients:
## (Intercept) x
## -8.6439005707 -0.0006322991
##
## Estimate S.E. CI95.lo CI95.hi Ztest
## (Intercept) -8.6439005707 0.1572144119 -8.95203516 -8.3357659855 ***
## x -0.0006322991 0.0005670975 -0.00174379 0.0004791917
## Zval
## (Intercept) -54.981604
## x -1.114974#28
Modelo_sumaxy =ppm(yn, ~x + y)
Modelo_sumaxy## Nonstationary Poisson process
##
## Log intensity: ~x + y
##
## Fitted trend coefficients:
## (Intercept) x y
## -8.631185e+00 -6.322901e-04 -1.278328e-05
##
## Estimate S.E. CI95.lo CI95.hi Ztest
## (Intercept) -8.631185e+00 0.2114250551 -9.045570614 -8.2167996271 ***
## x -6.322901e-04 0.0005670977 -0.001743781 0.0004792009
## y -1.278328e-05 0.0001424203 -0.000291922 0.0002663555
## Zval
## (Intercept) -40.82385182
## x -1.11495797
## y -0.08975738\(\lambda_{\theta}((x, y))=\exp \left(\theta_{0}+\theta_{1} x+\theta_{2} y\right)\) Formula pag. 97
#29\(\lambda_{\theta}((x, y))= \begin{cases}\exp (-5.1222) & \text { if } x<300 \\ \exp (-5.1222055+0.5283049) & \text { if } x \geq 300\end{cases}\)
Modelo_polino= ppm(yn, ~polynom(x, y, 2))
Modelo_polino## Nonstationary Poisson process
##
## Log intensity: ~x + y + I(x^2) + I(x * y) + I(y^2)
##
## Fitted trend coefficients:
## (Intercept) x y I(x^2) I(x * y)
## -8.537772e+00 -1.819548e-04 -4.011439e-04 -9.742665e-07 2.677938e-08
## I(y^2)
## 1.919375e-07
##
## Estimate S.E. CI95.lo CI95.hi Ztest
## (Intercept) -8.537772e+00 3.962618e-01 -9.314431e+00 -7.761113e+00 ***
## x -1.819548e-04 2.435064e-03 -4.954593e-03 4.590683e-03
## y -4.011439e-04 6.061152e-04 -1.589108e-03 7.868201e-04
## I(x^2) -9.742665e-07 4.419827e-06 -9.636968e-06 7.688435e-06
## I(x * y) 2.677938e-08 9.720067e-07 -1.878319e-06 1.931877e-06
## I(y^2) 1.919375e-07 2.732659e-07 -3.436539e-07 7.275289e-07
## Zval
## (Intercept) -21.54578552
## x -0.07472278
## y -0.66182784
## I(x^2) -0.22043090
## I(x * y) 0.02755061
## I(y^2) 0.70238363side = function(s) factor(ifelse(s < 300, "izquierda","derecha"))
ppm(bei, ~side(x))## Nonstationary Poisson process
##
## Log intensity: ~side(x)
##
## Fitted trend coefficients:
## (Intercept) side(x)izquierda
## -5.1222055 0.5283049
##
## Estimate S.E. CI95.lo CI95.hi Ztest Zval
## (Intercept) -5.1222055 0.02188965 -5.1651084 -5.0793026 *** -234.00128
## side(x)izquierda 0.5283049 0.03373948 0.4621768 0.5944331 *** 15.65836#30
#desviaciĆ³n de y, x, x+y
anova(Model_Lat) ## Analysis of Deviance Table
## Terms added sequentially (first to last)
##
## Df Deviance Npar
## NULL 1
## y 1 0.0080924 2anova(Model_Long)## Analysis of Deviance Table
## Terms added sequentially (first to last)
##
## Df Deviance Npar
## NULL 1
## x 1 1.2463 2anova(Modelo_sumaxy) ## Analysis of Deviance Table
## Terms added sequentially (first to last)
##
## Df Deviance Npar
## NULL 1
## x 1 1.24626 2
## y 1 0.00806 3#31
summary(anova(Modelo_polino,Modelo_sumaxy,test="Chi"))## Npar Df Deviance Pr(>Chi)
## Min. :3.00 Min. :-3 Min. :-0.5372 Min. :0.9107
## 1st Qu.:3.75 1st Qu.:-3 1st Qu.:-0.5372 1st Qu.:0.9107
## Median :4.50 Median :-3 Median :-0.5372 Median :0.9107
## Mean :4.50 Mean :-3 Mean :-0.5372 Mean :0.9107
## 3rd Qu.:5.25 3rd Qu.:-3 3rd Qu.:-0.5372 3rd Qu.:0.9107
## Max. :6.00 Max. :-3 Max. :-0.5372 Max. :0.9107
## NA's :1 NA's :1 NA's :1