Geoestadìstica_Nov2_Monitoreo1
Lina Marcela Sandoval
30/11/2020
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summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00library(readxl)
Fresno_1_Da <- read_excel("C:/Users/linam/Downloads/Fresno_1_Da.xlsx",
sheet = "Sheet1")
Fresno_1_Da## # A tibble: 2,363 x 9
## id_arbol Latitude Longitude Date2 `Psychro Wet Bu~ `Station Pressu~
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2.38 -76.6 21/0~ 14.8 805.
## 2 2 2.38 -76.6 21/0~ 11.6 805.
## 3 3 2.38 -76.6 21/0~ 12.9 806.
## 4 4 2.38 -76.6 21/0~ 14.1 806.
## 5 5 2.38 -76.6 21/0~ 14.3 805.
## 6 6 2.38 -76.6 21/0~ 14.2 805.
## 7 7 2.38 -76.6 21/0~ 14.4 805.
## 8 8 2.38 -76.6 21/0~ 12.8 805.
## 9 9 2.38 -76.6 21/0~ 15 805
## 10 10 2.38 -76.6 21/0~ 14 805.
## # ... with 2,353 more rows, and 3 more variables: `Relative Humidity` <dbl>,
## # Crosswind <dbl>, Temperature <dbl>plot(Fresno_1_Da[,3:2])
require(ggplot2)## Loading required package: ggplot2
ggplot(Fresno_1_Da,aes(x=Longitude, y=Longitude))+geom_point()+theme_bw()
ggplot(Fresno_1_Da,aes(x=Longitude, y=Longitude))+geom_point()+theme_bw()+facet_wrap(~Date2)
ggplot(Fresno_1_Da,aes(x=Longitude, y=Latitude, color=Temperature))+geom_point()+theme_bw()+facet_wrap(~Date2)+scale_color_continuous(type="viridis")
###Filtramos el instante 1 del tiempo
fechas=unique(Fresno_1_Da$Date2)
pos=which(Fresno_1_Da$Date2==fechas[4])
datos_m1=Fresno_1_Da[pos,]
datos_m1## # A tibble: 394 x 9
## id_arbol Latitude Longitude Date2 `Psychro Wet Bu~ `Station Pressu~
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2.38 -76.6 1/10~ 17.4 799.
## 2 2 2.38 -76.6 1/10~ 18.4 800.
## 3 3 2.38 -76.6 1/10~ 17.4 800
## 4 4 2.38 -76.6 1/10~ 16.9 800.
## 5 5 2.38 -76.6 1/10~ 17.3 800.
## 6 6 2.38 -76.6 1/10~ 18.4 799.
## 7 7 2.38 -76.6 1/10~ 16.6 799.
## 8 8 2.38 -76.6 1/10~ 17.1 799.
## 9 9 2.38 -76.6 1/10~ 17 800.
## 10 10 2.38 -76.6 1/10~ 16.4 800.
## # ... with 384 more rows, and 3 more variables: `Relative Humidity` <dbl>,
## # Crosswind <dbl>, Temperature <dbl>require(leaflet)## Loading required package: leafletleaflet() %>% addCircleMarkers(lng=datos_m1$Longitude,lat=datos_m1$Latitude, radius=0.05, color= "Black", opacity=0.9, label = datos_m1$id_arbol)%>% addTiles()require(geoR)## Loading required package: geoR## --------------------------------------------------------------
## Analysis of Geostatistical Data
## For an Introduction to geoR go to http://www.leg.ufpr.br/geoR
## geoR version 1.8-1 (built on 2020-02-08) is now loaded
## --------------------------------------------------------------geo_datos=as.geodata(datos_m1,coords.col=3:2,data.col=9)
geo_datos## $coords
## Longitude Latitude
## [1,] -76.61264 2.381290
## [2,] -76.61259 2.381320
## [3,] -76.61254 2.381353
## [4,] -76.61261 2.381374
## [5,] -76.61266 2.381335
## [6,] -76.61271 2.381313
## [7,] -76.61282 2.381298
## [8,] -76.61276 2.381342
## [9,] -76.61270 2.381366
## [10,] -76.61263 2.381415
## [11,] -76.61268 2.381459
## [12,] -76.61271 2.381429
## [13,] -76.61275 2.381400
## [14,] -76.61283 2.381336
## [15,] -76.61286 2.381292
## [16,] -76.61293 2.381310
## [17,] -76.61288 2.381354
## [18,] -76.61284 2.381396
## [19,] -76.61280 2.381428
## [20,] -76.61273 2.381484
## [21,] -76.61280 2.381517
## [22,] -76.61282 2.381486
## [23,] -76.61286 2.381453
## [24,] -76.61290 2.381405
## [25,] -76.61292 2.381355
## [26,] -76.61296 2.381295
## [27,] -76.61301 2.381266
## [28,] -76.61310 2.381211
## [29,] -76.61306 2.381251
## [30,] -76.61303 2.381306
## [31,] -76.61299 2.381364
## [32,] -76.61294 2.381411
## [33,] -76.61291 2.381464
## [34,] -76.61288 2.381514
## [35,] -76.61284 2.381542
## [36,] -76.61282 2.381568
## [37,] -76.61284 2.381621
## [38,] -76.61287 2.381598
## [39,] -76.61291 2.381561
## [40,] -76.61294 2.381508
## [41,] -76.61298 2.381460
## [42,] -76.61301 2.381415
## [43,] -76.61304 2.381366
## [44,] -76.61308 2.381309
## [45,] -76.61311 2.381264
## [46,] -76.61315 2.381207
## [47,] -76.61319 2.381144
## [48,] -76.61341 2.380892
## [49,] -76.61337 2.380951
## [50,] -76.61333 2.380991
## [51,] -76.61330 2.381045
## [52,] -76.61325 2.381096
## [53,] -76.61319 2.381201
## [54,] -76.61316 2.381255
## [55,] -76.61313 2.381313
## [56,] -76.61309 2.381361
## [57,] -76.61305 2.381414
## [58,] -76.61302 2.381482
## [59,] -76.61299 2.381522
## [60,] -76.61296 2.381580
## [61,] -76.61292 2.381630
## [62,] -76.61289 2.381655
## [63,] -76.61286 2.381673
## [64,] -76.61292 2.381715
## [65,] -76.61294 2.381688
## [66,] -76.61298 2.381630
## [67,] -76.61301 2.381581
## [68,] -76.61304 2.381528
## [69,] -76.61307 2.381467
## [70,] -76.61311 2.381419
## [71,] -76.61319 2.381319
## [72,] -76.61322 2.381265
## [73,] -76.61325 2.381210
## [74,] -76.61328 2.381153
## [75,] -76.61333 2.381113
## [76,] -76.61336 2.381052
## [77,] -76.61339 2.380998
## [78,] -76.61343 2.380950
## [79,] -76.61346 2.380900
## [80,] -76.61350 2.380856
## [81,] -76.61353 2.380799
## [82,] -76.61357 2.380830
## [83,] -76.61354 2.380869
## [84,] -76.61351 2.380917
## [85,] -76.61344 2.381013
## [86,] -76.61340 2.381068
## [87,] -76.61336 2.381111
## [88,] -76.61333 2.381169
## [89,] -76.61330 2.381216
## [90,] -76.61326 2.381268
## [91,] -76.61323 2.381324
## [92,] -76.61319 2.381377
## [93,] -76.61316 2.381428
## [94,] -76.61312 2.381487
## [95,] -76.61306 2.381590
## [96,] -76.61303 2.381647
## [97,] -76.61299 2.381697
## [98,] -76.61296 2.381750
## [99,] -76.61295 2.381833
## [100,] -76.61299 2.381804
## [101,] -76.61302 2.381751
## [102,] -76.61306 2.381697
## [103,] -76.61309 2.381633
## [104,] -76.61312 2.381590
## [105,] -76.61315 2.381542
## [106,] -76.61319 2.381486
## [107,] -76.61322 2.381426
## [108,] -76.61325 2.381364
## [109,] -76.61328 2.381322
## [110,] -76.61331 2.381276
## [111,] -76.61334 2.381214
## [112,] -76.61337 2.381156
## [113,] -76.61341 2.381103
## [114,] -76.61346 2.381059
## [115,] -76.61350 2.381014
## [116,] -76.61355 2.380968
## [117,] -76.61357 2.380924
## [118,] -76.61362 2.380876
## [119,] -76.61365 2.380837
## [120,] -76.61366 2.380887
## [121,] -76.61362 2.380931
## [122,] -76.61341 2.381209
## [123,] -76.61337 2.381247
## [124,] -76.61333 2.381320
## [125,] -76.61329 2.381371
## [126,] -76.61327 2.381426
## [127,] -76.61323 2.381493
## [128,] -76.61321 2.381536
## [129,] -76.61317 2.381583
## [130,] -76.61314 2.381636
## [131,] -76.61307 2.381751
## [132,] -76.61305 2.381799
## [133,] -76.61301 2.381855
## [134,] -76.61304 2.381912
## [135,] -76.61307 2.381864
## [136,] -76.61311 2.381810
## [137,] -76.61313 2.381759
## [138,] -76.61316 2.381701
## [139,] -76.61321 2.381644
## [140,] -76.61324 2.381594
## [141,] -76.61326 2.381538
## [142,] -76.61330 2.381485
## [143,] -76.61333 2.381434
## [144,] -76.61336 2.381383
## [145,] -76.61340 2.381325
## [146,] -76.61338 2.381448
## [147,] -76.61335 2.381497
## [148,] -76.61331 2.381545
## [149,] -76.61329 2.381598
## [150,] -76.61322 2.381711
## [151,] -76.61319 2.381769
## [152,] -76.61316 2.381816
## [153,] -76.61312 2.381866
## [154,] -76.61308 2.381913
## [155,] -76.61306 2.381973
## [156,] -76.61308 2.382037
## [157,] -76.61311 2.381975
## [158,] -76.61315 2.381930
## [159,] -76.61317 2.381872
## [160,] -76.61321 2.381814
## [161,] -76.61328 2.381707
## [162,] -76.61334 2.381606
## [163,] -76.61337 2.381541
## [164,] -76.61341 2.381488
## [165,] -76.61338 2.381606
## [166,] -76.61335 2.381665
## [167,] -76.61332 2.381724
## [168,] -76.61329 2.381770
## [169,] -76.61326 2.381828
## [170,] -76.61324 2.381882
## [171,] -76.61320 2.381932
## [172,] -76.61316 2.381988
## [173,] -76.61312 2.382038
## [174,] -76.61310 2.382097
## [175,] -76.61309 2.382195
## [176,] -76.61312 2.382148
## [177,] -76.61318 2.382037
## [178,] -76.61322 2.381989
## [179,] -76.61324 2.381935
## [180,] -76.61328 2.381876
## [181,] -76.61333 2.381821
## [182,] -76.61335 2.381782
## [183,] -76.61338 2.381724
## [184,] -76.61342 2.381663
## [185,] -76.61347 2.381676
## [186,] -76.61343 2.381738
## [187,] -76.61339 2.381781
## [188,] -76.61336 2.381840
## [189,] -76.61334 2.381896
## [190,] -76.61330 2.381942
## [191,] -76.61327 2.381983
## [192,] -76.61323 2.382044
## [193,] -76.61318 2.382151
## [194,] -76.61315 2.382200
## [195,] -76.61311 2.382245
## [196,] -76.61307 2.382297
## [197,] -76.61306 2.382402
## [198,] -76.61311 2.382333
## [199,] -76.61314 2.382300
## [200,] -76.61317 2.382255
## [201,] -76.61320 2.382198
## [202,] -76.61323 2.382146
## [203,] -76.61327 2.382098
## [204,] -76.61329 2.382049
## [205,] -76.61334 2.381987
## [206,] -76.61336 2.381932
## [207,] -76.61339 2.381879
## [208,] -76.61343 2.381822
## [209,] -76.61346 2.381784
## [210,] -76.61350 2.381727
## [211,] -76.61352 2.381671
## [212,] -76.61354 2.381738
## [213,] -76.61351 2.381798
## [214,] -76.61347 2.381851
## [215,] -76.61344 2.381892
## [216,] -76.61342 2.381937
## [217,] -76.61338 2.381996
## [218,] -76.61335 2.382055
## [219,] -76.61332 2.382108
## [220,] -76.61328 2.382167
## [221,] -76.61325 2.382213
## [222,] -76.61322 2.382263
## [223,] -76.61319 2.382322
## [224,] -76.61316 2.382362
## [225,] -76.61312 2.382405
## [226,] -76.61307 2.382451
## [227,] -76.61310 2.382497
## [228,] -76.61315 2.382458
## [229,] -76.61318 2.382406
## [230,] -76.61322 2.382367
## [231,] -76.61323 2.382319
## [232,] -76.61326 2.382266
## [233,] -76.61330 2.382216
## [234,] -76.61334 2.382155
## [235,] -76.61337 2.382111
## [236,] -76.61340 2.382053
## [237,] -76.61343 2.382002
## [238,] -76.61347 2.381950
## [239,] -76.61351 2.381889
## [240,] -76.61354 2.381839
## [241,] -76.61358 2.381777
## [242,] -76.61362 2.381766
## [243,] -76.61364 2.381813
## [244,] -76.61359 2.381858
## [245,] -76.61356 2.381904
## [246,] -76.61352 2.381949
## [247,] -76.61349 2.381994
## [248,] -76.61346 2.382058
## [249,] -76.61342 2.382103
## [250,] -76.61339 2.382166
## [251,] -76.61336 2.382219
## [252,] -76.61332 2.382270
## [253,] -76.61329 2.382326
## [254,] -76.61326 2.382380
## [255,] -76.61324 2.382442
## [256,] -76.61320 2.382480
## [257,] -76.61316 2.382527
## [258,] -76.61322 2.382524
## [259,] -76.61326 2.382482
## [260,] -76.61328 2.382422
## [261,] -76.61331 2.382363
## [262,] -76.61335 2.382325
## [263,] -76.61338 2.382267
## [264,] -76.61341 2.382226
## [265,] -76.61344 2.382160
## [266,] -76.61348 2.382106
## [267,] -76.61352 2.382052
## [268,] -76.61355 2.382005
## [269,] -76.61358 2.381961
## [270,] -76.61362 2.381913
## [271,] -76.61366 2.381857
## [272,] -76.61369 2.381802
## [273,] -76.61372 2.381852
## [274,] -76.61368 2.381917
## [275,] -76.61364 2.381964
## [276,] -76.61361 2.382025
## [277,] -76.61357 2.382074
## [278,] -76.61353 2.382113
## [279,] -76.61350 2.382169
## [280,] -76.61347 2.382227
## [281,] -76.61343 2.382275
## [282,] -76.61340 2.382334
## [283,] -76.61336 2.382377
## [284,] -76.61333 2.382442
## [285,] -76.61331 2.382489
## [286,] -76.61327 2.382551
## [287,] -76.61335 2.382532
## [288,] -76.61338 2.382480
## [289,] -76.61341 2.382436
## [290,] -76.61344 2.382384
## [291,] -76.61347 2.382326
## [292,] -76.61351 2.382271
## [293,] -76.61355 2.382211
## [294,] -76.61357 2.382162
## [295,] -76.61361 2.382118
## [296,] -76.61365 2.382067
## [297,] -76.61367 2.382013
## [298,] -76.61372 2.381961
## [299,] -76.61375 2.381922
## [300,] -76.61379 2.381871
## [301,] -76.61378 2.381960
## [302,] -76.61374 2.382011
## [303,] -76.61370 2.382054
## [304,] -76.61367 2.382114
## [305,] -76.61360 2.382220
## [306,] -76.61356 2.382270
## [307,] -76.61350 2.382361
## [308,] -76.61347 2.382426
## [309,] -76.61344 2.382471
## [310,] -76.61339 2.382535
## [311,] -76.61346 2.382526
## [312,] -76.61350 2.382476
## [313,] -76.61353 2.382430
## [314,] -76.61356 2.382378
## [315,] -76.61366 2.382214
## [316,] -76.61370 2.382173
## [317,] -76.61373 2.382116
## [318,] -76.61377 2.382066
## [319,] -76.61381 2.382011
## [320,] -76.61385 2.381955
## [321,] -76.61389 2.381983
## [322,] -76.61386 2.382025
## [323,] -76.61383 2.382080
## [324,] -76.61379 2.382119
## [325,] -76.61376 2.382171
## [326,] -76.61371 2.382228
## [327,] -76.61369 2.382286
## [328,] -76.61365 2.382331
## [329,] -76.61359 2.382436
## [330,] -76.61355 2.382486
## [331,] -76.61352 2.382525
## [332,] -76.61354 2.382587
## [333,] -76.61358 2.382533
## [334,] -76.61361 2.382485
## [335,] -76.61365 2.382441
## [336,] -76.61375 2.382274
## [337,] -76.61378 2.382226
## [338,] -76.61382 2.382176
## [339,] -76.61386 2.382119
## [340,] -76.61389 2.382062
## [341,] -76.61400 2.382030
## [342,] -76.61398 2.382076
## [343,] -76.61393 2.382136
## [344,] -76.61390 2.382175
## [345,] -76.61386 2.382224
## [346,] -76.61382 2.382271
## [347,] -76.61377 2.382325
## [348,] -76.61374 2.382368
## [349,] -76.61371 2.382424
## [350,] -76.61367 2.382486
## [351,] -76.61364 2.382538
## [352,] -76.61360 2.382581
## [353,] -76.61363 2.382629
## [354,] -76.61368 2.382582
## [355,] -76.61371 2.382540
## [356,] -76.61373 2.382481
## [357,] -76.61376 2.382433
## [358,] -76.61382 2.382375
## [359,] -76.61384 2.382331
## [360,] -76.61388 2.382293
## [361,] -76.61391 2.382226
## [362,] -76.61397 2.382176
## [363,] -76.61397 2.382129
## [364,] -76.61405 2.382061
## [365,] -76.61408 2.382066
## [366,] -76.61405 2.382136
## [367,] -76.61402 2.382188
## [368,] -76.61398 2.382236
## [369,] -76.61395 2.382279
## [370,] -76.61391 2.382331
## [371,] -76.61387 2.382377
## [372,] -76.61382 2.382436
## [373,] -76.61379 2.382487
## [374,] -76.61376 2.382535
## [375,] -76.61374 2.382582
## [376,] -76.61370 2.382639
## [377,] -76.61373 2.382694
## [378,] -76.61375 2.382641
## [379,] -76.61379 2.382588
## [380,] -76.61383 2.382536
## [381,] -76.61385 2.382484
## [382,] -76.61389 2.382430
## [383,] -76.61395 2.382374
## [384,] -76.61396 2.382333
## [385,] -76.61401 2.382278
## [386,] -76.61406 2.382220
## [387,] -76.61408 2.382183
## [388,] -76.61412 2.382121
## [389,] -76.61415 2.382056
## [390,] -76.61391 2.382486
## [391,] -76.61388 2.382543
## [392,] -76.61385 2.382596
## [393,] -76.61382 2.382643
## [394,] -76.61379 2.382693
##
## $data
## [1] 22.9 23.8 22.6 22.3 22.7 23.1 21.0 21.6 21.4 20.8 21.3 21.1 21.1 21.3 21.5
## [16] 21.6 21.4 21.2 21.4 21.3 21.1 21.4 21.4 21.3 21.2 21.1 21.0 21.1 21.0 21.2
## [31] 21.2 21.4 21.4 21.0 21.0 21.1 21.0 20.9 21.0 21.0 21.2 21.2 21.2 21.5 21.7
## [46] 21.3 21.1 21.1 21.0 21.0 21.1 21.2 21.2 21.4 21.5 21.8 21.9 21.6 21.6 21.5
## [61] 21.6 21.5 21.6 21.9 21.5 21.7 22.7 22.7 22.5 22.8 23.0 22.1 22.2 21.8 21.7
## [76] 21.2 21.9 21.7 21.4 21.5 21.8 21.9 21.7 21.6 21.6 21.4 21.4 21.5 21.5 22.0
## [91] 22.0 22.0 22.1 22.0 22.1 22.5 21.4 20.6 21.0 20.7 20.9 21.3 21.3 21.3 21.1
## [106] 21.4 21.2 21.1 21.3 21.2 21.2 21.2 21.2 21.3 21.3 21.1 21.6 21.6 21.8 24.1
## [121] 23.6 23.3 24.0 24.2 24.2 25.0 25.1 24.6 24.9 25.1 25.0 25.6 25.2 24.6 25.6
## [136] 26.8 26.5 25.7 26.5 26.8 26.2 26.5 25.9 26.7 27.2 27.2 26.7 26.5 26.6 25.6
## [151] 28.1 27.2 27.2 26.9 26.4 28.2 27.4 27.7 27.7 26.8 26.4 25.5 26.0 25.7 25.4
## [166] 25.3 25.0 25.2 25.5 24.8 25.4 25.6 26.4 25.8 25.5 23.4 23.5 23.0 23.1 23.0
## [181] 23.9 23.9 23.6 25.8 25.7 27.3 24.7 26.2 26.2 28.6 28.4 31.4 30.9 30.5 29.7
## [196] 30.5 27.6 29.2 25.6 26.7 25.9 25.2 26.1 26.2 25.8 26.8 25.5 25.3 27.1 29.3
## [211] 29.8 28.5 27.7 29.0 29.4 29.5 29.7 29.5 29.9 28.3 29.4 29.8 29.8 29.7 28.9
## [226] 29.3 28.1 27.1 25.7 25.7 28.2 27.5 27.7 28.1 27.9 27.4 26.2 27.1 27.3 25.8
## [241] 26.8 29.2 30.0 28.7 29.8 29.4 28.6 29.4 29.3 28.2 29.5 30.3 30.4 29.0 28.4
## [256] 28.7 28.2 28.4 28.9 27.4 29.0 28.9 28.2 27.3 28.6 28.2 27.7 26.9 28.1 27.8
## [271] 27.0 27.7 27.6 28.6 28.5 27.5 27.3 26.5 26.3 26.0 26.2 27.2 26.9 28.7 26.4
## [286] 25.1 25.4 25.3 26.1 26.8 27.0 27.3 27.1 26.1 28.4 27.2 27.5 26.6 27.4 28.3
## [301] 26.8 26.1 25.4 24.7 26.3 24.9 27.0 26.3 25.2 25.3 25.6 25.4 24.8 23.5 23.8
## [316] 24.0 24.2 25.2 24.6 23.2 23.4 22.0 22.3 22.5 22.3 22.7 23.5 23.1 24.5 24.8
## [331] 25.0 25.9 25.5 24.1 28.0 25.8 25.1 24.4 26.4 25.0 24.8 26.0 26.3 25.7 25.2
## [346] 27.0 27.1 29.4 26.7 29.6 30.0 22.6 24.0 23.8 22.4 21.4 21.7 22.3 22.9 23.3
## [361] 23.0 22.9 22.4 22.1 23.0 22.6 23.3 22.3 22.4 23.2 23.4 22.9 25.4 24.2 22.0
## [376] 21.5 22.0 22.7 22.7 21.4 21.9 22.0 21.1 21.8 23.4 22.1 22.0 21.8 21.6 23.5
## [391] 22.4 23.3 21.8 24.4
##
## attr(,"class")
## [1] "geodata"plot(geo_datos)
summary(dist(geo_datos[[1]]))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.302e-05 4.017e-04 6.544e-04 7.038e-04 9.703e-04 1.912e-03variograma=variog(geo_datos, option = "bin", uvec =seq(0,0.0009703,0.00003))## variog: computing omnidirectional variogramplot(variograma)
variograma.env=variog.mc.env(geo_datos,obj=variograma)## variog.env: generating 99 simulations by permutating data values
## variog.env: computing the empirical variogram for the 99 simulations
## variog.env: computing the envelopsplot (variograma, envelope=variograma.env)
###Ajuste del Modelo de Semivarianza"
ini.vals=expand.grid(seq(8,12,l=10), seq(0.0006,0.0008,l=10))
model_mco_gaus=variofit(variograma,ini=ini.vals,cov.model="gaussian",wei="npair", min="optim", nugget=0)## variofit: covariance model used is gaussian
## variofit: weights used: npairs
## variofit: minimisation function used: optim
## variofit: searching for best initial value ... selected values:
## sigmasq phi tausq kappa
## initial.value "12" "0" "0" "0.5"
## status "est" "est" "est" "fix"
## loss value: 128786.888699576model_mco_exp=variofit(variograma,ini=ini.vals,cov.model="exponential",wei="npair", min="optim", nugget=0) ## variofit: covariance model used is exponential
## variofit: weights used: npairs
## variofit: minimisation function used: optim
## variofit: searching for best initial value ... selected values:
## sigmasq phi tausq kappa
## initial.value "12" "0" "0" "0.5"
## status "est" "est" "est" "fix"
## loss value: 161534.404990903model_mco_sphe=variofit(variograma,ini=ini.vals,cov.model="spheric",wei="npair", min="optim", nugget=0) ## variofit: covariance model used is spherical
## variofit: weights used: npairs
## variofit: minimisation function used: optim
## variofit: searching for best initial value ... selected values:
## sigmasq phi tausq kappa
## initial.value "10.22" "0" "0" "0.5"
## status "est" "est" "est" "fix"
## loss value: 99030.0417375569lines(model_mco_gaus, col="red")
lines(model_mco_exp, col="blue")
lines( model_mco_sphe,col="purple")
##El gausiano presenta el mejor ajuste
model_mco_gaus## variofit: model parameters estimated by WLS (weighted least squares):
## covariance model is: gaussian
## parameter estimates:
## tausq sigmasq phi
## 1.2062 12.6072 0.0006
## Practical Range with cor=0.05 for asymptotic range: 0.00108736
##
## variofit: minimised weighted sum of squares = 14912.21min(geo_datos$coords[,1])## [1] -76.61415max(geo_datos$coords[,1])## [1] -76.61254min(geo_datos$coords[,2])## [1] 2.380799max(geo_datos$coords[,2])## [1] 2.382694geodatos_grid=expand.grid(lat=seq(-76.61415,-76.61254,l=100),long=seq(2.380799,2.382694,l=100))
plot(geodatos_grid)
points(geo_datos$coords,col="red", pch="16")
geodatos_ko=krige.conv(geo_datos,loc=geodatos_grid,krige=krige.control(nugget=0,trend.d = "cte", trend.l= "cte", cov.pars =c(sigmasq=1.5665, phi=0.0004)))## krige.conv: model with constant mean
## krige.conv: Kriging performed using global neighbourhoodpar(mfrow=c(1,1))
image(geodatos_ko,main="kriging predict", xlab="Lat", ylab="Long")
contour(geodatos_ko,main="Kriging Predict", add=TRUE, drawlabels=TRUE) 
image(geodatos_ko,main="kriging StDv predict", val=sqrt(geodatos_ko$krige.var),xlab="East", ylab="North")
contour(geodatos_ko,main="kriging StDv predict", val=sqrt(geodatos_ko$krige.var),xlab="East", ylab="North")
valida=xvalid(geo_datos,model=model_mco_gaus)## xvalid: number of data locations = 394
## xvalid: number of validation locations = 394
## xvalid: performing cross-validation at location ... 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394,
## xvalid: end of cross-validationMAE=mean(abs(valida$error))
MAE## [1] 1.10579require(raster)## Loading required package: raster## Loading required package: spmat_imagen=data.frame(geodatos_grid, geodatos_ko$predict)
tem_m1_raster=rasterFromXYZ(mat_imagen)
plot(tem_m1_raster)
library(readr)
Fresno_area <- read_csv("C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp")##
## -- Column specification --------------------------------------------------------
## cols(
## col_character()
## )## Warning: 9 parsing failures.
## row col expected actual file
## 1 embedded null 'C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp'
## 3 -- 1 columns 2 columns 'C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp'
## 17 embedded null 'C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp'
## 17 -- 1 columns 2 columns 'C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp'
## 18 embedded null 'C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp'
## ... ... ......... ............. .......................................................................................................................
## See problems(...) for more details.finca=shapefile("C:/Users/linam/Desktop/Doctorado-20201031T024248Z-001/Doctorado/Semestre V/Analisis Espacial de Datos/Fresno_area.shp")
plot(finca, add=TRUE)
tem_m1_raster_finca=mask(tem_m1_raster, finca)
plot(tem_m1_raster_finca)
plot(finca, add=TRUE)
crs(tem_m1_raster_finca)=crs(finca)
require(rasterVis)## Loading required package: rasterVis## Loading required package: lattice## Loading required package: latticeExtra##
## Attaching package: 'latticeExtra'## The following object is masked from 'package:ggplot2':
##
## layerlevelplot(tem_m1_raster_finca, par.settings=BuRdTheme)
require(leaflet)
leaflet() %>% addTiles() %>% addRasterImage(tem_m1_raster_finca,opacity = 0.5, colors="Spectral")## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
## +wktext +no_defs +type=crs## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
## +wktext +no_defs +type=crs## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition