LabEcoPob: Análisis Espacial
D. S. Fernandez del Viso
11/1/2019
Introducción
Metodología
Diseño y metodología de campo
Procesamiento de los datos
Análisis de los datos
Cargar datos a data frame
# con datos csv usando paquete readr
library(readr)
melodata <- read_csv("melocactus2016.csv")
melodata## # A tibble: 113 x 5
## Xtran Ytran altura inflor Status
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 99.1 105. 45 17 S
## 2 99.1 105. 42 17 S
## 3 99.0 106. 40 18 X
## 4 97.7 104. 29 0 S
## 5 93.5 107. 26 0 E
## 6 93.4 107. 43 12 S
## 7 89.8 110. 25 0 X
## 8 89.0 111. 27 8 S
## 9 86.4 112. 64 0 E
## 10 86.7 113. 9 0 S
## # … with 103 more rowsdim(melodata)## [1] 113 5Visualización de los datos
# leer solo coordenadas
xy <- melodata[,1:2]
head(xy)## # A tibble: 6 x 2
## Xtran Ytran
## <dbl> <dbl>
## 1 99.1 105.
## 2 99.1 105.
## 3 99.0 106.
## 4 97.7 104.
## 5 93.5 107.
## 6 93.4 107.plot(xy$Xtran, xy$Ytran, type = "p", asp=1)
Crear matriz de distancias
library(spam)
NNmatrix <- nearest.dist(xy, y=NULL, method = "euclidean", upper = TRUE)
dim(NNmatrix)## [1] 113 113class(NNmatrix)## [1] "spam"
## attr(,"package")
## [1] "spam"NNmatrix## Matrix of dimension 113x113 with (row-wise) nonzero elements:
##
## [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [7] 0.55578773 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [13] 0.49040799 0.63639610 0.00000000 0.43185646 0.80056230 0.00000000
## [19] 0.45254834 0.00000000 0.00000000 0.00000000 0.00000000 0.86683332
## [25] 0.86683332 0.86683332 0.87361319 0.89844310 0.89844310 0.00000000
## [31] 0.00000000 0.00000000 0.51865210 0.51400389 0.51400389 0.00000000
## [37] 0.00000000 0.51865210 0.51400389 0.51400389 0.00000000 0.51865210
## [43] 0.51400389 0.51400389 0.00000000 0.02828427 0.02828427 0.00000000
## [49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.42801869
## [61] 0.00000000 0.00000000 0.00000000 0.35171011 0.32140317 0.32695565
## [67] 0.87091905 0.42011903 0.47853944 0.48259714 0.00000000 0.00000000
## [73] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [79] 0.36496575 0.00000000 0.00000000 0.00000000 0.84970583 0.00000000
## [85] 0.97493590 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [91] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [97] 0.00000000 0.00000000 0.00000000 0.00000000 0.20099751 0.36345564
## [103] 0.78746428 0.95005263 0.81006173 0.00000000 0.16278821 0.69318107
## [109] 0.98843310 0.85146932 0.00000000 0.66708320 0.92849340 0.00000000
## [115] 0.61032778 0.54230987 0.00000000 0.14000000 0.95131488 0.00000000
## [121] 0.00000000 0.00000000 0.62369865 0.97616597 0.60539243 0.55081757
## [127] 0.00000000 0.35510562 0.19416488 0.51923020 0.00000000 0.46690470
## [133] 0.80156098 0.00000000 0.34132096 0.00000000 0.00000000 0.52630789
## [139] 0.27513633 0.00000000 0.25317978 0.00000000 0.00000000 0.00000000
## [145] 0.54424259 0.00000000 0.00000000 0.80752709 0.78771822 0.96176920
## [151] 0.00000000 0.02000000 0.30016662 0.73783467 0.73437048 0.79120162
## [157] 0.00000000 0.31064449 0.74886581 0.73246160 0.78587531 0.00000000
## [163] 0.91482239 0.00000000 0.49244289 0.63560994 0.00000000 0.14317821
## [169] 0.00000000 0.00000000 0.27730849 0.80622577 0.00000000 0.59236813
## [175] 0.00000000 0.00000000 0.03605551 0.00000000 0.00000000 0.00000000
## [181] 0.00000000 0.00000000 0.00000000 0.53037722 0.00000000 0.00000000
## [187] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [193] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.34985711
## [199] 0.00000000 0.00000000 0.00000000 0.12165525 0.22360680 0.54571055
## [205] 0.12806248 0.13000000 0.18973666 0.00000000 0.10198039 0.55009090
## [211] 0.10198039 0.19209373 0.31048349 0.00000000 0.57706152 0.16970563
## [217] 0.27802878 0.41231056 0.00000000 0.45099889 0.42059482 0.56302753
## [223] 0.00000000 0.11180340 0.28071338 0.00000000 0.19313208 0.00000000
## Class 'spam' (32-bit)NNmat <- matrix(NNmatrix)
dim(NNmat)## [1] 228 1NNmat## [,1]
## [1,] 0.00000000
## [2,] 0.00000000
## [3,] 0.00000000
## [4,] 0.00000000
## [5,] 0.00000000
## [6,] 0.00000000
## [7,] 0.55578773
## [8,] 0.00000000
## [9,] 0.00000000
## [10,] 0.00000000
## [11,] 0.00000000
## [12,] 0.00000000
## [13,] 0.49040799
## [14,] 0.63639610
## [15,] 0.00000000
## [16,] 0.43185646
## [17,] 0.80056230
## [18,] 0.00000000
## [19,] 0.45254834
## [20,] 0.00000000
## [21,] 0.00000000
## [22,] 0.00000000
## [23,] 0.00000000
## [24,] 0.86683332
## [25,] 0.86683332
## [26,] 0.86683332
## [27,] 0.87361319
## [28,] 0.89844310
## [29,] 0.89844310
## [30,] 0.00000000
## [31,] 0.00000000
## [32,] 0.00000000
## [33,] 0.51865210
## [34,] 0.51400389
## [35,] 0.51400389
## [36,] 0.00000000
## [37,] 0.00000000
## [38,] 0.51865210
## [39,] 0.51400389
## [40,] 0.51400389
## [41,] 0.00000000
## [42,] 0.51865210
## [43,] 0.51400389
## [44,] 0.51400389
## [45,] 0.00000000
## [46,] 0.02828427
## [47,] 0.02828427
## [48,] 0.00000000
## [49,] 0.00000000
## [50,] 0.00000000
## [51,] 0.00000000
## [52,] 0.00000000
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## [54,] 0.00000000
## [55,] 0.00000000
## [56,] 0.00000000
## [57,] 0.00000000
## [58,] 0.00000000
## [59,] 0.00000000
## [60,] 0.42801869
## [61,] 0.00000000
## [62,] 0.00000000
## [63,] 0.00000000
## [64,] 0.35171011
## [65,] 0.32140317
## [66,] 0.32695565
## [67,] 0.87091905
## [68,] 0.42011903
## [69,] 0.47853944
## [70,] 0.48259714
## [71,] 0.00000000
## [72,] 0.00000000
## [73,] 0.00000000
## [74,] 0.00000000
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## [76,] 0.00000000
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## [78,] 0.00000000
## [79,] 0.36496575
## [80,] 0.00000000
## [81,] 0.00000000
## [82,] 0.00000000
## [83,] 0.84970583
## [84,] 0.00000000
## [85,] 0.97493590
## [86,] 0.00000000
## [87,] 0.00000000
## [88,] 0.00000000
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## [99,] 0.00000000
## [100,] 0.00000000
## [101,] 0.20099751
## [102,] 0.36345564
## [103,] 0.78746428
## [104,] 0.95005263
## [105,] 0.81006173
## [106,] 0.00000000
## [107,] 0.16278821
## [108,] 0.69318107
## [109,] 0.98843310
## [110,] 0.85146932
## [111,] 0.00000000
## [112,] 0.66708320
## [113,] 0.92849340
## [114,] 0.00000000
## [115,] 0.61032778
## [116,] 0.54230987
## [117,] 0.00000000
## [118,] 0.14000000
## [119,] 0.95131488
## [120,] 0.00000000
## [121,] 0.00000000
## [122,] 0.00000000
## [123,] 0.62369865
## [124,] 0.97616597
## [125,] 0.60539243
## [126,] 0.55081757
## [127,] 0.00000000
## [128,] 0.35510562
## [129,] 0.19416488
## [130,] 0.51923020
## [131,] 0.00000000
## [132,] 0.46690470
## [133,] 0.80156098
## [134,] 0.00000000
## [135,] 0.34132096
## [136,] 0.00000000
## [137,] 0.00000000
## [138,] 0.52630789
## [139,] 0.27513633
## [140,] 0.00000000
## [141,] 0.25317978
## [142,] 0.00000000
## [143,] 0.00000000
## [144,] 0.00000000
## [145,] 0.54424259
## [146,] 0.00000000
## [147,] 0.00000000
## [148,] 0.80752709
## [149,] 0.78771822
## [150,] 0.96176920
## [151,] 0.00000000
## [152,] 0.02000000
## [153,] 0.30016662
## [154,] 0.73783467
## [155,] 0.73437048
## [156,] 0.79120162
## [157,] 0.00000000
## [158,] 0.31064449
## [159,] 0.74886581
## [160,] 0.73246160
## [161,] 0.78587531
## [162,] 0.00000000
## [163,] 0.91482239
## [164,] 0.00000000
## [165,] 0.49244289
## [166,] 0.63560994
## [167,] 0.00000000
## [168,] 0.14317821
## [169,] 0.00000000
## [170,] 0.00000000
## [171,] 0.27730849
## [172,] 0.80622577
## [173,] 0.00000000
## [174,] 0.59236813
## [175,] 0.00000000
## [176,] 0.00000000
## [177,] 0.03605551
## [178,] 0.00000000
## [179,] 0.00000000
## [180,] 0.00000000
## [181,] 0.00000000
## [182,] 0.00000000
## [183,] 0.00000000
## [184,] 0.53037722
## [185,] 0.00000000
## [186,] 0.00000000
## [187,] 0.00000000
## [188,] 0.00000000
## [189,] 0.00000000
## [190,] 0.00000000
## [191,] 0.00000000
## [192,] 0.00000000
## [193,] 0.00000000
## [194,] 0.00000000
## [195,] 0.00000000
## [196,] 0.00000000
## [197,] 0.00000000
## [198,] 0.34985711
## [199,] 0.00000000
## [200,] 0.00000000
## [201,] 0.00000000
## [202,] 0.12165525
## [203,] 0.22360680
## [204,] 0.54571055
## [205,] 0.12806248
## [206,] 0.13000000
## [207,] 0.18973666
## [208,] 0.00000000
## [209,] 0.10198039
## [210,] 0.55009090
## [211,] 0.10198039
## [212,] 0.19209373
## [213,] 0.31048349
## [214,] 0.00000000
## [215,] 0.57706152
## [216,] 0.16970563
## [217,] 0.27802878
## [218,] 0.41231056
## [219,] 0.00000000
## [220,] 0.45099889
## [221,] 0.42059482
## [222,] 0.56302753
## [223,] 0.00000000
## [224,] 0.11180340
## [225,] 0.28071338
## [226,] 0.00000000
## [227,] 0.19313208
## [228,] 0.00000000NNo <- NNmat > 0
NNo## [,1]
## [1,] FALSE
## [2,] FALSE
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## [228,] FALSENN <- NNmat[NNo]
NN## [1] 0.55578773 0.49040799 0.63639610 0.43185646 0.80056230 0.45254834
## [7] 0.86683332 0.86683332 0.86683332 0.87361319 0.89844310 0.89844310
## [13] 0.51865210 0.51400389 0.51400389 0.51865210 0.51400389 0.51400389
## [19] 0.51865210 0.51400389 0.51400389 0.02828427 0.02828427 0.42801869
## [25] 0.35171011 0.32140317 0.32695565 0.87091905 0.42011903 0.47853944
## [31] 0.48259714 0.36496575 0.84970583 0.97493590 0.20099751 0.36345564
## [37] 0.78746428 0.95005263 0.81006173 0.16278821 0.69318107 0.98843310
## [43] 0.85146932 0.66708320 0.92849340 0.61032778 0.54230987 0.14000000
## [49] 0.95131488 0.62369865 0.97616597 0.60539243 0.55081757 0.35510562
## [55] 0.19416488 0.51923020 0.46690470 0.80156098 0.34132096 0.52630789
## [61] 0.27513633 0.25317978 0.54424259 0.80752709 0.78771822 0.96176920
## [67] 0.02000000 0.30016662 0.73783467 0.73437048 0.79120162 0.31064449
## [73] 0.74886581 0.73246160 0.78587531 0.91482239 0.49244289 0.63560994
## [79] 0.14317821 0.27730849 0.80622577 0.59236813 0.03605551 0.53037722
## [85] 0.34985711 0.12165525 0.22360680 0.54571055 0.12806248 0.13000000
## [91] 0.18973666 0.10198039 0.55009090 0.10198039 0.19209373 0.31048349
## [97] 0.57706152 0.16970563 0.27802878 0.41231056 0.45099889 0.42059482
## [103] 0.56302753 0.11180340 0.28071338 0.19313208Aplicar método del vecino más cercano
#sumatoria distancias NN
sd <- sum(NN)
sprintf("Sumatoria de distancias: %.2f m", sd)## [1] "Sumatoria de distancias: 54.44 m"#número de distancias NN
n <- length(NN)
sprintf("Número de distancias: %1.f",n)## [1] "Número de distancias: 106"#distancia media NN: rA
rA <- sd/n
sprintf("Distancia media: %.2f m",rA)## [1] "Distancia media: 0.51 m"#densidad real
#Ra: radio del circulo
Ra <- 25
N <- ncol(NNmatrix)
d <- N/(pi*Ra^2)
sprintf("Densidad real: %.3f individuos/m^2",d)## [1] "Densidad real: 0.058 individuos/m^2"#distancia nn esperada: rE
rE <- 1/(2*sqrt(d))
sprintf("Distancia esperada: %.2f m",rE)## [1] "Distancia esperada: 2.08 m"# R: indice de agregación
R <- rA/rE
sprintf("Indice de agregación: %.3f",R)## [1] "Indice de agregación: 0.246"#prueba de significancia Ho:random
#sr: error estandar de rE
sr <- 0.26136/sqrt(N*d)
sprintf("Error estándar de la distancia esperada: %.3f m",sr)## [1] "Error estándar de la distancia esperada: 0.102 m"z <- abs((rA - rE)/sr)
sprintf("Estadístico Z: %.3f",z)## [1] "Estadístico Z: 15.326"vc <- abs(qnorm(0.05))
sprintf("Valor crítico distribución normal (p=0.05): %.3f",vc)## [1] "Valor crítico distribución normal (p=0.05): 1.645"Referencias
Nearest Neighbor Index [WWW Document], n.d. . IB Geography. URL http://www.GeoIB.com/nearest-neighbor-index.html (accessed 12.15.17).