AnĂ¡lisis de Patrones Puntuados en R
##R Markdown en creaciĂ³n, utilizar con precauciĂ³n.
Patrones Espaciales Puntuados
En el presente documento de R Markdown se presentan el anĂ¡lisis de Patrones Espaciales Puntuados. Se generaron en QGIS un polĂgono cuadrado (ventana) y dentro de la ventana se generaron tres shapes con los siguientes patrones:
- Conglomerado
- Aleatorio
- Equidistante
Para generar la ventana se extrajeron los vĂ©rtices y se calcularon las coordenadas (GTM), tanto para la ventana cĂ³mo para los tres patrones generados. Posteriormente, se exportĂ³ la tabla asociada en formato CSV.
Jalar los ficheros CSV desde su lugar de origen al de trabajo
Desde la Terminal de R, ejecutar los siguientes comandos. Se deberĂ¡n modificar la ubicaciĂ³n de acuerdo al ordenador
cp /media/manuel/MBARRIOS/GIS/SpatialPointPattern/clustered1.csv ./CSV-files/ cp /media/manuel/MBARRIOS/GIS/SpatialPointPattern/random1.csv ./CSV-files/ cp /media/manuel/MBARRIOS/GIS/SpatialPointPattern/equidistant1.CSV ./CSV-files/ cp /media/manuel/MBARRIOS/GIS/SpatialPointPattern/window1.CSV ./CSV-files/
PPP
Cargar las librerĂas
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(maptools)## Loading required package: sp## Checking rgeos availability: FALSE
## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.
## Note: when rgeos is not available, polygon geometry computations in maptools depend on gpclib,
## which has a restricted licence. It is disabled by default;
## to enable gpclib, type gpclibPermit()library(spatstat)## Loading required package: spatstat.data## Loading required package: spatstat.geom## spatstat.geom 2.4-0## Loading required package: spatstat.random## spatstat.random 2.2-0## Loading required package: spatstat.core## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## Loading required package: rpart## spatstat.core 2.4-4## Loading required package: spatstat.linnet## spatstat.linnet 2.3-2##
## spatstat 2.3-4 (nickname: 'Watch this space')
## For an introduction to spatstat, type 'beginner'Cargar los ficheros de las localidades muestreadas
cluster<-read.csv("./CSV-files/clustered1.csv", header=T, sep=',')
equidistant<-read.csv("./CSV-files/equidistant1.csv", header=T, sep=',')
random<-read.csv("./CSV-files/random1.csv", header=T, sep=',')
names(cluster); names(equidistant); names(random)## [1] "id" "X" "Y"## [1] "id" "X" "Y"## [1] "rand_point" "id" "X" "Y"Window
Crear el objeto con las coordenadas de la ventana
window<-read.csv("./CSV-files/window1-vert.csv", header=T, sep=',')
names(cluster); names(equidistant); names(random)## [1] "id" "X" "Y"## [1] "id" "X" "Y"## [1] "rand_point" "id" "X" "Y"Crear la ventana con el comando owin
Mask <- read.csv('./CSV-files/window1-vert.csv',sep = ',',header=T)
W1 <- owin(poly=data.frame(x=rev(Mask$X), y=rev(Mask$Y)))
plot(W1)
Modelos
Preparar el archivo PPP para clustered
En los siguientes diagramas se despliegan los modelos de densidad, KRipleys, KRipleys envelopes y L-transformation
p.clustered<-ppp(cluster$X, cluster$Y, window=W1)## Warning: data contain duplicated pointsp2.clustered<- as.ppp(p.clustered)#remueve los puntos ilegales
plot(p2.clustered) # Imprimir los puntos
DD.p2.clustered <- density(p2.clustered)
plot(DD.p2.clustered) #Imprimir la desnisad
KK.p2.clustered<- Kest(p2.clustered)
plot(KK.p2.clustered) #Realizar la prueba de Ripleys K-function
plot(envelope(p2.clustered,Kest))## Generating 99 simulations of CSR ...
## 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.
##
## Done.
plot(envelope(p2.clustered,Lest, nsim=1000,fix.n = T))## Generating 1000 simulations of CSR with fixed number of points ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990......... 1000.
##
## Done.
Preparar el archivo PPP para random
En los siguientes diagramas se despliegan los modelos de densidad, KRipleys, KRipleys envelopes y L-transformation
p.random<-ppp(random$X, random$Y, window=W1)
p2.random<- as.ppp(p.random)#remueve los puntos ilegales
plot(p2.random) # Imprimir los puntos
DD.p2.random <- density(p2.random)
plot(DD.p2.random) #Imprimir la desnisad
KK.p2.random<- Kest(p2.random)
plot(KK.p2.random) #Realizar la prueba de Ripleys K-function
plot(envelope(p2.random,Kest))## Generating 99 simulations of CSR ...
## 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.
##
## Done.
plot(envelope(p2.random,Lest, nsim=1000,fix.n = T))## Generating 1000 simulations of CSR with fixed number of points ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990......... 1000.
##
## Done.
Preparar el archivo PPP para archivos equidistantes
En los siguientes diagramas se despliegan los modelos de densidad, KRipleys, KRipleys envelopes y L-transformation
p.equidistant<-ppp(equidistant$X, equidistant$Y, window=W1)
p2.equidistant<- as.ppp(p.equidistant)#remueve los puntos ilegales
plot(p2.equidistant) # Imprimir los puntos
DD.p2.equidistant <- density(p2.equidistant)
plot(DD.p2.equidistant) #Imprimir la desnisad
KK.p2.equidistant<- Kest(p2.equidistant)
plot(KK.p2.equidistant) #Realizar la prueba de Ripleys K-function
plot(envelope(p2.equidistant,Kest))## Generating 99 simulations of CSR ...
## 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.
##
## Done.
plot(envelope(p2.equidistant,Lest, nsim=1000,fix.n = T))## Generating 1000 simulations of CSR with fixed number of points ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990......... 1000.
##
## Done.