Laporan Praktikum 1
Tugas STA553 - Analisis Statistika Spasial
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Pengenalan Struktur R untuk Data Spasial (Responsi Pertemuan 1)
Jenis Data Spasial
Data spasial terdiri dari data vector dan data raster. Contoh data vektor adalah data peta administrasi. Data vektor terdiri dari point, line, polygon. Contoh data raster adalah data pada google eart. Data raster terdiri dari Rasterlayer dan Rasterstack.
Packages
Packages yang digunakan adalah sp,gstat rgdal, spData, readxl, raster, spdep
library(sp)
library(gstat)
library(rgdal) # membaca shapefile
library(spData) #data columbus
library(readxl) # membaca file excel
library(raster) # untuk menambahkan text pada peta
library(spdep) # pembobot data spasialData
Data dalam package R
Dengan memanfaatkan datasets yang ada di R pada package yang sesuai tema. Sebagai contoh untuk data spasial ini dapat menggunakan data yang berada pada package sp. Disini digunakan meuse datasets
ls() # melihat data set yang sedang dijalankan pada R kota## character(0)data(meuse) # menggunakan data meuse
ls() # panggil list lagi untuk melihat berhasil atau tiDak## [1] "meuse"Untuk melihat struktur data dapat menggunakan fungsi str diikuti oleh nama datasets nya.
str(meuse)## 'data.frame': 155 obs. of 14 variables:
## $ x : num 181072 181025 181165 181298 181307 ...
## $ y : num 333611 333558 333537 333484 333330 ...
## $ cadmium: num 11.7 8.6 6.5 2.6 2.8 3 3.2 2.8 2.4 1.6 ...
## $ copper : num 85 81 68 81 48 61 31 29 37 24 ...
## $ lead : num 299 277 199 116 117 137 132 150 133 80 ...
## $ zinc : num 1022 1141 640 257 269 ...
## $ elev : num 7.91 6.98 7.8 7.66 7.48 ...
## $ dist : num 0.00136 0.01222 0.10303 0.19009 0.27709 ...
## $ om : num 13.6 14 13 8 8.7 7.8 9.2 9.5 10.6 6.3 ...
## $ ffreq : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ soil : Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 1 1 2 ...
## $ lime : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## $ landuse: Factor w/ 15 levels "Aa","Ab","Ag",..: 4 4 4 11 4 11 4 2 2 15 ...
## $ dist.m : num 50 30 150 270 380 470 240 120 240 420 ...Dapat terlihat bahwa data tersebut berbentuk data.frame terdiri dari 155 observasi dengan 14 peubah. Masing- masing variabel pun diberikan penjelasan jenis/tipe datanya. Untuk angka yang muncul adalah cuplikan data dari masing-masing peubah.
Untuk melihat deskripsi dari data dapat menggunakan fungsi help
help("meuse")This data set gives locations and topsoil heavy metal concentrations, along with a number of soil and landscape variables at the observation locations, collected in a flood plain of the river Meuse, near the village of Stein (NL). Heavy metal concentrations are from composite samples of an area of approximately 15 m x 15 m.
Jika akan dilakukan analisis dapat dilihat dari deksripsi setiap peubah ini.
Plot The Data
Points
coordinates(meuse) <- c("x","y")
plot(meuse)
title("points")
Polygons
Kita menggunakan meuse.riv yang ada dalam package sp.
?meuse.riv## starting httpd help server ... doneThe meuse.riv data consists of an outline of the Meuse river in the area a few kilometers around the meuse data set.The meuse.area polygon has an outline of meuse.grid.two-column data.frame containing 176 coordinates.x and y are in RDM, the Dutch topographical map coordinate system
data(meuse.riv)
meuse.1st <- list(Polygons(list(Polygon(meuse.riv)),"meuse.riv"))
meuse.sr <- SpatialPolygons(meuse.1st)
plot(meuse.sr, col = "blue")
title("polygons")
Grid
Kita menggunakan meuse.grid yang ada dalam package sp.
?meuse.gridThe meuse.grid data frame has 3103 rows and 7 columns; a grid with 40 m x 40 m spacing that covers the Meuse study area. x and y are in RD New, the Dutch topographical map coordinate system. Roger Bivand projected this to UTM in the R-Grass interface package.
data(meuse.grid)
coordinates(meuse.grid) <- c("x", "y")
meuse.grid <- as(meuse.grid, "SpatialPixels")
image(meuse.grid, col = "blue")
title("grid")
image(meuse.grid, col = "lightblue")
plot(meuse.sr, col = "blue", add = TRUE)
plot(meuse,add = TRUE)
data raster
f <- system.file("external/test.grd", package="raster")
library(raster)
r <- raster(f)
plot(r)
?rasterimage(meuse.grid, col = "lightblue")
plot(r, add=TRUE)
plot(meuse.sr, col = "blue", add = TRUE)
plot(meuse,add = TRUE)
External Data Input
Selain menggunakan data yang berada di dalam package R, kita juga dapat memasukkan data dari luar package. Sebagai contoh, akan digunakan data pertumbuhan penduduk.
datapop <- read.csv('http://bit.ly/Popgrowth2000', header=T, sep=',')Untuk melihat dimensi dari data dapat menggunakan fungsi dim. Terlihat bahwa dimensi dari data tersebut adalah 293 observasi dan 5 variabel/peubah.
dim(datapop)## [1] 293 5Untuk melihat struktur data dapat menggunakan fungsi str. Terlihat bahwa data pertumbuhan penduduk ini berbentuk data.frame terdiri dari 293 observasi dengan 5 peubah.
str(datapop)## 'data.frame': 293 obs. of 5 variables:
## $ City : chr "Abidjan" "Adana" "Addis Ababa" "Adelaide" ...
## $ Country : chr "Cote d'Ivoire" "Turkey" "Ethiopia" "Australia" ...
## $ Latitude : num 5.31 37 9.02 -34.93 23.04 ...
## $ Longitude : num -4.01 35.33 38.75 138.6 72.57 ...
## $ PopGrowth_2000: int 1929000 1041000 2424000 1092000 2954000 1582000 3339000 2562000 1135000 1147000 ...Untuk melihat data pertumbuhan penduduk tersebut dalam bentuk tabel dapat menggunakan fungsi view. Akan memunculkan sebuah menu berisi tabel data tersebut.
View(datapop)Plot
Ada beberapa alternatif dalam R untuk membuat peta. Dapat menggunakan base plot atau menggunakan levelplot yang dapat diakses dengan fungsi spplot. Untuk membuat peta diperlukan shape file. File ini adalah digital vector dari peta tersebut, berisi beberapa file yaitu .shp, .shx, dan .dbf. Untuk membaca peta pada R menggunakan fungsi readOGR yang berada di library rgdal.
coordinates(datapop) <- c("Longitude", "Latitude")
plot(datapop)
size<-datapop$PopGrowth_2000/sum(datapop$PopGrowth_2000)
plot(datapop,pch=20, col="steelblue", cex=size*100)
Menambahkan Peta Dunia
Install package
library(rworldmap)## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')data(package="rworldmap")
data(countriesCoarse,envir=environment(),package="rworldmap")Melihat struktur Data
str(countriesCoarse)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 243 obs. of 49 variables:
## .. ..$ ScaleRank : int [1:243] 1 1 1 1 1 1 1 3 1 1 ...
## .. ..$ LabelRank : int [1:243] 1 1 1 1 1 1 1 3 1 1 ...
## .. ..$ FeatureCla : Factor w/ 2 levels "Adm-0 country",..: 2 2 2 2 2 2 2 2 2 2 ...
## .. ..$ SOVEREIGNT : Factor w/ 202 levels "Afghanistan",..: 1 5 2 189 8 9 6 60 10 11 ...
## .. ..$ SOV_A3 : Factor w/ 202 levels "AFG","AGO","ALB",..: 1 2 3 5 6 7 8 61 10 11 ...
## .. ..$ ADM0_DIF : num [1:243] 0 0 0 0 0 0 0 1 0 0 ...
## .. ..$ LEVEL : num [1:243] 2 2 2 2 2 2 2 2 2 2 ...
## .. ..$ TYPE : Factor w/ 6 levels "Country","County",..: 6 6 6 6 6 6 5 3 1 6 ...
## .. ..$ ADMIN : Factor w/ 244 levels "Afghanistan",..: 1 7 3 228 11 12 9 76 15 16 ...
## .. ..$ ADM0_A3 : Factor w/ 244 levels "ABW","AFG","AGO",..: 2 3 5 8 9 10 12 14 16 17 ...
## .. ..$ GEOU_DIF : num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ GEOUNIT : Factor w/ 244 levels "Afghanistan",..: 1 7 3 229 11 12 9 76 15 16 ...
## .. ..$ GU_A3 : Factor w/ 244 levels "ABW","AFG","AGO",..: 2 3 5 8 9 10 12 14 16 17 ...
## .. ..$ SU_DIF : num [1:243] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ SUBUNIT : Factor w/ 244 levels "Afghanistan",..: 1 7 3 229 11 12 9 76 15 16 ...
## .. ..$ SU_A3 : Factor w/ 244 levels "ABW","AFG","AGO",..: 2 3 5 8 9 10 12 14 16 17 ...
## .. ..$ NAME : Factor w/ 243 levels "Afghanistan",..: 1 7 3 229 11 12 9 75 15 16 ...
## .. ..$ ABBREV : Factor w/ 240 levels "A.C.Is.","Afg.",..: 2 8 4 222 11 12 9 74 15 14 ...
## .. ..$ POSTAL : Factor w/ 234 levels "A","AE","AF",..: 3 8 6 2 10 11 9 203 13 1 ...
## .. ..$ NAME_FORMA : Factor w/ 194 levels "Arab Republic of Egypt",..: 44 75 74 NA 2 76 NA 185 7 77 ...
## .. ..$ TERR_ : Factor w/ 13 levels "Assoc. with N.Z.",..: NA NA NA NA NA NA 10 9 NA NA ...
## .. ..$ NAME_SORT : Factor w/ 244 levels "Afghanistan",..: 1 7 3 229 11 12 9 78 15 16 ...
## .. ..$ MAP_COLOR : num [1:243] 7 1 6 3 13 10 0 11 7 4 ...
## .. ..$ POP_EST : num [1:243] 28400000 12799293 3639453 4798491 40913584 ...
## .. ..$ GDP_MD_EST : num [1:243] 22270 110300 21810 184300 573900 ...
## .. ..$ FIPS_10_ : Factor w/ 3 levels "-99","0","FG": 2 2 2 2 2 2 2 2 2 2 ...
## .. ..$ ISO_A2 : Factor w/ 236 levels "-99","AD","AE",..: 4 9 7 3 11 8 10 206 14 13 ...
## .. ..$ ISO_A3 : Factor w/ 244 levels "ABW","AFG","AGO",..: 2 3 6 8 9 10 13 14 16 17 ...
## .. ..$ ISO_N3 : num [1:243] 4 24 8 784 32 51 10 260 36 40 ...
## .. ..$ ISO3 : Factor w/ 244 levels "ABW","AFG","AGO",..: 2 3 6 8 9 10 13 14 16 17 ...
## .. ..$ LON : num [1:243] 66.1 17.5 20 54.2 -65.1 ...
## .. ..$ LAT : num [1:243] 33.9 -12.3 41.1 23.9 -35.2 ...
## .. ..$ ISO3.1 : Factor w/ 243 levels "ABW","AFG","AGO",..: 2 3 6 8 9 10 13 14 16 17 ...
## .. ..$ ADMIN.1 : Factor w/ 243 levels "Afghanistan",..: 1 7 3 227 11 12 9 75 15 16 ...
## .. ..$ REGION : Factor w/ 7 levels "Africa","Antarctica",..: 3 1 5 3 7 5 2 NA 4 5 ...
## .. ..$ continent : Factor w/ 6 levels "Africa","Antarctica",..: 4 1 4 4 6 4 2 NA 3 4 ...
## .. ..$ GEO3major : Factor w/ 7 levels "Africa","Asia and the Pacific",..: 2 1 3 7 4 3 6 NA 2 3 ...
## .. ..$ GEO3 : Factor w/ 24 levels "Antarctic","Arabian Peninsula",..: 17 20 8 2 16 10 1 NA 3 23 ...
## .. ..$ IMAGE24 : Factor w/ 26 levels "Antarctica","Asia-Stan",..: 9 21 5 14 18 19 1 NA 16 26 ...
## .. ..$ GLOCAF : Factor w/ 19 levels "Brazil","Canada",..: 14 17 4 9 13 5 NA NA 11 4 ...
## .. ..$ Stern : Factor w/ 13 levels "Australasia",..: 4 11 6 13 9 6 NA NA 1 6 ...
## .. ..$ SRESmajor : Factor w/ 4 levels "ALM","ASIA","OECD90",..: 2 1 4 1 1 4 NA NA 3 3 ...
## .. ..$ SRES : Factor w/ 11 levels "Central and Eastern Europe (EEU)",..: 9 10 1 4 3 5 NA NA 8 11 ...
## .. ..$ GBD : Factor w/ 21 levels "Asia Pacific, High Income",..: 4 18 8 15 13 2 NA NA 6 10 ...
## .. ..$ AVOIDnumeric: int [1:243] 21 24 25 30 26 25 NA NA 10 25 ...
## .. ..$ AVOIDname : Factor w/ 30 levels "Australia","Brazil",..: 19 27 7 15 25 7 NA NA 1 7 ...
## .. ..$ LDC : Factor w/ 2 levels "LDC","other": 1 1 2 2 2 2 2 NA 2 2 ...
## .. ..$ SID : Factor w/ 2 levels "other","SID": 1 1 1 1 1 1 1 NA 1 1 ...
## .. ..$ LLDC : Factor w/ 2 levels "LLDC","other": 1 2 2 2 2 1 2 NA 2 2 ...
## ..@ polygons :List of 243
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 66.1 33.9
## .. .. .. .. .. .. ..@ area : num 63.6
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:69, 1:2] 61.2 62.2 63 63.2 64 ...
## .. .. .. ..@ plotOrder: int 1
## .. .. .. ..@ labpt : num [1:2] 66.1 33.9
## .. .. .. ..@ ID : chr "Afghanistan"
## .. .. .. ..@ area : num 63.6
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 2
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 17.5 -12.3
## .. .. .. .. .. .. ..@ area : num 103
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:66, 1:2] 16.3 16.6 16.9 17.1 17.5 ...
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 12.38 -5.05
## .. .. .. .. .. .. ..@ area : num 0.653
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:9, 1:2] 12.4 12.2 11.9 12.3 12.6 ...
## .. .. .. ..@ plotOrder: int [1:2] 1 2
## .. .. .. ..@ labpt : num [1:2] 17.5 -12.3
## .. .. .. ..@ ID : chr "Angola"
## .. .. .. ..@ area : num 104
## .. .. .. ..$ comment: chr "0 0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 20 41.1
## .. .. .. .. .. .. ..@ area : num 3.19
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:22, 1:2] 20.6 20.5 20.6 21 21 ...
## .. .. .. ..@ plotOrder: int 1
## .. .. .. ..@ labpt : num [1:2] 20 41.1
## .. .. .. ..@ ID : chr "Albania"
## .. .. .. ..@ area : num 3.19
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 54.2 23.9
## .. .. .. .. .. .. ..@ area : num 7.1
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:22, 1:2] 51.6 51.8 51.8 52.6 53.4 ...
## .. .. .. ..@ plotOrder: int 1
## .. .. .. ..@ labpt : num [1:2] 54.2 23.9
## .. .. .. ..@ ID : chr "United Arab Emirates"
## .. .. .. ..@ area : num 7.1
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 2
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] -65.1 -35.2
## .. .. .. .. .. .. ..@ area : num 276
## .. .. .. .. .. .. ..@ hole : logi FALSE
## .. .. .. .. .. .. ..@ ringDir: int 1
## .. .. .. .. .. .. ..@ coords : num [1:110, 1:2] -65 -64.4 -64 -62.8 -62.7 ...
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] -67.3 -54.4
## .. .. .. .. .. .. ..@ area : num 3.3
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. .. .. ..@ coords : num [1:11, 1:2] -65.5 -66.5 -67 -67.6 -68.6 ...
## .. .. .. ..@ plotOrder: int [1:2] 1 2
## .. .. .. ..@ labpt : num [1:2] -65.1 -35.2
## .. .. .. ..@ ID : chr "Argentina"
## .. .. .. ..@ area : num 279
## .. .. .. ..$ comment: chr "0 0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 45 40.2
## .. .. .. .. .. .. ..@ area : num 3.03
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. ..@ plotOrder: int 1
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## .. .. .. ..@ ID : chr "Armenia"
## .. .. .. ..@ area : num 3.03
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 8
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 21.3 -80.5
## .. .. .. .. .. .. ..@ area : num 5983
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] -48 -79.6
## .. .. .. .. .. .. ..@ area : num 20.4
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] -71 -71.1
## .. .. .. .. .. .. ..@ area : num 12.7
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. .. .. .. ..@ area : num 4.2
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. .. .. .. ..@ area : num 3.72
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. .. .. ..@ area : num 1.54
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. ..@ plotOrder: int [1:8] 1 2 3 4 5 6 7 8
## .. .. .. ..@ labpt : num [1:2] 21.3 -80.5
## .. .. .. ..@ ID : chr "Antarctica"
## .. .. .. ..@ area : num 6029
## .. .. .. ..$ comment: chr "0 0 0 0 0 0 0 0"
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. .. .. .. ..@ area : num 1.43
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. ..@ ID : chr "French Southern and Antarctic Lands"
## .. .. .. ..@ area : num 1.43
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## .. .. .. .. .. .. ..@ area : num 688
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. .. .. .. ..@ labpt : num [1:2] 147 -42
## .. .. .. .. .. .. ..@ area : num 7.18
## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. ..@ labpt : num [1:2] 134.4 -25.6
## .. .. .. ..@ ID : chr "Australia"
## .. .. .. ..@ area : num 696
## .. .. .. ..$ comment: chr "0 0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. .. .. .. .. .. ..@ hole : logi FALSE
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## .. .. .. ..@ plotOrder: int 1
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## .. .. .. ..@ ID : chr "Austria"
## .. .. .. ..@ area : num 10.2
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. .. .. .. ..@ area : num 9.1
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## .. .. .. ..$ comment: chr "0"
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## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. ..@ ID : chr "Sri Lanka"
## .. .. .. ..@ area : num 5.36
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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## .. .. .. ..@ area : num 2.56
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 23.9 55.3
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## .. .. .. .. .. .. ..@ coords : num [1:19, 1:2] 22.7 22.7 22.8 22.3 21.3 ...
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## .. .. .. ..@ area : num 9.02
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 5.97 49.77
## .. .. .. .. .. .. ..@ area : num 0.302
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## .. .. .. .. .. .. ..@ coords : num [1:7, 1:2] 6.04 6.24 6.19 5.9 5.67 ...
## .. .. .. ..@ plotOrder: int 1
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## .. .. .. ..@ ID : chr "Luxembourg"
## .. .. .. ..@ area : num 0.302
## .. .. .. ..$ comment: chr "0"
## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
## .. .. .. ..@ Polygons :List of 1
## .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
## .. .. .. .. .. .. ..@ labpt : num [1:2] 24.8 56.8
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## .. .. .. ..@ plotOrder: int 1
## .. .. .. ..@ labpt : num [1:2] 24.8 56.8
## .. .. .. ..@ ID : chr "Latvia"
## .. .. .. ..@ area : num 9.4
## .. .. .. ..$ comment: chr "0"
## .. .. [list output truncated]
## ..@ plotOrder : int [1:243] 7 135 28 167 31 23 9 66 84 5 ...
## ..@ bbox : num [1:2, 1:2] -180 -90 180 83.6
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "x" "y"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"Menampilkan peta dunia dalam bentuk dasar berupa garis-garis wilayah.
plot(countriesCoarse) ## Warning in wkt(obj): CRS object has no comment
Menggabungkan peta dunia dengan data pertumbuhan penduduk:
plot(countriesCoarse) ## Warning in wkt(obj): CRS object has no commentplot(datapop,add=TRUE, pch=20)
Coba tampilkan data pertumbuhan penduduk dengan ukuran titik sesuai dengan besarnya pertumbuhan, pada peta dunia.!
plot(countriesCoarse) ## Warning in wkt(obj): CRS object has no commentplot(datapop,add=TRUE, pch=20, col="red", cex=size*150)
Rasterize
library(raster)
r <- raster(datapop)
res(r)<-c(5,5)
nc <- rasterize(coordinates(datapop), r, fun=mean, background=NA)
plot(nc)
plot(countriesCoarse, add=TRUE)
Cara Lain menampilkan peta Tidak dijalankan di sini karena Error: Google now requires an API key. See ?register_google for details.):
#library(ggmap)
#map <- get_map(location = 'Indonesia', zoom = 4)
#plot(map)
#datapop<-read.csv('http://bit.ly/Popgrowth2000', header=T,sep=',')
#ggmap(map)+geom_point(aes(x = Longitude, y = Latitude),
#data = pop_ina,col="red")Tugas Mandiri : Peta Pulau Jawa
- Impor Data Polygon (SHP File)
library(rgdal)- Import Data dari Direktori
Menggunakan fungsi readOGR: >>readOGR(dsn=‘folder (direktori) tempat shape file disimpan’, layer=‘nama shape file (tanpa .shp extension)’)
jawa <- readOGR(dsn='D:\\Kuliah S2 IPB\\Bahan Kuliah\\Semester 2 SSD 2020\\STA533 Spasial\\PRAKTIKUM\\R\\Map of Jawa (original)', layer='jawa')## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS
## = dumpSRS, : Discarded datum D_unknown in Proj4 definition: +proj=longlat
## +ellps=GRS80 +no_defs## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\Map of Jawa (original)", layer: "jawa"
## with 116 features
## It has 5 fieldsMelihat data:
head(jawa@data)## MISKIN KODE_KAB NAMA_KAB KODE_PROP NAMA_PROP
## 0 0 3501 Pacitan 35 Jawa Timur
## 1 0 3502 Ponorogo 35 Jawa Timur
## 2 0 3503 Trenggalek 35 Jawa Timur
## 3 0 3504 Tulungagung 35 Jawa Timur
## 4 0 3508 Lumajang 35 Jawa Timur
## 5 0 3511 Bondowoso 35 Jawa Timur- Menampilkan Peta Pulau Jawa:
plot(jawa)
- Menampilkan nama kota/kabupaten
plot(jawa)
text(jawa,'NAMA_KAB',cex=0.5)
- Memberi warna yang berbeda utk setiap Propinsi
plot(jawa,col=jawa$KODE_PROP-30)
Mengganti referensi warna:
Untuk memberikan warna peta menggunakan pallete untuk membatasi warna apa yang akan dipakai.
palette(rainbow(6))
plot(jawa, col=jawa$KODE_PROP-30)
#setiap warna sesuai dengan ID-nya
# karena dibatasi hanya 6 warna sehingga ada beberapa warna yang sama pada suatu daerahpalette(terrain.colors(6))
plot(jawa, col=jawa$KODE_PROP-30)
Konfigurasi Titik Ruang dalam Ruang (Responsi Pertemuan 2)
Review
Titik adalah objek dalam dimensi nol dan menggambarkan sifat dari titik menduduki ruang. Pola keseluruhan titik dalam ruang mencerminkan sifat keseluruhan titik tersebut. Pembahasan bukan membicarakan sebuah titik tapi keseluruhan titik dalam ruang.
Ada tiga pola titik:
Acak (Poisson) : setiap titik mempunyai peluang yang sama untuk menduduki suatu ruang dan tidak dipengaruhi oleh titik yang lain.
Uniform : setiap titik adalah sejauh mungkin dari titik-titik tetangganya.
Cluster : Banyak titik terkonsentrasi menduduki pada ruang yang sama, dan ruang yang lain sangat sedikit ditempati oleh titik.
First Approach: Quadrant Method
Metode Kuadran
Bagilah Daerah Studi menjadi beberapa sel yang berukuran sama. Ukuran Sel Ditentukan oleh skala yang diinginkan.
Tentukan Rata-rata Banyaknya Titik per sel.
Tentukan Variance banyaknya titik per sel.
Hitung perbandingan Ragam dengan Rata-rata (VMR).
Aplikasi pada R
library(spatstat)data(swedishpines)
help(swedishpines)Swedish Pines Point Pattern
Description
The data give the locations of pine saplings in a Swedish forest.
Usage
data(swedishpines)
Format
An object of class “ppp” representing the point pattern of tree locations in a rectangular plot 9.6 by 10 metres.
Cartesian coordinates are given in decimetres (multiples of 0.1 metre) rounded to the nearest decimetre. Type rescale(swedishpines) to get an equivalent dataset where the coordinates are expressed in metres.
See ppp.object for details of the format of a point pattern object.
X <- swedishpines
plot(X)
sepintas terlihat acak
summary(X)## Planar point pattern: 71 points
## Average intensity 0.007395833 points per square unit (one unit = 0.1 metres)
##
## Coordinates are integers
## i.e. rounded to the nearest unit (one unit = 0.1 metres)
##
## Window: rectangle = [0, 96] x [0, 100] units
## Window area = 9600 square units
## Unit of length: 0.1 metresPlot Contour seperti memprediksi ketinggian kepekatan menjadi dua dimensi. Jika pakai contour:
contour(density(X,10),axes = F)
nx dan ny default nya 5 x 5 dibaginya.
q <- quadratcount(X, nx = 4, ny = 3)
q## x
## y [0,24) [24,48) [48,72) [72,96]
## [66.7,100] 7 3 6 5
## [33.3,66.7) 5 9 7 7
## [0,33.3) 4 3 6 9plot(q)
mu <- mean(q)
sigma <- sd(q)^2
VMR <- sigma/mu
VMR## [1] 0.6901408quadrat.test(q) #H1 tidak sama dengan##
## Chi-squared test of CSR using quadrat counts
##
## data:
## X2 = 7.5915, df = 11, p-value = 0.5013
## alternative hypothesis: two.sided
##
## Quadrats: 4 by 3 grid of tilesp-value = 0.5013 > alpha = 0.05 sehingga tidak tolak Ho berarti pada taraf alpha 5%, tidak ada bukti untuk menyatakan bahwa titik tidak menyebar acak.
quadrat.test(q, alt = "regular")##
## Chi-squared test of CSR using quadrat counts
##
## data:
## X2 = 7.5915, df = 11, p-value = 0.2506
## alternative hypothesis: regular
##
## Quadrats: 4 by 3 grid of tilesp-value = 0.2506 > alpha = 0.05 sehingga tidak tolak Ho berarti pada taraf alpha 5%, tidak ada bukti untuk menyatakan bahwa titik tidak menyebar acak atau regular.
quadrat.test(q, alt = "cluster")##
## Chi-squared test of CSR using quadrat counts
##
## data:
## X2 = 7.5915, df = 11, p-value = 0.7494
## alternative hypothesis: clustered
##
## Quadrats: 4 by 3 grid of tilesp-value = 0.7494 > alpha = 0.05 sehingga tidak tolak Ho berarti pada taraf alpha 5%, tidak ada bukti untuk menyatakan bahwa titik tidak menyebar acak atau cluster.
Second Approach: Empirical K-Function
nn <- nndist(swedishpines)
hist(nn)
The Empirical K-Function
Application on R
K<- Kest(swedishpines, correction="Ripley")
plot(K)
E<-envelope(swedishpines,Kest, nsim=99)## 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(E)
Non-graphical test
mad.test(swedishpines, Kest, nsim=99, alternative="two.sided")## 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.##
## Maximum absolute deviation test of CSR
## Monte Carlo test based on 99 simulations
## Summary function: K(r)
## Reference function: theoretical
## Alternative: two.sided
## Interval of distance values: [0, 24] units (one unit = 0.1 metres)
## Test statistic: Maximum absolute deviation
## Deviation = observed minus theoretical
##
## data: swedishpines
## mad = 150.69, rank = 22, p-value = 0.22Menggunakan data external
Jenis data yang diperlukan
Import Data
Data dapat diunduh di: city.rds crime.rds
library(raster)
library(spatstat)city <- readRDS('city.rds')
crime <- readRDS('crime.rds')Konversi data menjadi titik point pattern
border<-city
coord.city<-city@polygons[[1]]@Polygons[[1]]@coords
window<-owin(poly=data.frame(x=rev(coord.city[,1]),
y=rev(coord.city[,2])))
plot(window)
Menghitung titik di dalam kuadran
crime2 <- remove.duplicates(crime)## Warning in wkt(obj): CRS object has no comment
## Warning in wkt(obj): CRS object has no commentcrime2 <- crime2[crime,]## Warning in wkt(obj): CRS object has no comment
## Warning in wkt(obj): CRS object has no commentcrime2.ppp<-ppp(x=crime2@coords[,1],y=crime2@coords[,2],
window=window)## Warning: 5 points were rejected as lying outside the specified windowquad<-quadratcount(crime2.ppp)
plot(quad, col="red")
plot(crime2.ppp, add=T, pch=20, cex = 0.5)## Warning in plot.ppp(crime2.ppp, add = T, pch = 20, cex = 0.5): 5 illegal points
## also plotted
quadrat.test(crime2.ppp,alt="cluster")## Warning: Some expected counts are small; chi^2 approximation may be inaccurate##
## Chi-squared test of CSR using quadrat counts
##
## data: crime2.ppp
## X2 = 480.83, df = 22, p-value < 2.2e-16
## alternative hypothesis: clustered
##
## Quadrats: 23 tiles (irregular windows)Using K-function
K<- Kest(crime2.ppp,
correction="Ripley")
plot(K)
mad.test(crime2.ppp, Kest, nsim = 20, alternative = "greater")## Generating 20 simulations of CSR ...
## 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20.
##
## Done.##
## Maximum signed deviation test of CSR
## Monte Carlo test based on 20 simulations
## Summary function: K(r)
## Reference function: theoretical
## Alternative: greater
## Interval of distance values: [0, 3697.26712973043]
## Test statistic: Maximum signed deviation
## Deviation = observed minus theoretical
##
## data: crime2.ppp
## mad = 16518845, rank = 1, p-value = 0.04762Simulasi Data
Poisson process
To create a Poisson process with uniform intensity of 50 over [0; 1] [0; 1]
set.seed(3108)
pp0 <- rpoispp(50)
plot(pp0)
Matern Clustering
set.seed(3108)
pp3 <- rMatClust(12, 0.1, 4)
plot(pp3)
Apakah data yang dibangkitkan selalu sesuai dengan yg diinginkan? belum tentu
Other generator function
rThomas
rCauchy
rVarGamma
rNeymanScott
rGaussPoisson
Other point pattern data
amacrine (rabbit amacrine cells, locations and 2 types)
anemones (sea anemones data, locations and sizes)
ants (ant nests data, location and 2 types)
bei (tropical rainforest trees, locations)
betacells (cat retinal ganglia data, locations, 2 types and sizes)
bramblecanes (Bramble Canes data, locations and 3 types)
cells (biological cells data, locations) chorley (cancer data, locations and 2 types) finpines (Finnish Pines data, locations and 2 size measures)
hamster (hamster tumour data, locations and 2 types)
japanesepines (Japanese Pines data, locations)
lansing (Lansing Woods data, locations and 6 types)
longleaf (Longleaf Pines data, locations and sizes)
nztrees (trees data, locations)
ponderosa (ponderosa pine trees data, locations)
redwood (redwood samplings data, locations)
spruces (Spruce trees in Saxonia, locations and sizes)
TERIMA KASIH
Referensi
Anisa, R. 2021.Pengenalan Struktur R untuk Data Spasial.Retrieved from newlms.ipb.ac.id
Anisa, R. 2021. Konfigurasi Titik Ruang. Retrieved from newlms.ipb.ac.id
Mahasiswa Pascasarjana Statistika dan Sains Data, IPB University, reniamelia@apps.ipb.ac.id↩︎