Rरैंडम वर्षा डेटा का उपयोग करके मेरा अपना समाधान जोड़ें
library(tidyverse)
library(sp) # for coordinates, CRS, proj4string, etc
library(gstat)
library(maptools)
# Coordinates of gridded precipitation cells
precGridPts <- ("ID lat long
1 46.78125 -121.46875
2 46.84375 -121.53125
3 46.84375 -121.46875
4 46.84375 -121.40625
5 46.84375 -121.34375
6 46.90625 -121.53125
7 46.90625 -121.46875
8 46.90625 -121.40625
9 46.90625 -121.34375
10 46.90625 -121.28125
11 46.96875 -121.46875
12 46.96875 -121.40625
13 46.96875 -121.34375
14 46.96875 -121.28125
15 46.96875 -121.21875
16 46.96875 -121.15625
")
# Read precipitation cells
precGridPtsdf <- read.table(text = precGridPts, header = TRUE)
किसी ऑब्जेक्ट में कनवर्ट करें
sp::coordinates(precGridPtsdf) <- ~long + lat # longitude first
एक स्थानिक संदर्भ प्रणाली (SRS) जोड़ें या संदर्भ प्रणाली (CRS) का समन्वय करें।
# CRS database: http://spatialreference.org/ref/epsg/
sp::proj4string(precGridPtsdf) <- sp::CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")
str(precGridPtsdf)
#> Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
#> ..@ data :'data.frame': 16 obs. of 1 variable:
#> .. ..$ ID: int [1:16] 1 2 3 4 5 6 7 8 9 10 ...
#> ..@ coords.nrs : int [1:2] 3 2
#> ..@ coords : num [1:16, 1:2] -121 -122 -121 -121 -121 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:16] "1" "2" "3" "4" ...
#> .. .. ..$ : chr [1:2] "long" "lat"
#> ..@ bbox : num [1:2, 1:2] -121.5 46.8 -121.2 47
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:2] "long" "lat"
#> .. .. ..$ : chr [1:2] "min" "max"
#> ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
#> .. .. ..@ projargs: chr "+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0"
यूटीएम 10 एन में परिवर्तित करें
utm10n <- "+proj=utm +zone=10 ellps=WGS84"
precGridPtsdf_UTM <- spTransform(precGridPtsdf, CRS(utm10n))
हाइपोथेटिकल वार्षिक वर्षा डेटा पोइसन वितरण का उपयोग करके उत्पन्न।
precDataTxt <- ("ID PRCP2016 PRCP2017 PRCP2018
1 2125 2099 2203
2 2075 2160 2119
3 2170 2153 2180
4 2130 2118 2153
5 2170 2083 2179
6 2109 2008 2107
7 2109 2189 2093
8 2058 2170 2067
9 2154 2119 2139
10 2056 2184 2120
11 2080 2123 2107
12 2110 2150 2175
13 2176 2105 2126
14 2088 2057 2199
15 2032 2029 2100
16 2133 2108 2006"
)
precData <- read_table2(precDataTxt, col_types = cols(ID = "i"))
Prec आकृतिफाइल के साथ मर्ज Prec डेटा फ़्रेम
precGridPtsdf <- merge(precGridPtsdf, precData, by.x = "ID", by.y = "ID")
precdf <- data.frame(precGridPtsdf)
रेन शेपफाइल (UTM) के साथ डेटा फ्रेम मर्ज करें
precGridPtsdf_UTM <- merge(precGridPtsdf_UTM, precData, by.x = "ID", by.y = "ID")
# sample extent
region_extent <- structure(c(612566.169007975, 5185395.70942594, 639349.654465079,
5205871.0782451), .Dim = c(2L, 2L), .Dimnames = list(c("x", "y"
), c("min", "max")))
स्थानिक प्रक्षेप के लिए सीमा को परिभाषित करें। प्रत्येक दिशा पर इसे 4 किमी बड़ा करें
x.range <- c(region_extent[1] - 4000, region_extent[3] + 4000)
y.range <- c(region_extent[2] - 4000, region_extent[4] + 4000)
1 किमी रिज़ॉल्यूशन पर वांछित ग्रिड बनाएं
grd <- expand.grid(x = seq(from = x.range[1], to = x.range[2], by = 1000),
y = seq(from = y.range[1], to = y.range[2], by = 1000))
# Convert grid to spatial object
coordinates(grd) <- ~x + y
# Use the same projection as boundary_UTM
proj4string(grd) <- "+proj=utm +zone=10 ellps=WGS84 +ellps=WGS84"
gridded(grd) <- TRUE
व्युत्क्रम दूरी भारित (IDW) का उपयोग करके इंटरपोल करें
idw <- idw(formula = PRCP2016 ~ 1, locations = precGridPtsdf_UTM, newdata = grd)
#> [inverse distance weighted interpolation]
# Clean up
idw.output = as.data.frame(idw)
names(idw.output)[1:3] <- c("Longitude", "Latitude", "Precipitation")
precdf_UTM <- data.frame(precGridPtsdf_UTM)
प्लॉट प्रक्षेप परिणाम
idwPlt1 <- ggplot() +
geom_tile(data = idw.output, aes(x = Longitude, y = Latitude, fill = Precipitation)) +
geom_point(data = precdf_UTM, aes(x = long, y = lat, size = PRCP2016), shape = 21, colour = "red") +
viridis::scale_fill_viridis() +
scale_size_continuous(name = "") +
theme_bw() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.y = element_text(angle = 90)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))
idwPlt1
### Now looping through every year
list.idw <- colnames(precData)[-1] %>%
set_names() %>%
map(., ~ idw(as.formula(paste(.x, "~ 1")),
locations = precGridPtsdf_UTM, newdata = grd))
#> [inverse distance weighted interpolation]
#> [inverse distance weighted interpolation]
#> [inverse distance weighted interpolation]
idw.output.df = as.data.frame(list.idw) %>% as.tibble()
idw.output.df
#> # A tibble: 1,015 x 12
#> PRCP2016.x PRCP2016.y PRCP2016.var1.pred PRCP2016.var1.var PRCP2017.x
#> * <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 608566. 5181396. 2114. NA 608566.
#> 2 609566. 5181396. 2115. NA 609566.
#> 3 610566. 5181396. 2116. NA 610566.
#> 4 611566. 5181396. 2117. NA 611566.
#> 5 612566. 5181396. 2119. NA 612566.
#> 6 613566. 5181396. 2121. NA 613566.
#> 7 614566. 5181396. 2123. NA 614566.
#> 8 615566. 5181396. 2124. NA 615566.
#> 9 616566. 5181396. 2125. NA 616566.
#> 10 617566. 5181396. 2125. NA 617566.
#> # ... with 1,005 more rows, and 7 more variables: PRCP2017.y <dbl>,
#> # PRCP2017.var1.pred <dbl>, PRCP2017.var1.var <dbl>, PRCP2018.x <dbl>,
#> # PRCP2018.y <dbl>, PRCP2018.var1.pred <dbl>, PRCP2018.var1.var <dbl>