Precipitation Anomalies
Last updated: 2021-03-24
Checks: 5 1
Knit directory:
thesis/analysis/
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1 Loading the data
files = list.files("../data/vector/extraction/", pattern = "ANOM", full.names = T)
data = lapply(files, function(x){
filename = str_split(basename(x), "_")[[1]]
if(length(filename) == 3){
unit = filename[1]
buffer = as.numeric(filename[2])
var = str_remove(filename[3], ".gpkg")
} else {
unit = filename[1]
buffer = 0
var = str_remove(filename[2], ".gpkg")
}
layers = ogrListLayers(x)
layers = layers[grep("attr_", layers)]
data = do.call(cbind, lapply(layers, function(l){
tmp = st_read(x, layer = l, quiet = TRUE)
names(tmp) = l
tmp
}))
data$id = 1:nrow(data)
data %>%
as_tibble() %>%
mutate(unit = unit, buffer = buffer, var = var) %>%
gather("time", "value", -id, -unit, -buffer, -var) %>%
mutate(time = str_remove(time, "attr_"))
})
data = do.call(rbind, data)
str(data)tibble [3,571,200 × 6] (S3: tbl_df/tbl/data.frame)
$ id : int [1:3571200] 1 2 3 4 5 6 7 8 9 10 ...
$ unit : chr [1:3571200] "basins" "basins" "basins" "basins" ...
$ buffer: num [1:3571200] 100 100 100 100 100 100 100 100 100 100 ...
$ var : chr [1:3571200] "AGRANOM" "AGRANOM" "AGRANOM" "AGRANOM" ...
$ time : chr [1:3571200] "2000-01" "2000-01" "2000-01" "2000-01" ...
$ value : num [1:3571200] 0.0252 0 0 0 0 ...2 Missing Values
The resulting dataframe contains roughly 1.8 Mio. rows containing the district-month observations for the two different units of analysis as well as the buffered data. Let’s start looking at the unbuffered data.
data %>%
group_by(unit, buffer) %>%
summarise(N = n(), isna = sum(is.na(value)), isnotna = sum(!is.na(value)), perc = sum(is.na(value)) / n() * 100)# A tibble: 8 x 6
# Groups: unit [2]
unit buffer N isna isnotna perc
<chr> <dbl> <int> <int> <int> <dbl>
1 basins 0 486240 720 485520 0.148
2 basins 50 486240 0 486240 0
3 basins 100 486240 0 486240 0
4 basins 200 486240 0 486240 0
5 states 0 406560 720 405840 0.177
6 states 50 406560 0 406560 0
7 states 100 406560 0 406560 0
8 states 200 406560 0 406560 0 3 Time Series
data %>%
filter(buffer==0) %>%
mutate(time = as.Date(paste0(time, "-01"))) %>%
group_by(time, unit) %>%
summarise(value = mean(value, na.rm = T)) %>%
ggplot() +
geom_line(aes(x=time, y=value, color = unit)) +
theme_classic() +
labs(y="Precipitation [mm]", x = "Time", color = "Unit of Analysis")
4 Seasonal decompositon
data %>%
filter(unit == "basins", buffer == 0) %>%
mutate(time = as.Date(paste0(time, "-01"))) %>%
group_by(time) %>%
summarise(value = mean(value, na.rm = T)) %>%
pull(value) %>%
ts(start = c(2000,1), frequency = 12) %>%
decompose() -> dec_basins
data %>%
filter(unit == "states", buffer == 0) %>%
mutate(time = as.Date(paste0(time, "-01"))) %>%
group_by(time) %>%
summarise(value = mean(value, na.rm = T)) %>%
pull(value) %>%
ts(start = c(2000,1), frequency = 12) %>%
decompose() -> dec_states
dec_data = list(basins = dec_basins, states = dec_states)
dec_data = lapply(c("basins", "states"), function(x){
tmp = dec_data[[x]]
data.frame(type = x,
obsv = as.numeric(tmp$x),
seasonal = as.numeric(tmp$seasonal),
trend = as.numeric(tmp$trend),
random = as.numeric(tmp$random),
date = seq(as.Date("2000-01-01"), as.Date("2019-12-31"), by = "month"))
})
dec_data = do.call(rbind, dec_data)
dec_data %>%
as_tibble() %>%
gather(component, value, -type, -date) %>%
mutate(component = factor(component, levels = c("obsv", "trend", "seasonal", "random"))) %>%
ggplot() +
geom_line(aes(x=date, y=value, color=type)) +
facet_wrap(~component, nrow = 4, scales = "free_y") +
theme_classic() +
labs(y = "Precipitation [mm]", x = "Time", color = "Unit of Analysis") +
theme(legend.position="bottom")
5 Auto-correlation Analysis
data %>%
filter( buffer == 0) %>%
mutate(time = as.Date(paste0(time, "-01"))) %>%
group_by(unit, time) %>%
summarise(value = mean(value, na.rm = T)) %>%
spread(key = unit, value = value) -> acf_data5.1 States
acf(acf_data$states, lag.max = 24)
5.2 Basins
acf(acf_data$basins, lag.max = 24)
6 Spatial Pattern
poly_bas = st_read("../data/vector/basins_mask.gpkg", quiet = T)
crs <- st_crs("EPSG:3857")
poly_bas <- st_transform(poly_bas, crs)
poly_bas <- st_simplify(poly_bas, dTolerance = 1000, preserveTopology = T)
poly_bas$id = 1:nrow(poly_bas)
poly_adm = st_read("../data/vector/states_mask.gpkg", quiet = T)
poly_adm <- st_transform(poly_adm, crs)
poly_adm <- st_simplify(poly_adm, dTolerance = 1500, preserveTopology = T)
poly_adm$id = 1:nrow(poly_adm)
data %>%
filter(buffer == 0) %>%
mutate(time = as.Date(paste0(time, "-01")),
month = month(time)) %>%
group_by(unit, id, month) %>%
summarise(obsv = sum(is.na(value))) -> obs_data
data %>%
filter(buffer == 0) %>%
mutate(time = as.Date(paste0(time, "-01")),
month = month(time)) %>%
group_by(unit, id, month) %>%
summarise(value = mean(value, na.rm = T)) -> sum_data
poly_adm = left_join(poly_adm, filter(sum_data, unit == "states"))
poly_bas = left_join(poly_bas, filter(sum_data, unit == "basins"))
poly_adm = left_join(poly_adm, filter(obs_data, unit == "states"))
poly_bas = left_join(poly_bas, filter(obs_data, unit == "basins"))6.1 Administrative Units by month
6.1.1 Number of missing observations
tm_shape(poly_adm) +
tm_polygons("obsv", palette = "-RdBu", border.col = "white", lwd = .5) +
tm_facets("month")
### Precipitation map
tm_shape(poly_adm) +
tm_polygons(col = "value", palette = "RdYlBu", breaks = c(-Inf,-30,-20,-10,0,10,20,30,+Inf), border.col = "white", lwd = .5) +
tm_facets("month")
6.2 Sub-basin watersheds by month
6.2.1 Number of missing observations
tm_shape(poly_bas) +
tm_polygons("obsv", palette = "-RdBu", border.col = "white", lwd = .5) +
tm_facets("month")
6.2.2 Precipitation map
tm_shape(poly_bas) +
tm_polygons(col = "value", palette = "RdYlBu", breaks = c(-Inf,-30,-20,-10,0,10,20,30,+Inf), border.col = "white", lwd = .5) +
tm_facets("month")
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.7.9.2 rgdal_1.5-18 countrycode_1.2.0 welchADF_0.3.2
[5] rstatix_0.6.0 ggpubr_0.4.0 scales_1.1.1 RColorBrewer_1.1-2
[9] latex2exp_0.4.0 cubelyr_1.0.0 gridExtra_2.3 ggtext_0.1.1
[13] magrittr_2.0.1 tmap_3.2 sf_0.9-7 raster_3.4-5
[17] sp_1.4-4 forcats_0.5.0 stringr_1.4.0 purrr_0.3.4
[21] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6 tidyverse_1.3.0
[25] huwiwidown_0.0.1 kableExtra_1.3.1 knitr_1.31 rmarkdown_2.7.3
[29] bookdown_0.21 ggplot2_3.3.3 dplyr_1.0.2 devtools_2.3.2
[33] usethis_2.0.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.0 workflowr_1.6.2
[4] lwgeom_0.2-5 splines_3.6.3 crosstalk_1.1.0.1
[7] leaflet_2.0.3 digest_0.6.27 htmltools_0.5.1.1
[10] fansi_0.4.2 memoise_1.1.0 openxlsx_4.2.3
[13] remotes_2.2.0 modelr_0.1.8 prettyunits_1.1.1
[16] colorspace_2.0-0 rvest_0.3.6 haven_2.3.1
[19] xfun_0.21 leafem_0.1.3 callr_3.5.1
[22] crayon_1.4.0 jsonlite_1.7.2 lme4_1.1-26
[25] glue_1.4.2 stars_0.4-3 gtable_0.3.0
[28] webshot_0.5.2 car_3.0-10 pkgbuild_1.2.0
[31] abind_1.4-5 DBI_1.1.0 Rcpp_1.0.5
[34] viridisLite_0.3.0 gridtext_0.1.4 units_0.6-7
[37] foreign_0.8-71 htmlwidgets_1.5.3 httr_1.4.2
[40] ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[43] XML_3.99-0.3 dbplyr_2.0.0 utf8_1.1.4
[46] labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.10
[49] later_1.1.0.1 tmaptools_3.1 munsell_0.5.0
[52] cellranger_1.1.0 tools_3.6.3 cli_2.3.0
[55] generics_0.1.0 broom_0.7.2 evaluate_0.14
[58] yaml_2.2.1 processx_3.4.5 leafsync_0.1.0
[61] fs_1.5.0 zip_2.1.1 nlme_3.1-150
[64] xml2_1.3.2 compiler_3.6.3 rstudioapi_0.13
[67] curl_4.3 png_0.1-7 e1071_1.7-4
[70] testthat_3.0.1 ggsignif_0.6.0 reprex_0.3.0
[73] statmod_1.4.35 stringi_1.5.3 highr_0.8
[76] ps_1.5.0 desc_1.2.0 lattice_0.20-41
[79] Matrix_1.2-18 nloptr_1.2.2.2 classInt_0.4-3
[82] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0
[85] data.table_1.13.2 httpuv_1.5.5 R6_2.5.0
[88] promises_1.1.1 KernSmooth_2.23-18 rio_0.5.16
[91] sessioninfo_1.1.1 codetools_0.2-16 dichromat_2.0-0
[94] boot_1.3-25 MASS_7.3-53 assertthat_0.2.1
[97] pkgload_1.1.0 rprojroot_2.0.2 withr_2.4.1
[100] parallel_3.6.3 hms_1.0.0 grid_3.6.3
[103] minqa_1.2.4 class_7.3-17 carData_3.0-4
[106] git2r_0.27.1 base64enc_0.1-3