I am desperately trying to use two different rasters for getting data and then run regressions with that. I will first try to present what I want (need) to do and then present the problem(s).
Exercise:
I am trying to detect the relationship between historical temperature abnormalities and GDP. I did it already on country level, but since my results were unsatisfying, I am redoing the analysis with gridded data by using night-light data as proxy for GDP.
I am absolutely new to spatial data analysis and now I am struggling with (probably) an easy solution.
Data:
-Climate ("wilmott" below): https://climatedataguide.ucar.edu/climate-data/global-land-precipitation-and-temperature-willmott-matsuura-university-delaware
-Night-lights:https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html
Code:
Loading both data sources and dividing the climate data brick in individual layers (below I used January 1901 as example, summarizing the layers over the years is the next step).
wilmott <- brick("...air.mon.mean.v501.nc")
ls()
list2env(setNames(unstack(wilmott), names(wilmott)), .GlobalEnv)
ls()
lights <- raster("...F182013.v4/F182013.v4c_web.stable_lights.avg_vis.tif")
Both rasters have the following properties..
wilmott
class : RasterBrick
dimensions : 360, 720, 259200, 1416 (nrow, ncol, ncell, nlayers)
resolution : 0.5, 0.5 (x, y)
extent : 0, 360, -90, 90 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
source : ...air.mon.mean.v501.nc
names : X1900.01.01, X1900.02.01, X1900.03.01, X1900.04.01, X1900.05.01, X1900.06.01, X1900.07.01, X1900.08.01, X1900.09.01, X1900.10.01, X1900.11.01, X1900.12.01, X1901.01.01, X1901.02.01, X1901.03.01, ...
Date/time : 1900-01-01, 2017-12-01 (min, max)
varname : air
lights
class : RasterLayer
dimensions : 16801, 43201, 725820001 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -180.0042, 180.0042, -65.00417, 75.00417 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : ...F182013.v4/F182013.v4c_web.stable_lights.avg_vis.tif
names : F182013.v4c_web.stable_lights.avg_vis
values : 0, 255 (min, max)
and look like this
Plot Temperature Data
Plot Lights Data
As you can see both plots have a different extent. And now comes the problem. If I project (or resample (or playing around with the CRS, extents etc.)) my climate raster, I am able to match their CRS, extents etc. but a lot of data gets thrown away. See in the plot below:
wilmott.new.1901.01 = projectRaster(wilmott1901.01, lights)
> wilmott.new.1901.01
class : RasterLayer
dimensions : 16801, 43201, 725820001 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -180.0042, 180.0042, -65.00417, 75.00417 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : /private/var/folders/dr/4gjhd01x5sd4wf965z0v11200000gp/T/RtmprJJ26D/raster/r_tmp_2021-01-12_164836_576_12591.grd
names : X1901.01.01
values : -56.09999, 35.79999 (min, max)
Plot of climate data projected to light data
How can I solve this? Any ideas? And how would I proceed to retrieve only the values for all grids in both sources in a new data frame? With rastertoPoints()or extract()?
Hope you get what I mean. Thanks a lot!
