Best Practices for Big Polygon Data Sets

Some common, useful formats for storing this type of information…

  • CSV
  • GeoJSON/TopoJSON
  • GML
  • KML
  • MySQL/SQLite/SpatiaLite/PostGIS
  • NetCDF
  • SVG
  • VRT
  • ESRI shape file
  • ESRI feature class

It can also be advantageous to abstract data in memory to work with it. I had never heard of GMT or VPF until now but I’m curious about them! A colleague suggested GPKG so I have been looking into that format. I am also reading about the INTERLIS and OGC standards.

I’ve seen advice that database formats are better than file formats for spatial data, and this fits well with another project I’m working on. So, I think I will invest in learning about spatial databases.

Extracting NetCDF Values to a Shape File

Here’s a script that loops over climate NetCDF bricks in a folder and extracts the values for each layer in the brick of each file, in this case averaged over polygons in a shape file.

rm(list = ls())
setwd("where your climate files are")
your_shapefile <- readOGR("path to your shapefile","the layer name for your shapefile")

climate <- rbindlist(lapply(list.files(pattern="nc"), function(climate_file)
#in my case, the weather variable is in the filename
climate_var = ifelse(grepl("pr",climate_file),"pr",ifelse(grepl("tmax",climate_file),"tmax","tmin"))
climate_variable <- brick(climate_file,varname=climate_var,stopIfNotEqualSpaced=FALSE)
#I needed to shift the x-axis to be on a -180 to 180 scale
extent(climate_variable)=c(xmin(climate_variable)-360, xmax(climate_variable)-360, ymin(climate_variable), ymax(climate_variable))
blocks <- spTransform(blocks,projection(climate_variable))
r.vals <- extract(climate_variable, blocks, fun=mean,na.rm=TRUE,df=TRUE,layer=1,nl=1680)
r.vals <- melt(r.vals, id.vars = c("ID"), = "date")
r.vals$climate <- climate_var
#depending on the filename convention of your files, get a variable for your climate model and name it "modelname"
r.vals$model <- modelname
climate_variables[[length(climate_variables)+1]] <- r.vals

climate <- rbindlist(climate_variables)

At this point, you have a data frame called “climate” that has all your data long-form. You can cast this as you see appropriate depending on your needs!

How This Problematic NetCDF File Was Fixed

I can’t be thankful enough for my godsend Twitter friend/NetCDF guru Michael Sumner for coming in clutch to rectify this problematic file! As I take my baby steps in learning how to deal with NetCDF files (~3 weeks after being thrown into the “deep end” and trying to tread water), I realized that one of our files had some sort of problem. Relevant to this issue, NetCDF files are often (not always) multidimensional arrays, where 2 of the dimensions are coordinates. The variables can be defined in relation to the dimensions of the file. So for instance, in my case, temperature is described over space and time (4-dimensional array), and each temperature value corresponds to a location and time. So, each temperature value “goes along with” a set of coordinates and a time. When I imported the NetCDF file as a raster brick (which means I put the array data into a stack of grids), the metadata seemed to show a larger longitudinal “step” than latitudinal, the latter of which was at the correct resolution. Note that this means, as is common, I took what is really a point (a temperature value corresponding to a latitude, longitude and time) and turned it into a grid, where the original point is the center of the resulting cell that gets the value. I tried  plotting it to see what it looked like.

This first plot hints at the real problem: it appears the image has been grabbed at either end and stretched too wide

I tried to crop the image to only the extent I needed but it still was messed up.

I think those are the Great Lakes erroneously stretched into the study area

I took this at face value, that perhaps the grid was somehow “wrong,” but my new friend showed me how to look more deeply into the problem! He opened the file in R and extracted the coordinate values, plotting for a visual diagnosis of the problem.

library(ncdf4) con <- ncdf4::nc_open("")
lon <- ncdf4::ncvar_get(con, "lon")
lat <- ncdf4::ncvar_get(con, "lat")

So, my friend suggested plotting the longitude values stored in this dimension of the array.


Herein we start to see a problem! Something is off about the longitudes stored in the array.

# [1] 244.0625 244.1875 244.3125 244.4375 244.5625 244.6875
# [1] 281.8125 281.9375 282.0625 282.1875 3.5200 3.5100
r <- raster::brick(f, stopIfNotEqualSpaced = FALSE)
# class : Extent
# xmin : 3.056128
# xmax : 282.6414
# ymin : 40
# ymax : 52.875
The first longitude values are the correct lowest values for the raster. For whatever reason, the last 2 longitude values got reassigned to the (very wrong) smallest values in the grid, though they should be the largest values. The extent describes the grid that I assigned the points to when I imported the data into R. For comparison and clarification, I also posted the results of looking at the extent of the raster that I created , to show that they’re “2 different things.” Assuming that the NetCDF stores the center point of each cell, the latitude extents (which are correct) provide a half-cell-width buffer to the actual data point, correctly placing them in the center of the cell.
So to correct, he loaded the raster with the following note…
## let's treat it as if the start is correct,
## the final two columns are incorrect
## we hard-code the half-cell left and right cell edge
## (but note the "centre" might not
## be the right interpretation from this model, if we're being exacting)
valid_lon2 <- c(lon[1] - 0.125/2) + c(0, length(lon)) * 0.125
valid_lat <- range(lat) + c(-1, 1) * 0.125/2
r2 <- setExtent(r, extent(valid_lon2, valid_lat))
r_final <- raster::shift(r2, x = -360)
Now that the extent is rectified he shifted it to our desired reference, namely out of 0-360 longitude to -180/180.
Now the Great Lakes are…great!
Voila! Thank you very much, Michael!

Next NetCDF Issue I Discovered Today

Another problem I ran into: apparently Nov-Dec. 1999 monthly averages for HADCM3 B1 precipitation, where I flipped the axes in NCO, are blank.

problem <- brick("")

The above returns the correct axes, but blank plots. The problem is present before I flipped the axes, so I have to go back to the original files before I did the averaging to find out what’s wrong. I wonder if there were blank layers anywhere in the Nov-Dec daily data?

How I’ve Been Processing Climate Data

From a THREDDS server, I’m using the NetcdfSubset portal to obtain a spatial subset of my climate data set of interest. Since the files I need are too big to download from their HTTP server, they instead give me an OPeNDAP URI with the spatial subset info. I then pass that to nccopy from the NetCDF library.

Then, I installed CDO and wrote a script to do the monthly means for all the files: it averages a daily time step file into a monthly averaged (or whatever metric you choose) file.

cdo monmean

Then, I installed nco to flip the axes.

ncpdq -a lat,lon

Then, I imported the correctly-oriented files into R.

rm(list = ls())
setwd("wherever your files are")
climate <- brick("",varname="whatever your climate variable is")

In my case, as I think is common for climate files, longitude was on a 0-360 scale, instead of -180/180.

Spatially Manipulating NetCDF Files

I ended up getting a bunch of climate NetCDF files from a colleague for each combination of climate model, climate change scenario and variable. So, what I have is a list of 3-D files consisting of observations/predictions of a given weather variable over latitude, longitude and time (you can picture them as cubes, if you like). I will need to spatially adjust the files, and then subset the data.

ncpdq -a lat,lon

I need to…

  • keep only the extent within a shape file we’re using
  • figure out how to summarize it by the polygons in the shape file
    • average?
    • keep only the center point?

Some of my closest colleagues, Brooke Bateman and Andy Allstadt, had some good advice for how to work with the netCDF files in R once I get them:

  • ncdf4 package
  • raster package: can open netCDF either as…
    • single layer (with the raster function)
    • the entire thing (brick function)
  • SDMTools
    • sample x,y points from raster using extract data function
    • convert the raster to ASCII

You can load those libraries, and then do a few things to take a look.

library(<span class="pl-smi">raster</span>)
print(raster(<span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span>))  <span class="pl-c">## same as 'ncdump -h</span>
b <- brick(<span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span>, <span class="pl-v">varname</span> <span class="pl-k">=</span> <span class="pl-s"><span class="pl-pds">"</span>pr<span class="pl-pds">"</span></span>) #this is the variable name internal to the file

My Annoying Climate Download Process

Here’s a summary of my week: I needed mass amounts of data from the NetcdfSubset portal, but it was too much for the HTTP server to handle (they set a cap) with just selecting the products and spatial extent to download. So, instead they returned to me a URI that needed to be passed through an external program, nccopy, to download the data. I wrote a script that separated the URI into separate files by model and scenario, and thus automated the download to save each combination of model, scenario and variable into separate NetCDF files.

The problem became that the download was really slow, owing to traffic here on my work network. Since there was no file size estimate given to me, I assumed maybe the files were huge. So, I did some internal compression to get them to download faster, but at the expense of read access speed for the files. Once I realized it wasn’t the file size, I redid the request for the files without chunking. I then had to kill that request to tailor the data acquisition for our needs, so I finally got all the temperature files today.

Then, I installed CDO and wrote a script to do the monthly means for all the files: it averages a daily time step file into a monthly averaged (or whatever metric you choose) file. I got a list of the base file names as such:

cat climate_structure | cut -d . -f1 > climate_models

Then  I wrote this simple bash script to loop over them all and write out the monthly averages:

while read climate_model; do
 echo "Now averaging $climate_model"
 cdo monmean ${climate_model}.nc ${climate_model}
done < climate_models