A package to aggregate gridded data in xarray to polygons in geopandas using area-weighting from the relative area overlaps between pixels and polygons.
The easiest way to install the latest version of xagg is using conda or mamba:
conda install -c conda-forge xagg==0.3.2.4
# or
mamba install -c conda-forge xagg==0.3.2.4
We recommend installing xagg in a new environment whenever possible, to ensure all (sub)dependencies are correctly loaded.
Alternatively, you can use pip, though not all optional dependencies are available through pip, meaning that certain features may not be available:
pip install xagg
See the latest documentation at https://xagg.readthedocs.io/en/latest/index.html
Science often happens on grids - gridded weather products, interpolated pollution data, night time lights, remote sensing all approximate the continuous real world for reasons of data resolution, processing time, or ease of calculation.
However, living things don't live on grids, and rarely play, act, or observe data on grids either. Instead, humans tend to work on the county, state, township, Bezirk, or city level; birds tend to fly along complex migratory corridors; and rain- and watersheds follow valleys and mountains.
So, whenever we need to work with both gridded and geographic data products, we need ways of getting them to match up. We may be interested for example what the average temperature over a county is, or the average rainfall rate over a watershed.
Enter xagg.
xagg provides an easy-to-use (2 lines!), standardized way of aggregating raster data to polygons. All you need is some gridded data in an xarray Dataset or DataArray and some polygon data in a geopandas GeoDataFrame. Both of these are easy to use for the purposes of xagg - for example, all you need to use a shapefile is to open it:
import xarray as xr
import geopandas as gpd
# Gridded data file (netcdf/climate data)
ds = xr.open_dataset('file.nc')
# Shapefile
gdf = gpd.open_dataset('file.shp')
xagg will then figure out the geographic grid (lat/lon) in ds, create polygons for each pixel, and then generate intersects between every polygon in the GeoDataFrame and every pixel. For each polygon in the GeoDataFrame, the relative area of each covering pixel is calculated - so, for example, if a polygon (say, a US county) is the size and shape of a grid pixel, but is split halfway between two pixels, the weight for each pixel will be 0.5, and the value of the gridded variables on that polygon will just be the average of both.
Here is a sample code run, using the loaded files from above:
import xagg as xa
# Get overlap between pixels and polygons
weightmap = xa.pixel_overlaps(ds,gdf)
# Aggregate data in [ds] onto polygons
aggregated = xa.aggregate(ds,weightmap)
# aggregated can now be converted into an xarray dataset (using aggregated.to_dataset()),
# or a geopandas geodataframe (using aggregated.to_geodataframe() or aggregated.to_dataframe()
# for a pure pandas result), or directly exported to netcdf, csv, or shp files using
# aggregated.to_csv()/.to_netcdf()/.to_shp()
Researchers often need to weight your data by more than just its relative area overlap with a polygon (for example, do you want to weight pixels with more population more?). xagg has a built-in support for adding an additional weight grid (another xarray DataArray) into xagg.pixel_overlaps().
Finally, xagg allows for direct exporting of the aggregated data in several commonly used data formats:
- NetCDF
- CSV for STATA, R
- Shapefile for QGIS, further spatial processing
Best of all, xagg is flexible. Multiple variables in your dataset? xagg will aggregate them all, as long as they have at least lat/lon dimensions. Fields in your shapefile that you'd like to keep? xagg keeps all attributes/fields (for example FIPS codes from county datasets) all the way through the final export. Weird dimension names? xagg is trained to recognize all versions of "lat", "Latitude", "Y", "nav_lat", "Latitude_1"... etc. that the author has run into over the years of working with climate data; and this list is easily expandable as a keyword argument if needed.
The easiest way to support xagg is to star the repository and spread the word!
Please also consider citing xagg if you use it in your research. The preferred citation can be found at the "Cite this repository" button in the About section on the top right of this page.
xagg, like much of open-source software, is a volunteer-run effort. It means a lot to the developers if you reach out and tell us that you're using our software, how it's helped you, and how it can be improved - it makes the long hours fixing bugs feel that much more worth it. (If you're feeling particularly generous, the lead developer would not say no to additional thanks through contributions to his tea fund through Ko-Fi ;) )
If you have any questions about how to use xagg, please ask them in the GitHub Discussions forum!
If you spot a bug (xagg not working as advertised), please open an issue if it hasn't yet been raised (or comment on an existing one if you see it listed already). To make sure the issue gets solved as quickly as possible:
- Include a minimally reproducible example that triggers the bug
- Include a copy of your environment (for example, the output of
conda list) in which the bug occurred
If you'd like to go the extra mile and help us fix the bug, feel free to contribute a pull request! We ask that any PR:
- Follows a standard development workflow, like this one.
- If fixing a bug, includes unit tests that fail when confronted with the original bug. GitHub Actions are set up to automatically run all tests in
xagg/tests/upon a push.
If there's a feature that you'd like xagg to have, please start a Discussion in the GitHub Discussions forum, or implement it yourself in a pull request.
For more information on contributing in general, the contribution guidelines to the xarray package are a great starting point (not everything will be directly relevant to xagg, but much of this guide is generally relevant!).
Many climate econometrics studies use societal data (mortality, crop yields, etc.) at a political or administrative level (for example, counties) but climate and weather data on grids. Oftentimes, further weighting by population or agricultural density is needed.
Area-weighting of pixels onto polygons ensures that aggregating weather and climate data onto polygons occurs in a robust way. Consider a (somewhat contrived) example: an administrative region is in a relatively flat lowlands, but a pixel that slightly overlaps the polygon primarily covers a wholly different climate (mountainous, desert, etc.). Using a simple mask would weight that pixel the same, though its information is not necessarily relevant to the climate of the region. Population-weighting may not always be sufficient either; consider Los Angeles, which has multiple significantly different climates, all with high densities.
xagg allows a simple population and area-averaging, in addition to export functions that will turn the aggregated data into output easily used in STATA or R for further calculations.
Project based on the cookiecutter science project template.