Package: xagg-no-xesmf-deps
xaggA package to aggregate gridded data inxarrayto polygons ingeopandasusing area-weighting from the relative area overlaps between pixels and polygons.InstallationThe easiest way to install the latest version ofxaggis usingconda:conda install -c conda-forge xagg(There may be a version issue withnumba- in which case downgradingnumpyusingpip install numpy==1.21.0should fix the problem)Alternatively, you can usepip, though current dependency issues (esmpyis no longer updated on PyPI) is limitingpipto only installingxagg<=0.1.4:pip install xaggDocumentationSee the latest documentation athttps://xagg.readthedocs.io/en/latest/index.htmlIntroScience 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, okrug, 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.Enterxagg.xaggprovides an easy-to-use (2 lines!), standardized way of aggregating raster data to polygons. All you need is some gridded data in anxarrayDataset or DataArray and some polygon data in ageopandasGeoDataFrame. Both of these are easy to use for the purposes ofxagg- 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')xaggwill then figure out the geographic grid (lat/lon) inds, create polygons for each pixel, and then generate intersects between every polygon in the shapefile and every pixel. For each polygon in the shapefile, 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_dataframe()), 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?).xagghas a built-in support for adding an additional weight grid (anotherxarrayDataArray) intoxagg.pixel_overlaps().Finally,xaggallows for direct exporting of the aggregated data in several commonly used data formats:NetCDFCSV for STATA, RShapefile for QGIS, further spatial processingBest of all,xaggis flexible. Multiple variables in your dataset?xaggwill aggregate them all, as long as they have at leastlat/londimensions. Fields in your shapefile that you'd like to keep?xaggkeeps all fields (for example FIPS codes from county datasets) all the way through the final export. Weird dimension names?xaggis 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.Use CasesClimate econometricsMany 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.xaggallows a simple populationandarea-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 thecookiecutter science project template.