xrspatial.aspect.eastness#

xrspatial.aspect.eastness(agg: DataArray, name: str | None = 'eastness', method: str = 'planar', z_unit: str = 'meter', boundary: str = 'nan') → DataArray[source]#

Computes the east-west component of aspect.

Returns sin(aspect) for each cell, ranging from +1 (due east) to -1 (due west). Flat cells (where aspect() returns -1) are set to NaN.

This is the standard way to encode aspect for use in regression, clustering, and other models that assume linear inputs. Raw aspect in degrees is circular (0 and 360 are the same direction), so feeding it directly into a linear model gives wrong results.

Parameters:

  • agg (xarray.DataArray or xr.Dataset) – 2D elevation raster (NumPy, CuPy, Dask, or Dask+CuPy backed). If a Dataset is passed, the operation is applied to each data variable independently.

  • name (str, default='eastness') – Name of output DataArray.

  • method (str, default='planar') – Passed to aspect(). 'planar' or 'geodesic'.

  • z_unit (str, default='meter') – Passed to aspect(). Only used when method='geodesic'.

  • boundary (str, default='nan') – Passed to aspect(). 'nan', 'nearest', 'reflect', or 'wrap'.

Returns:

eastness_agg – Values in [-1, +1]. NaN where the input has NaN or where the surface is flat.

Return type:

xarray.DataArray or xr.Dataset

References

Stage, A.R. (1976). “An Expression for the Effect of Aspect, Slope, and Habitat Type on Tree Growth.” Forest Science 22(4): 457-460.

Examples

>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial import eastness

>>> data = np.array([
    [1, 1, 1, 1, 1],
    [1, 1, 1, 2, 0],
    [1, 1, 1, 0, 0],
    [4, 4, 9, 2, 4],
    [1, 5, 0, 1, 4],
    [1, 5, 0, 5, 5]
], dtype=np.float32)
>>> raster = xr.DataArray(data, dims=['y', 'x'])
>>> east = eastness(raster)