xrspatial.kde.line_density#

xrspatial.kde.line_density(x1: ndarray | list, y1: ndarray | list, x2: ndarray | list, y2: ndarray | list, *, weights: ndarray | list | None = None, bandwidth: float | str = 'silverman', kernel: str = 'gaussian', template: DataArray | None = None, x_range: Tuple[float, float] | None = None, y_range: Tuple[float, float] | None = None, width: int = 256, height: int = 256, name: str = 'line_density') → DataArray[source]#

Compute line density from line-segment data.

Each segment is uniformly sampled and the samples are convolved with the chosen kernel, producing a smooth density surface that represents the concentration of linear features.

Parameters:

  • x1 (array-like) – Start and end coordinates of each line segment.

  • y1 (array-like) – Start and end coordinates of each line segment.

  • x2 (array-like) – Start and end coordinates of each line segment.

  • y2 (array-like) – Start and end coordinates of each line segment.

  • weights (array-like, optional) – Per-segment weights. Defaults to uniform weights of 1.

  • bandwidth (float or 'silverman') – Kernel bandwidth. 'silverman' uses an automatic estimate based on all segment endpoints.

  • kernel ({'gaussian', 'epanechnikov', 'quartic'}) – Kernel shape.

  • template (xr.DataArray, optional) – Output grid specification (same as kde()).

  • x_range ((min, max), optional) – Spatial extent.

  • y_range ((min, max), optional) – Spatial extent.

  • width (int) – Grid dimensions.

  • height (int) – Grid dimensions.

  • name (str) – Name of the output DataArray.

Returns:

2-D line-density surface.

Return type:

xr.DataArray