xrspatial.focal.hotspots¶
-
xrspatial.focal.hotspots(raster: xarray.core.dataarray.DataArray, kernel: xarray.core.dataarray.DataArray, x: Optional[str] = 'x', y: Optional[str] = 'y') → xarray.core.dataarray.DataArray[source]¶ Identify statistically significant hot spots and cold spots in an input raster. To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. Neighborhood of a feature defined by the input kernel, which currently support a shape of circle, annulus, or custom kernel.
- The result should be a raster with the following 7 values:
90 for 90% confidence high value cluster 95 for 95% confidence high value cluster 99 for 99% confidence high value cluster
-90 for 90% confidence low value cluster -95 for 95% confidence low value cluster -99 for 99% confidence low value cluster
0 for no significance
- raster: xarray.DataArray
2D Input raster image with shape = (height, width).
- kernel: Numpy Array
2D array where values of 1 indicate the kernel.
- xarray.DataArray
2D array of hotspots with values indicating confidence level.
Imports
>>> import numpy as np >>> import xarray as xr >>> from xrspatial import focal
Create Data Array >>> agg = xr.DataArray(np.array([[0, 0, 0, 0, 0, 0, 0], >>> [0, 0, 0, 0, 0, 0, 0], >>> [0, 0, 10, 10, 10, 0, 0], >>> [0, 0, 10, 10, 10, 0, 0], >>> [0, 0, 10, 10, 10, 0, 0], >>> [0, 0, 0, 0, 0, 0, 0], >>> [0, 0, 0, 0, 0, 0, 0]]), >>> dims = [“lat”, “lon”]) >>> height, width = agg.shape >>> _lon = np.linspace(0, width - 1, width) >>> _lat = np.linspace(0, height - 1, height) >>> agg[“lon”] = _lon >>> agg[“lat”] = _lat
Create Kernel
>>> kernel = focal.circle_kernel(1, 1, 1)
Create Hotspot Data Array
>>> focal.hotspots(agg, kernel, x = 'lon', y = 'lat') <xarray.DataArray (lat: 7, lon: 7)> array([[ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 95, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], dtype=int8) Coordinates: * lon (lon) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 * lat (lat) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0