Efficient Geospatial Nearest Neighbor Search with KDTree and xarray

When working with large-scale geospatial data, efficient nearest neighbor search is crucial. This article explores how to combine KDTree and xarray to achieve fast and accurate geospatial nearest neighbor searches, addressing some common challenges.

Problem Background

In fields like GIS, climate science, and remote sensing, we often need to find the nearest data points based on given coordinates (e.g., longitude and latitude). Traditional linear search methods become inefficient with large datasets. xarray provides powerful multi-dimensional data handling, but its built-in sel(method='nearest') has limitations when dealing with geospatial data, as it selects based on coordinate value proximity, not actual geographic distance.

Solution: Combining KDTree and xarray

KDTree is an efficient spatial indexing data structure for fast nearest neighbor lookups. By combining KDTree with xarray, we overcome the limitations of sel(method='nearest') and achieve accurate geospatial nearest neighbor searches.

Implementation Steps

1. Build KDTree: Construct a KDTree using the longitude and latitude coordinates from your xarray dataset. For large geographic extents, consider converting coordinates to a projected coordinate system.
2. Vectorized Queries: Store the longitude and latitude coordinates of all query points in a NumPy array for vectorized queries.
3. Search with KDTree: Use tree.query(query_points, k=1, distance_upper_bound=radius) for the search, where radius is the search radius. k=1 ensures finding the unique nearest neighbor. distance_upper_bound limits the search radius.
4. Process Search Results: KDTree returns flattened index arrays. Use numpy.unravel_index to convert them to original array indices. Check index values to handle cases where no neighbor is found (index value tree.data.shape[0]).
5. Map Indices Back to xarray Dataset: Use xarray.isel to select corresponding data in the xarray dataset based on the indices.

Code Example

import xarray as xr
import numpy as np
from scipy.spatial import KDTree

def find_unique_nearest_neighbors(ds, query_lons, query_lats, radius):
    """
    Find unique nearest neighbors, allowing for cases where no neighbor is found.
    """
    points = np.column_stack((ds['lon'].values.ravel(), ds['lat'].values.ravel()))
    tree = KDTree(points)
    query_points = np.column_stack((query_lons, query_lats))
    distances, indices = tree.query(query_points, k=1, distance_upper_bound=radius)
    results = []
    for i, index in enumerate(indices.flatten()):
        if index == tree.data.shape[0]:
            results.append(None)
        else:
            nearest_indices = np.unravel_index(index, ds['lon'].shape)
            nearest_data = ds.isel(lon=nearest_indices[1], lat=nearest_indices[0])
            results.append(nearest_data)
    return results        

Summary

By integrating KDTree with xarray, we can efficiently perform geospatial nearest neighbor searches while overcoming the limitations of xarray's built-in methods. This approach offers a robust solution for various geospatial data analysis tasks.

However, further exploration is also needed on how to quickly and effectively extract valid points from the data, combine them with invalid points, and return the results in the same order as the input points. In addition, it is important to consider how to extend this approach to high-dimensional data, ensuring scalability and adaptability for more complex geospatial analyses.

References

https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html

https://xarray.pydata.org/en/stable/