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SPATIAL

cKDTree

cKDTree(data, leafsize=16, compact_nodes=True, copy_data=False,.

Example of usage

Python API reference included; executable data demonstration pending review.

Usage / limitations

+Use when: Analyse spatial geometry.

−Limitations: Check input requirements, mathematical assumptions and version-specific behaviour before using.

Mathematical formula

See method documentation: implementation and assumptions vary.
Sample dataShow 
{
  "variable": "site_locations",
  "domain": "Urban logistics and distribution-site planning",
  "feature_names": [
    "east_km",
    "north_km"
  ],
  "records": [
    {
      "site": "Depot A",
      "east_km": 0,
      "north_km": 0
    },
    {
      "site": "Depot B",
      "east_km": 2,
      "north_km": 0
    },
    {
      "site": "Depot C",
      "east_km": 0,
      "north_km": 2
    },
    {
      "site": "Depot D",
      "east_km": 2,
      "north_km": 2
    },
    {
      "site": "Depot E",
      "east_km": 1,
      "north_km": 1
    },
    {
      "site": "Depot F",
      "east_km": 3,
      "north_km": 1
    }
  ]
}

Code example (Python)

# DataLens.ai example — cKDTree
# Domain: urban logistics and distribution-site planning.
# Sample input is provided by the Copy input + code + test button.
import numpy as np
from scipy import spatial
sites = np.array([[r["east_km"], r["north_km"]] for r in site_locations["records"]], dtype=float)

# Build a fast spatial index and find the closest depot to a delivery point.
tree = spatial.cKDTree(sites)
distance, index = tree.query([0.2, 0.1])
assert index == 0 and np.isclose(distance, np.hypot(0.2, 0.1))

print("Expected output verified")