Spatial Statistics

Spatial data arise whenever observations are indexed by geographical location. Unlike classical statistics, spatial observations are typically not independent: nearby locations tend to exhibit similar values—a phenomenon known as spatial autocorrelation. Ignoring this structure leads to invalid standard errors, inflated Type I error rates, and inefficient predictions. Spatial statistics provides the formal apparatus for modeling, estimating, and predicting under dependence.

Spatial Autocorrelation

Spatial autocorrelation measures the degree to which nearby observations are similar (positive) or dissimilar (negative).

Moran's I

where are spatial weights and . Under the null hypothesis of no spatial autocorrelation, , and for large , is approximately standard normal after standardization.

Geary's C

Values indicate positive spatial autocorrelation; indicates negative autocorrelation. Unlike Moran's I (which is correlation-like), Geary's C is based on squared differences and is more sensitive to local variability.

Variogram Estimation

The variogram is the fundamental tool for quantifying spatial continuity.

For an intrinsically stationary process (where and ), the semivariogram exists even when the process is non-stationary in the strict sense.

where is the set of pairs within a tolerance window of the lag vector .

Parametric Variogram Models

Kriging

Kriging is the best linear unbiased prediction (BLUP) for spatially correlated data.

Simple Kriging

where is known, is the covariance matrix, and is the covariance between the target and observed locations. The kriging variance is .

Ordinary Kriging

When the mean is unknown but assumed constant, ordinary kriging jointly estimates and :

The weights satisfy (unbiasedness), and the kriging variance is .

Universal Kriging

When the mean is modeled as a spatial trend , universal kriging accounts for this drift:

where is the generalized least squares estimator and is the residual covariance matrix.

Spatial Regression

Point Process Models

For data recorded as event locations in a study region , point process models characterize the stochastic mechanism generating these events.

The inhomogeneous extension allows to vary over space, with .

Quadrat Analysis

VMR = 1 indicates CSR; VMR > 1 suggests clustering; VMR < 1 suggests regularity. The chi-squared statistic tests against the Poisson assumption.

Python Implementation

import numpy as np
import pandas as pd
from scipy import stats
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt

np.random.seed(42)

# --- Generate spatial data with autocorrelation ---
n = 200
coords = np.column_stack([
    np.random.uniform(0, 10, n),
    np.random.uniform(0, 10, n)
])

# True surface with spatial trend
def true_surface(x, y):
    return 3 * np.sin(x / 3) + 2 * np.cos(y / 4) + np.random.normal(0, 0.3, len(x))

z = true_surface(coords[:, 0], coords[:, 1])

# --- Spatial weights matrix (KNN) ---
k = 8
nn = NearestNeighbors(n_neighbors=k + 1).fit(coords)
distances, indices = nn.kneighbors(coords)

W = np.zeros((n, n))
for i in range(n):
    for j_idx in range(1, k + 1):
        j = indices[i, j_idx]
        W[i, j] = 1.0 / max(distances[i, j_idx], 1e-10)

# Row-standardize
row_sums = W.sum(axis=1, keepdims=True)
W = W / row_sums

# --- Moran's I ---
z_centered = z - z.mean()
numerator = n * z_centered @ W @ z_centered
denominator = W.sum() * (z_centered ** 2).sum()
morans_i = numerator / denominator

# Expected value and variance under H0
S0 = W.sum()
S1 = 0.5 * ((W + W.T) ** 2).sum()
S2 = ((W.sum(axis=1) + W.sum(axis=0)) ** 2).sum()
EI = -1.0 / (n - 1)
VI = ((n**2 - 3*n + 3)*S1 - n*S2 + 3*S0) / ((n**2 - 1)*S0) - 1.0/(n-1)**2
z_moran = (morans_i - EI) / np.sqrt(VI)

print(f"Moran's I: {morans_i:.4f}")
print(f"Expected I: {EI:.4f}")
print(f"z-score: {z_moran:.4f}")
print(f"p-value: {2 * (1 - stats.norm.cdf(abs(z_moran))):.6f}")

# --- Empirical Semivariogram ---
def empirical_variogram(coords, z, n_bins=15, max_dist=5.0):
    from scipy.spatial.distance import pdist, squareform
    D = squareform(pdist(coords))
    pairs = np.triu_indices(len(z), k=1)
    dists = D[pairs]
    diffs_sq = (z[pairs[0]] - z[pairs[1]]) ** 2

    bins = np.linspace(0, max_dist, n_bins + 1)
    bin_centers = 0.5 * (bins[:-1] + bins[1:])
    gamma = np.zeros(n_bins)

    for i in range(n_bins):
        mask = (dists >= bins[i]) & (dists < bins[i + 1])
        if mask.sum() > 0:
            gamma[i] = 0.5 * diffs_sq[mask].mean()
        else:
            gamma[i] = np.nan

    return bin_centers, gamma

h, gamma_hat = empirical_variogram(coords, z)
valid = ~np.isnan(gamma_hat)

# Fit exponential variogram via least squares
def exponential_variogram(h, nugget, sill, range_param):
    return nugget + sill * (1 - np.exp(-3 * h / range_param))

from scipy.optimize import curve_fit
popt, pcov = curve_fit(
    exponential_variogram, h[valid], gamma_hat[valid],
    p0=[0.1, 2.0, 2.0], maxfev=10000
)
print(f"\nFitted variogram: nugget={popt[0]:.3f}, sill={popt[1]:.3f}, range={popt[2]:.3f}")

# --- Simple Kriging prediction at a new point ---
def sk_predict(coords, z, new_point, cov_func, mu, **kwargs):
    from scipy.spatial.distance import cdist
    c_0 = cov_func(0, **kwargs)
    C = cov_func(cdist(coords, coords), **kwargs)
    c = cov_func(cdist(new_point, coords), **kwargs)

    C_inv = np.linalg.inv(C + 1e-8 * np.eye(len(C)))
    weights = C_inv @ c
    pred = mu + weights @ (z - mu)
    variance = c_0 - c @ C_inv @ c
    return pred, variance

def cov_exponential(dist, sill, range_param):
    return sill * np.exp(-3 * dist / range_param)

new_pt = np.array([[5.0, 5.0]])
mu_hat = z.mean()
pred, var = sk_predict(coords, z, new_pt, cov_exponential, mu_hat,
                        sill=popt[1], range_param=popt[2])
print(f"\nSimple Kriging prediction at (5,5): {pred[0]:.3f} ± {np.sqrt(max(var, 0)):.3f}")

Summary