Moran's I: Spatial Autocorrelation Measure

Updated 13 January 2026

Moran's I is a global statistic that measures spatial autocorrelation by comparing attribute similarities across neighboring spatial units.
It employs a spatial weights matrix to compute deviations from the mean, with extensions like Local Moran's I enabling detailed cluster and outlier detection.
The statistic is applied in geographic analysis, imaging, network science, and spatial machine learning, offering insights into spatial clustering and pattern formation.

Moran’s I is the canonical global statistic for quantifying spatial autocorrelation in areal or lattice data, measuring the degree to which similar or dissimilar attribute values co-locate more frequently than expected under spatial randomness. It plays a central role in spatial statistics, geographical analysis, imaging and network science, providing a unifying framework for the analysis of global and local spatial clustering, pattern formation, and segregation.

1. Mathematical Definition and Properties

Classic Moran’s I for a real-valued attribute $x=(x_1,\ldots,x_n)^T$ observed on $n$ spatial units is defined in terms of a spatial weights matrix $W=(w_{ij})$ , typically binary or distance-decay, with $w_{ii}=0$ and $w_{ij}\geq 0$ for $i\neq j$ . The standard formula is: $I = \frac{n}{S_0} \cdot \frac{\sum_{i=1}^n\sum_{j=1}^n w_{ij}(x_i - \bar x)(x_j - \bar x)}{\sum_{i=1}^n(x_i - \bar x)^2}$ where $S_0 = \sum_{i=1}^n\sum_{j=1}^n w_{ij}$ , and $\bar x$ is the mean of $x_i$ .

In matrix notation, with $z=(x-\bar{x}1)/s$ (standardized, $s^2 = \frac{1}{n}\sum_i(x_i-\bar x)^2$ ) and $W$ normalized so $\sum_{i,j} w_{ij}=1$ : $I = z^T W z$ This formulation reveals that Moran’s I is a Rayleigh quotient, and characterizes the spatial autocorrelation as the average product of deviations from the mean for all spatially “close” pairs (Chen, 2016).

Key mathematical properties:

Range: For most practical spatial weights, $I\in(-1,1)$ , but the actual attainable bounds are determined by the spectrum of $W$ projected to the mean-zero space; with pathological $W$ , $I$ can (in theory) exceed these limits (Maruyama, 2015, Chen, 2022).
Expected value: For random (spatially permuted) $x$ , $\mathbb{E}I \approx -1/(n-1)$ (Mason et al., 2024, Pathmanathan et al., 2024).
Interpretation: $I > 0$ implies positive spatial autocorrelation (clusters of similar values); $I < 0$ signals negative autocorrelation (local checkerboarding or high-contrast); $I \approx 0$ is spatial randomness (Chen, 2016).

2. Generalizations and Variants

Local Moran’s I (LISA)

The local version, $I_i$ , assigns to each spatial unit a measure of its association with its neighbors: $I_i = \frac{(x_i-\bar x)}{S^2} \sum_{j} w_{ij} (x_j-\bar x)$ $S^2 = \frac{1}{n} \sum_{k}(x_k-\bar{x})^2$ is a variance estimate.

Local Moran’s I supports decomposition of the global index: $I = \frac{1}{n}\sum_{i=1}^n I_i$ and enables detection of spatial clusters and outliers ("High-High", "Low-Low", "High-Low", "Low-High" regions) (Mason et al., 2024, Klemmer et al., 2020).

Functional and Multivariate Extensions

Recent work extends Moran’s I to bivariate, multivariate, and functional-valued spatial fields:

Bivariate/multivariate functional Moran’s I: For vector/functions $S_i(x)$ at each site, $I$ is defined via the trace of spatially weighted cross-products, with or without centering depending on the expansion basis (Pathmanathan et al., 2024).
Graph-embedded and non-Euclidean domains: On graphs, choices of $W$ (adjacency, Laplacian, Metropolis–Hastings) alter the meaning and attainable range of $I$ , linking it to analysis of variance, Dirichlet energy, or random-walk diffusion (Duchin et al., 2021).
Multi-resolution decomposition: Multi-scale/local–global tensors of $I$ serve as predictors or loss functions in spatial machine learning, using custom coarsenings and adjacency kernels (Klemmer et al., 2020).

3. Spatial Weight Matrices and Theoretical Bounds

The choice of spatial weight matrix $W$ fundamentally determines the technical behavior and interpretability of Moran’s I (Chen, 2016, Maruyama, 2015, Chen, 2022):

For non-pathological $W$ (symmetry, sparsity, zero diagonal), $I$ is bounded by the extremal eigenvalues of the projected $W$ (Rayleigh quotient), typically within $[-1,1]$ .
Pathological configurations (e.g., full connectivity, negative definite $W$ ) can force $I$ to be strictly non-positive or take values outside $[-1,1]$ .
Several authors propose normalized measures (e.g., monotone transformations of $I$ ) that guarantee $I^* \in [-1,1]$ for any $W$ and standardize zero under the null (Maruyama, 2015, Tillé et al., 2017).
The structural decomposition of $I$ via Getis-Ord indices reveals its direct dependence on the pattern of spatial interaction strengths and the system’s “size-correlation” function (Chen, 27 Aug 2025).

4. Statistical Inference, Diagnostics, and Visualization

Significance Testing

Permutation testing is standard: Hold $x$ fixed, permute spatial locations, compute $I^\mathrm{(perm)}$ , and estimate p-values from the null distribution (Mason et al., 2024, Pathmanathan et al., 2024).
Theoretical mean and variance under the null are available for certain $W$ , but large-sample normality is only approximate (Pathmanathan et al., 2024).

Scatterplots and Regression Models

The Moran scatterplot (abscissa: $z_i$ , ordinate: $n(Wz)_i$ ) visualizes spatial lags. Its regression slope provides $I$ ; lines with and without intercept encode global and neighborhood effects (Chen, 2022, Chen, 2016).
Inner/outer product and regression models for $nWz=I z$ validate that $I$ is the leading eigenvalue (or autoregressive coefficient) of the spatial interaction process (Chen, 2022).

Visualization and Interpretation

Recent interactive tools visualize the computation and inferential structure of $I_i$ , spatial lags, and cluster/outlier status, linking datasets, maps, and permutation reference distributions (Mason et al., 2024).

5. Extensions to Dynamic, High-Dimensional, and Information-Theoretic Settings

Spatial Autocorrelation Functions and Scaling

Moran’s I extends to a spatial autocorrelation function $I(r)$ parameterized by pairwise displacement $r$ via stepwise construction of $W(r)$ , analogous to the time-series ACF (Chen, 2020). Partial autocorrelations are obtained via Yule–Walker recursion.
In heavily scale-free/fractal environments (e.g., urban built-up areas), $I(\varepsilon)$ obeys power-law scaling:

$I(\varepsilon) \propto \varepsilon^{-\alpha}, \qquad \alpha = 4 - D_0 - D_2$

where $D_0$ (box-counting) and $D_2$ (correlation) dimensions derive from multifractal analysis. Here, single-valued $I$ loses interpretability across scales and should be replaced by the scaling exponent as an invariant measure (Fu et al., 2023).

Information-Theoretic Interpretation

The observed value $I$ can be converted to a measure of spatial surprisal $J(\alpha) = -\log P(I=\alpha | T_M)$ , formalizing the intuition that high spatial autocorrelation (high $I$ ) indicates low-entropy, highly compressible patterns (Wang et al., 2024). This aligns the spatial statistics tradition with entropy-based anomaly detection and regularization in GeoAI.

6. Applied and Domain-Specific Use Cases

Moran’s I has been adapted for and extensively applied in a wide range of domains:

Matrix ordering for graph visualization: $I$ quantifies pattern coherence in adjacency matrix layouts, outperforming band- or profile-based metrics in distinguishing complex block, off-diagonal, and star patterns (Beusekom et al., 2021).
Medical imaging: $I$ tracks the clustering of high-attenuation lesions in pulmonary CT for sarcoidosis staging, with clear monotonic relationships to histopathological severity and spatial localization (Ryan et al., 2018).
Astrophysics: $I$ reveals the persistence of kinematic substructure in star cluster formation, providing model discrimination between hierarchical and monolithic formation scenarios (Arnold et al., 2022).
Spatial survey sampling: Normalized $I_B$ offers an absolute, interpretable index of sample spatial balance, distinguishing clustered, random, and regularly spaced samples on a fixed $[-1,1]$ scale and robust under unequal inclusion probabilities (Tillé et al., 2017).
Spatial-temporal data science and deep learning: $I$ and its local/multiscale variants serve as explicit or auxiliary losses in neural nets for interpolation, simulation, and generative modeling, enforcing learned spatial context (Klemmer et al., 2020).

7. Comparative Metrics, Limitations, and Theoretical Connections

Moran’s I should be interpreted with respect to:

Alternative indices: Geary’s $C$ offers a squared-difference perspective (with $C^* = 1 - I$ under population normalization), while Getis–Ord $G$ and local $G_i$ are directly linked in a structural decomposition of $I$ (Chen, 2016, Chen, 27 Aug 2025).
Limitations and edge cases:
- $I$ ’s attainable range and interpretability depend on $W$ ; normalization or alternative forms ( $I^*$ , $I_B$ ) are advocated in settings where meaningful comparison is needed (Tillé et al., 2017, Maruyama, 2015).
- Sensitivity to local structure can be limited—Geary’s $C$ or other local indicators may better detect fine-scale heterogeneity (Ryan et al., 2018).
- Observed $I$ is not generally comparable across spatial scales or sampling resolutions unless fractal scaling relations are established (Fu et al., 2023).
Deeper connections: Recent work establishes formal algebraic unification between $I$ and gravity models, spectral graph theory, random walks, and information theory, confirming that $I$ is not just an empirical index but encodes fundamental spatial interaction and diffusion properties of spatial systems (Chen, 27 Aug 2025, Duchin et al., 2021, Wang et al., 2024).