Spatial Outlier Detection Algorithm Implementation Using MATLAB

Spatial outlier detection is a technique for identifying anomalies in spatial data, widely applied in geographic information systems (GIS), environmental monitoring, urban planning, and other fields. MATLAB provides powerful numerical computation and visualization capabilities, enabling efficient implementation and analysis of spatial outlier detection.

### Core Concepts Spatial Autocorrelation Analysis: Detects whether observations at specific locations significantly differ from values in their neighboring areas. Common methods include Local Moran's I and Getis-Ord Gi* statistics. Distance Weight Matrix: Defines spatial neighborhood relationships (e.g., Euclidean distance, K-nearest neighbors) to calculate weight matrices that measure spatial influence between locations. Statistical Testing: Performs hypothesis testing on calculated statistics to determine outlier significance, typically using Z-Score or P-value thresholds for filtering.

### MATLAB Implementation Key Points Spatial Data Preprocessing: Ensure data format suitability for analysis, such as converting latitude-longitude coordinates to projected coordinates using mapping toolbox functions. Neighborhood Definition: Utilize MATLAB functions like `pdist2` for pairwise distance calculations or `rangsearch` for nearest neighbor searches to construct adjacency matrices. Statistical Computation: Implement spatial statistics through custom functions or specialized toolboxes like the Spatial Statistics Toolbox, which provides built-in functions for spatial autocorrelation measures. Visualization: Employ `geoscatter` for geographic scatter plots or `mapshow` for map-based displays to highlight detected outliers, enhancing result interpretability through color coding and marker differentiation.

This algorithm helps identify environmental pollution sources, traffic anomalies, and other spatial irregularities. Combined with MATLAB's computational efficiency, it can rapidly process large-scale spatial datasets with optimized memory management and parallel processing capabilities.