Geographic clustering is the tendency for data points or observations to exhibit spatial patterns or groupings based on their geographic locations. This clustering can be observed in various types of data, such as disease outbreaks, economic trends, or environmental variables. Researchers often use spatial statistics and techniques to analyze and model these patterns. Common methods include:
Spatial Autocorrelation: This method assesses the degree of similarity between neighboring geographic locations, indicating whether similar values tend to cluster together or exhibit spatial randomness.
Cluster Analysis: Cluster analysis methods, such as K-means or hierarchical clustering, can be applied to identify spatial clusters of data points with similar characteristics.
Spatial Regression: Spatial regression models extend traditional regression techniques to account for spatial dependencies in the data, allowing for better modeling of geographic clustering effects.