Point pattern analysis on spatially aggregated data

The paper operates within the established paradigm of spatial statistics and point pattern analysis. This paradigm seeks to identify and quantify non-random spatial patterns (clustered, dispersed, or random) in geographic data. Its standard tools, such as Ripley's K-function and nearest-neighbor analysis, traditionally rely on precise locational data for individual points.

This preprint is classified as Normal Science because it does not challenge the fundamental principles of point pattern analysis. Instead, it identifies a specific "puzzle" that the current paradigm's tools cannot adequately solve: the analysis of spatially aggregated data where exact point locations are unknown. The authors propose new statistical methods to extend the problem-solving capacity of the existing paradigm to this specific data type. This work represents an incremental advancement, refining and articulating the paradigm by developing new instrumentation to address a recognized limitation, which is a hallmark of normal scientific research.