Ripley's K-function is a widely used spatial summary statistic for assessing clustering in point patterns. However, existing K-based methods can be computationally prohibitive for large-scale data, particularly in high-throughput spatial proteomics, because they rely on spatial information from all points in the image. To address this challenge, we propose a computationally efficient block-based testing framework that extracts disjoint local blocks from an image and aggregates clustering evidence across them. The proposed adaptive spatial blocking algorithm constructs blocks satisfying point-count and shape constraints, enabling scalable spatial clustering inference and fast p-value computation through an asymptotic normal approximation. Numerical studies demonstrate that the proposed method provides a favorable balance between statistical power and computational efficiency. In an application to healthy human intestine spatial proteomics data, our method detects strong spatial aggregation of plasma cells and colocalization between plasma cells and macrophages, while scaling favorably to large images.

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