A partition-based spatial entropy for co-occurrence analysis with broad application.

Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy (RCE), that detects when categorical co-occurrences happen preferentially in specific environments. We demonstrate its use over a broad range of application fields. As examples, we study brain cell dynamics in Alzheimers Disease, identifying both known and likely novel interactions between immune cells around beta-amyloid plaques. We also investigate the diversity of buildings across a town neighborhoods, to detect potential drivers of social mixing at local scale. Finally, we dissect bird species distribution across a natural reserve, identifying potential vegetation-driven changes in community composition. Altogether, the proposed RCE enables rapid insights into interactions with an environmental component, making it a useful addition to the spatial data science toolbox. Significance StatementSpatial data is rapidly accumulating across fields as diverse as spatial biology, geography, ecology or astrophysics and promises to allow major scientific advances. For example, spatial biology is transforming our ability to study complex tissue organization and disease mechanisms. A key challenge remains the quantification of spatial relationships between individual points, such as cell types, in a statistically rigorous way. We propose a spatial entropy-based measure to quantify context-dependent interactions between categories, such as cell types across tissue subregions. Our novel method deriving from Information Science provides an efficient and versatile way to extract information from spatial datasets, with broad applicability across research fields.