<p>Spatial omics technologies facilitate comprehensive exploration of tissue microenvironments across development and disease. Yet a theoretical framework for modeling and comparing tissue architecture in diverse biological contexts remains lacking. We introduce N-Orbit, a mathematical model that captures both cell-type composition and spatial relationships within tissue cellular neighborhoods, encoding them as vectors for efficient distance calculations. While not a neighborhood detection method itself, N-Orbit enhances insights gleaned from neighborhoods generated by the plethora of recently developed methods. We benchmark the N-Orbit-based neighborhood distance metric on spatial omics datasets that include ground-truth neighborhoods and clinical outcomes. We demonstrate that N-Orbit outperforms cell-type-enrichment-based metrics in discriminating among neighborhood types, predicting clinical variables, and identifying homologous structures across species. Additionally, N-Orbit enhances model interpretability by tracing neighborhoods back to enriched spatial motifs. N-Orbit holds significant potential for deepening our understanding of how tissue microenvironments remodel during development, disease, and evolution.</p>