Researchers established a formal paradigm around spatial interaction as the central mechanism shaping how locations influence one another. This period produced practical estimation schemes for spatial interaction models, clarified that gravity models are a family rather than a single construct, and extended diagnostic tools for detecting spatial dependence in regression residuals. Entropy-based approaches to spatial hierarchy also entered the toolkit, broadening analysis beyond variance decomposition. Historical Significance: The era laid foundational concepts that would underpin later development of Markov random fields and lattice-based methods, influencing ecological and geographic modelling for decades. The shift toward treating spatial interaction as a coherent family of models, together with formalized diagnostics for spatial dependence, established enduring methodological norms and spurred broad adoption across disciplines. These innovations created a lasting bridge between probability theory, statistical inference, and applied geography.