State Memory, Change Memory and the Persistence Ceiling: Re-Examining Territorial Memory in Markov–Cellular Automata Models of Land-Use Allocation

Markov chains remain the default engine for land-use allocation. This paper argues for integrating territorial memory into the process. That is, an explicit representation of the persistence of land-use states. We subject that proposal to an unfavourable test. Using the five-epoch Agriculture and Agri-Food Canada (AAFC) Land Use series (2000–2020), harmonised to an epoch-stable legend, we compare five nested models across 260 Ontario population centres under a strict temporal hold-out in which every model receives identical class totals, isolating allocation skill. Three results follow. First, conventional validation is uninformative: because these settlements change only ~0.2% of cells per five-year interval, a null model predicting no change attains 99.8% accuracy and outscores every fitted model, so reported accuracies above 0.95 measure landscape inertia. Second, a register built from previous states is analytically and empirically near-inert: the previous state equals the current state for 98.9% of cells, so it merely reinforces an already-dominant transition diagonal and changes essentially no predictions. Third, allocation skill comes from spatial neighbourhood structure (0.00 to 0.03); re-specifying memory over conversions rather than states adds a small forward-fold gain that does not survive fold reversal, so we present it as suggestive rather than established; a sensitivity analysis ties the reversal to over-strong calibration of the momentum weight rather than to the change-memory field itself. We also show that the AAFC v5 legend, when used raw, inflates the 2010–2015 change twenty-fold. We conclude that territorial memory should be re-specified as memory of change, that accuracy and kappa cannot validate models on persistent landscapes, and that, once a neighbourhood term is present, errors in the projected quantity of change cost more skill than further refinement of the allocation rule in urban and peri-urban environments.