This chapter restudies the locational patterns at the La Primavera site. A GIS analysis was performed using free, open-source software. New explanatory variables were included, and an advanced statistical modeling technique, generalized linear modeling (GLM), was applied using the statistical programming tool R. The results revealed that the indigenous people's activities were more spatially stable when they manufactured lithic artifacts in workshops, painted art in caves and rock shelters, constructed chenques for burying the dead and/or as monuments, and stalked prey. Residential activities (base camps and temporary camps) were comparatively less stable. These patterns demonstrate a recurrence of spatial practices throughout the hunter-gatherer dwelling process, indicating its temporal depth. This spatial structuring in the La Primavera locality supports the idea of a consolidated occupation of the locality by high mobile societies. The locational patterns explored in Chaps.  6 and 7 at the La Primavera locality are re-examined in light of new questions arising from the exploratory analysis. This further work contrasts the previous results with more sophisticated multivariate statistics. Previously, the behavior of each archaeological class was examined in relation to the locational variables. Here, we analyze each functional class to construct multiple locational models and determine the best model for each class. We use the previous exploration to determine which variables are relevant to include in the modeling process. We agree with Gillings et al. (2020: 13) that “the application of quantified, formal methods can be used in an exploratory way—not least for developing more nuanced understandings of space and spatiality—and that any results produced are not ‘the answer’ but rather the starting point for a process of interpreting and understanding the past.” Along these lines, we believe that this restudy is important for gaining greater confidence in the results obtained. This new application of other analytical methods, including different variables, scrutinizing the data, and adding controls for specific formation processes allows to a better understanding of locational patterns and how they respond to specific spatially and temporally situated modes of living.