Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Abstract

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.

Quantitative analyses of wealth inequality at Classic period El Pilar: The Gini index and labor investment

“…Attempts to examine wealth distribution at the intracenter level, such as within the neighborhoods at Chunchucmil (Hutson and Welch 2021) and centers in southern Belize (Thompson et al 2021), also prove difficult at El Pilar. Spatial methods to identify El Pilar neighborhoods have met with limited success (Thompson et al 2022), and labor investment data argue against a concentric-zone model of elite residences clustering near the monumental city center (Horn III et al 2020). An alternative approach taken by Marken (2023) assessed inequality within El Perú-Waka' at the urban core, near periphery, and far periphery, rather than among specific neighborhoods.…”

Neanderthal coexistence with Homo sapiens in Europe was affected by herbivore carrying capacity

“…Despite the uncertainty of this method as a proxy of human population size ( 85 ), it is a valuable tool to identify spatiotemporal patterns in human settlements, as their outputs are similar to heatmaps of archaeological site frequencies ( 86 ). However, whereas simple point density analyses assume that the weight of the point occurs in a single location, KDE spreads the values over an area using a Gaussian distribution ( 87 ). Therefore, KDE produces smoother visualizations of archaeological site frequencies by mapping distribution between core areas (kernels) and surrounding neighborhoods ( 88 ).…”

“…We used a bandwidth of 150 km based on the annual migration distance of extant hunter-gatherers (mean, 158.43 ± 2.95) ( 89 ). The bandwidth should not be interpreted as a yearly mobility radius but as a specific spatial framework that establishes an outer limit to the density estimation ( 87 ). Accordingly, we generated one raster file with the KDE for the NAI and the SAI from between 50 and 30 ka BP on 1 ka time step.…”

Residential size and volume differentiation across urban zones at El Perú-Waka', Peten, Guatemala

“…Although ongoing, this research has highlighted that while Maya cities shared much in common in terms of architectural forms and components, each lowland city was unique in terms of its settlement ecology, occupational history, and overall socioeconomic composition. Recent comparative studies of Maya settlement clustering (Thompson et al 2022), urban scaling (Smith et al 2021), and other lines of evidence, illustrate this variability in urban form and organization across the Lowlands.…”