ArticlePloS one2022
Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Settlement heritage on the Coastal Peninsula: an investigation of the spatiotemporal evolution of Shandong Province via GIS.Scientific reports · 2026Article
- Spatiotemporal distribution patterns and influencing factors of marathon events: A case study of Shandong province, China.Scientific reports · 2025Article
- Assessing neighborhoods, wealth differentials, and perceived inequality in preindustrial societies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Analysis of Regional Differences, Dynamic Evolution, and Influencing Factors of Medical Service Levels in Guangzhou Under the Health China Strategy.Risk management and healthcare policy · 2024Article
- Neanderthal coexistence withScience advances · 2023Article
- Mesoamerican urbanism revisited: Environmental change, adaptation, resilience, persistence, and collapse.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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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.
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