Modeling Settlement Patterns of the Late Classic Maya Civilization with Bayesian Methods and Geographic Information Systems

Abstract

The ancient Maya occupied tropical lowland Mesoamerica and farmed successfully to support an elaborate settlement pattern that developed over many centuries. There has been debate as to the foundation of the settlement patterns. We show that Maya settlement locations were strongly influenced by environmental factors, primarily topographic slope, soil fertility, and soil drainage properties. Maps of these characteristics were created at the local scale and combined using Bayesian weights-of-evidence methods to develop probabilistic maps of settlement distributions based on the known, but incomplete, distribution of Maya archaeological sites, both domestic and monumental. The predictive model was validated with independently collected point-sampled field data for both presence and absence, predicting 82 percent of undiscovered Maya sites and 94 percent of site absence. This information should be of use in conservation planning for the region, which is under threat from contemporary agricultural expansion. Key Words: Maya settlement, predictive modeling, weights-of-evidence.Los antiguos mayas ocuparon las tierras bajas tropicales de Mesoamérica y produjeron una agricultura tan exitosa como para sostener un elaborado patrón de poblamiento, desarrollado a lo largo de varios siglos. Se ha presentado debate en lo que concierne a la fundación de los patrones de aquel asentamiento. Nosotros mostramos que las locaciones de los asentamientos maya fueron fuertemente influidas por factores ambientales, en especial la pendiente topográfica, la fertilidad del suelo y las propiedades del drenaje de los suelos. Para representar estas características, se crearon mapas a escala local, combinándolos con el uso de métodos bayesianos de peso-de-evidencia a fin de desarrollar mapas probabilísticos de la distribución de los asentamientos. Se tomó en cuenta la conocida aunque incompleta distribución de los sitios arqueológicos maya, tanto domésticos como monumentales. El modelo predictivo fue validado con datos de campo independientemente obtenidos en puntos de muestreo para presencia y ausencia, pronosticando el 82 por ciento de sitios maya sin descubrir y el 94 por ciento de ausencia de sitio. Esta información debería ser utilizada para planificar la conservación de la región, que se halla bajo la amenaza contemporánea de la expansión agrícola. Palabras clave: asentamientos mayas, modelación predictiva, peso-de-evidencia.

Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR

“…Figure 5 shows contrast values, which indicate the relationship between "distance to major roads" and deforestation (that is, the change from current forest to unstocked forest). Contrast is the difference between the positive and negative weights (derived from the "weights of evidence" analysis) that is used to measure the correlation between a particular forest cover change and sampled training points for each variable [40]. Positive contrast values (above zero) indicate that areas within the 10,000 m distance ranges are more suscpetible to deforestation, while negative contrast values (below zero) show that areas over 10,000 m distance ranges are less susceptible to deforestation [35].…”

Modeling the spatial patterns of human wildfire ignition in Yunnan province, China

“…The technique was later extended to determining animal habitats, archaeology, environmental impact evaluation, risk assessment and also used in wildfire prediction (Dickson et al, 2006;Ford, Clarke, & Raines, 2009;Ilia, Tsangaratos, Koumantakis, & Rozos, 2017;Lv, Zheng, Zhao, & Hu, 2013;Raines & Bonham-Carter, 2007;Romero-Calcerrada, Novillo, Millington, & Gomez-Jimenez, 2008, 2010Tahmassebipoor, Rahmati, Noormohamadi, & Lee, 2016;Weed, 2005). The method assumes that an event occurs at a set of known presence points and uses evidence from multiple evidential themes that might cause the event expressed in Fig.…”

Fuzzy Similarity and Fuzzy Inclusion Measures in Polyline Matching: A Case Study of Potential Streams Identification for Archaeological Modelling in GIS

“…The accessibility to the water sources, especially to the surface streams, is one of the most important factors in prediction of ancient settlements locations (Bátora and Tóth, 2014;Ford et al, 2009;Van Leusen et al, 2005;Lieskovský, 2011); the location of the streams themselves is important for archaeohydrological modelling of floods (Arnaud-Fassetta et al, 2010;Gillings, 1995), irrigation (Harrower, 2009;Harrower, 2010), or land-use potential (Bolten et al, 2006). Since current maps and datasets represent water sources in their present form (the vast majority of the streams in Europe were regulated and modified in 20th century), their usage in spatial analysis in archaeology is limited.…”