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Looking at the Big Picture: Using Spatial Statistical Analyses to Study Indigenous Settlement Patterns in the North-Western Dominican Republic Cover

Looking at the Big Picture: Using Spatial Statistical Analyses to Study Indigenous Settlement Patterns in the North-Western Dominican Republic

Open Access
|Mar 2023

Figures & Tables

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Figure 1

Archaeological site distribution in the north-western Dominican Republic (map by Eduardo Herrera Malatesta).

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Figure 2

Three topographically modified settlement sites from the survey region. The topography was recorded from drone imagery and a 3D model produced by photogrammetry. Each site is visualized by DEM (50% visibility), over slope (50% visibility), and over a hillshade visualization. Based on a visual analysis the mounds and levelled areas were drawn by hand (models created by Till Sonnemann).

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Figure 3

Stratigraphy of mound units at four sites with alternating layers and lenses of ash and soil. A) El Carril, B) El Flaco, C) El Manantial (photographs A & B by Menno Hoogland, C by Eduardo Herrera Malatesta).

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Figure 4

Results of point process modelling for sites with terrain modifications. Null: The pair correlation function estimated on an assumption of complete spatial randomness. Intensity surface: the predicted intensity surface created from the first-order fit. First-order: the pair correlation function with a critical envelope conditioned on the covariate data as first-order variables. Second-order: incorporating a point interaction term in the first-order model, accounting for all spatial variability.

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Figure 5

Results of point process modelling for sites without terrain modifications. Null: The pair correlation function estimated on an assumption of complete spatial randomness. Intensity surface: the predicted intensity surface created from the first-order fit. First-order: the pair correlation function with a critical envelope conditioned on the covariate data as first-order variables. Second-order: incorporating a point interaction term in the first-order model, accounting for all spatial variability.

Table 1

Fitted covariate datasets for the first-order model, sites with terrain modifications.

COVARIATESESTIMATESTD. ERRORZ VALUESIGNIFICANT
(Intercept)–1.75044504.7434160–36.902629***
Alluvium0,00018726810,000037715344.965303***
Hills and Plateaus–0,00020489700,00004904832–4.177451***
Mountainous areas–0,00013807110,00003149600–4.383766***
Arable soils–0,00011649220,00003183083–3.659729***
Table 2

Fitted covariate datasets for the first-order model, sites without terrain modifications.

COVARIATESESTIMATESTD. ERRORZ VALUESIGNIFICANT
(Intercept)–1.60489302.0523640–78.1972737***
Alluvium0,00013147540,000017911147.3404269***
Hills and Plateaus–0,00013938450,00002166846–6.4325969***
Mountainous areas–0,000060918280,00001397768–4.3582533***
Arable soils–0,000017861110,00003669478–0.4867479***
DOI: https://doi.org/10.5334/jcaa.83 | Journal eISSN: 2514-8362
Language: English
Submitted on: Aug 13, 2021
Accepted on: Mar 2, 2023
Published on: Mar 23, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Eduardo Herrera Malatesta, Jorge Ulloa Hung, Corinne L. Hofman, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.