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Carleton WC, Klassen S, Niles-Weed J, Evans D, Roberts P, Groucutt HS. Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor. Sci Rep 2023; 13:17913. [PMID: 37864037 PMCID: PMC10589302 DOI: 10.1038/s41598-023-44875-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/12/2023] [Indexed: 10/22/2023] Open
Abstract
Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modification at previously un-imagined scales. In the tropics, this has enabled documentation of previously archaeologically unrecorded cities in various tropical regions, igniting scientific and popular interest in ancient tropical urbanism. An emerging challenge, however, is to add temporal depth to this torrent of new spatial data because traditional archaeological investigations are time consuming and inherently destructive. So far, we are aware of only one attempt to apply statistics and machine learning to remotely-sensed data in order to add time-depth to spatial data. Using temples at the well-known massive urban complex of Angkor in Cambodia as a case study, a predictive model was developed combining standard regression with novel machine learning methods to estimate temple foundation dates for undated Angkorian temples identified with remote sensing, including lidar. The model's predictions were used to produce an historical population curve for Angkor and study urban expansion at this important ancient tropical urban centre. The approach, however, has certain limitations. Importantly, its handling of uncertainties leaves room for improvement, and like many machine learning approaches it is opaque regarding which predictor variables are most relevant. Here we describe a new study in which we investigated an alternative Bayesian regression approach applied to the same case study. We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties-especially chronological-into modelled temporal estimates.
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Affiliation(s)
- W Christopher Carleton
- Extreme Events Research Group, Max Planck Institutes of/for, Geoanthropology, Chemcial Ecology, and Biogeochemistry, Jena, Germany.
| | - Sarah Klassen
- Department of Anthropology, University of Toronto, Toronto, Canada
| | - Jonathan Niles-Weed
- Courant Institute of Mathematical Sciences and Center for Data Science, New York University, New York, USA
| | | | - Patrick Roberts
- isoTROPIC Research Group, Max Planck Institute of Geoanthropology, Jena, Germany
- Department of Archaeology, Max Planck Institute of Geoanthropology, Jena, Germany
| | - Huw S Groucutt
- Extreme Events Research Group, Max Planck Institutes of/for, Geoanthropology, Chemcial Ecology, and Biogeochemistry, Jena, Germany
- Department of Archaeology, Max Planck Institute of Geoanthropology, Jena, Germany
- Department of Classics and Archaeology, University of Malta, Msida, Malta
- Institute of Prehistoric Archaeology, University of Cologne, Cologne, Germany
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Affiliation(s)
- Jonathan Niles-Weed
- Courant Institute of Mathematical Sciences & Center for Data Science, New York University, 251 Mercer Street, New York, NY 10012-1185, USA
| | - Philippe Rigollet
- Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139-4307, USA
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Niles-Weed J, Berthet Q. Minimax estimation of smooth densities in Wasserstein distance. Ann Stat 2022. [DOI: 10.1214/21-aos2161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Klassen S, Carter AK, Evans DH, Ortman S, Stark MT, Loyless AA, Polkinghorne M, Heng P, Hill M, Wijker P, Niles-Weed J, Marriner GP, Pottier C, Fletcher RJ. Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex. Sci Adv 2021; 7:eabf8441. [PMID: 33962951 PMCID: PMC8104873 DOI: 10.1126/sciadv.abf8441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/22/2021] [Indexed: 06/12/2023]
Abstract
Angkor is one of the world's largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehensive demographic study has been completed, and key aspects of its population and demographic history remain unknown. Here, we combine lidar, archaeological excavation data, radiocarbon dates, and machine learning algorithms to create maps that model the development of the city and its population growth through time. We conclude that the Greater Angkor Region was home to approximately 700,000 to 900,000 inhabitants at its apogee in the 13th century CE. This granular, diachronic, paleodemographic model of the Angkor complex can be applied to any ancient civilization.
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Affiliation(s)
- Sarah Klassen
- Department of Anthropology, University of British Columbia, Vancouver, BC, Canada.
- University of Oregon, 1585 E 13th Avenue, Eugene, OR 97403, USA
- Leiden University, Rapenburg 70, 2311 EZ Leiden, Netherlands
| | - Alison K Carter
- Department of Anthropology, University of Oregon, 308 Condon Hall, 1321 Kincaid Street, Eugene, OR 97403 USA
| | - Damian H Evans
- École française d'Extrême-Orient, 22 Avenue du Président Wilson, 75116 Paris, France
| | - Scott Ortman
- Institute of Behavioral Science, University of Colorado, Boulder, CO 80309, USA
| | - Miriam T Stark
- Department of Anthropology, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Alyssa A Loyless
- Department of Anthropology, Stanford University, Palo Alto, CA 94305, USA
| | | | - Piphal Heng
- Center for Southeast Asian Studies and Department of Anthropology, Northern Illinois University, DeKalb, IL 60115, USA
| | | | - Pelle Wijker
- École française d'Extrême-Orient, 22 Avenue du Président Wilson, 75116 Paris, France
| | - Jonathan Niles-Weed
- Courant Institute of Mathematical Sciences and Center for Data Science, New York University, New York, NY 10012, USA
| | - Gary P Marriner
- Casey & Lowe Archaeology and Heritage, Sydney, NSW 2040, Australia
| | - Christophe Pottier
- École française d'Extrême-Orient, 22 Avenue du Président Wilson, 75116 Paris, France
| | - Roland J Fletcher
- Department of Archaeology, University of Sydney, NSW 2006, Australia
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