LIPIcs.GIScience.2023.71.pdf
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Inferring the History of Spatial Diffusion Processes (Short Paper)
Authors
Takuya Takahashi 
,
Geneviève Hannes ,
Nico Neureiter ,
Peter Ranacher
-
Part of:
Volume:
12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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Author Details
- Department of Geography, University of Zurich, Switzerland
- NCCR Evolving Language, University of Zurich, Switzerland
Acknowledgements
We thank Gereon Kaiping for valuable discussion and ideas in the early phase of the project.
Cite AsGet BibTex
Takuya Takahashi, Geneviève Hannes, Nico Neureiter, and Peter Ranacher. Inferring the History of Spatial Diffusion Processes (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 71:1-71:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.71
https://doi.org/10.4230/LIPIcs.GIScience.2023.71
Abstract
When studying the spatial diffusion of a phenomenon, we often know its geographic distribution at one or more snapshots in time, while the complete history of the diffusion process is unknown. For example, we know when and where the first Indo-European languages arrived in South America and their current distribution. However, we do not know the history of how these languages spread, displacing the indigenous languages from their original habitat. We present a Bayesian model to interpolate the history of a diffusion process between two points in time with known geographical distributions. We apply the model to recover the spread of the Indo-European languages in South America and infer a posterior distribution of possible evolutionary histories of how they expanded their areas since the time of the first invasion by Europeans. Our model is more generally applicable to infer the evolutionary history of geographic diffusion phenomena from incomplete data.
Subject Classification
ACM Subject Classification
- Computing methodologies
Keywords
- Bayesian inference
- geographic diffusion
- language evolution
- Indo-European
- colonisation of the Americas
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References
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