<html>Seasons of Movement: Omnidirectional Connectivity Modeling of Indigenous Place-Making in the Great Bay Estuary (<i>P8bagok</i>)</html>
Author(s): Meghan Howey
Year: 2025
Summary
This is an abstract from the "Ancient Pedestrians: Current Research in GIS-Based Movement Modeling for Archaeology" session, at the 90th annual meeting of the Society for American Archaeology.
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Movement has featured in archaeological GIS efforts to analyze social uses of landscape since the start. Important work has focused on Least Cost Paths. However, dividing landscapes into a binary of key sites to be connected and a matrix of land between does not always capture the scope of inter-relationships between people and land, particularly Indigenous place-making practices across total landscapes. Archaeologists can, and should, capitalize on advances in omnidirectional connectivity modeling approaches which allow for the creation of broad-scale seamless cumulative current maps and offer computational efficiency for multiple scenarios/runs to better capture these relationships. Omnidirectional modeling’s flexibility is critical for our case study where flow changes seasonally. The Great Bay Estuary<b> </b>(P8bagok) is a diverse ecosystem in the Northeast, its seasonal fluctuations and abundances stewarded by Pennacook/Abenaki peoples for millennia. Working with their knowledge keepers, we identified key variables for three seasonal scenarios -- frozen season, shoulder season, and warm season -- and engaged in an iterative process of refining inputs and model runs. By co-exploring resulting continuous surface connectivity maps, we assess if our approach captured seasonal flow patterns and, more generally, if this approach has value for archaeologists interested in advancing understandings of Indigenous place-making.
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Cite this Record
Seasons of Movement: Omnidirectional Connectivity Modeling of Indigenous Place-Making in the Great Bay Estuary (P8bagok). Meghan Howey. Presented at The 90th Annual Meeting of the Society for American Archaeology. 2025 ( tDAR id: 509617)
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Abstract Id(s): 52363