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Related Experiment Video

Updated: Jul 8, 2026

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

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Ambiguous landscapes: A framework for assessing robustness and uncertainties in archaeological point pattern

Eduardo Herrera Malatesta¹, Sébastien de Valeriola²

¹Centre for Urban Network Evolutions, Aarhus University, Aarhus, Midtjylland Region, Denmark.

|September 24, 2024

Summary

Archaeological spatial analysis using point pattern analysis is impacted by data uncertainty. This study introduces a framework to quantify uncertainty and assess the robustness of spatial statistical models in archaeology.

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Last Updated: Jul 8, 2026

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Area of Science:

Archaeological science
Spatial statistics
Computational archaeology

Background:

Computational methods and spatial statistics, particularly point pattern analysis, have transformed landscape research in archaeology.
Archaeological data often suffer from fragmentation, irregular distribution, and non-systematic survey methods, leading to inherent uncertainties.
Quantifying uncertainty in spatial data from non-systematic surveys remains a significant challenge in the discipline.

Purpose of the Study:

To address the challenge of uncertainty in archaeological spatial data analysis.
To develop a framework for assessing robustness and quantifying uncertainty in spatial statistical models.
To improve the reliability of archaeological landscape narratives derived from computational models.

Main Methods:

Reviewing existing research on uncertainty quantification in archaeology.
Formalizing best practices into a comprehensive assessment framework.
Focusing on the Pair Correlation Function, a common spatial point process method.

Main Results:

The proposed framework enables a better understanding of how incomplete archaeological data influence spatial models.
It provides methods to quantify uncertainties inherent in spatial statistical analyses.
The framework allows for a robust assessment of results obtained from spatial point processes.

Conclusions:

Accurate interpretation of past landscapes requires rigorous assessment of uncertainty in spatial statistical models.
The developed framework offers a systematic approach to evaluating the robustness of archaeological spatial analyses.
This work contributes to more reliable and evidence-based narratives about past human-environment interactions.