State estimation for aoristic models

vanLieshout, M.N.M.; L., Markwitz, R.

doi:10.1111/sjos.12619

Cited by 2 publications

References 32 publications

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A Bayesian alternative for aoristic analyses in archaeology

Crema

2024

Archaeometry

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Aoristic analysis is often used to handle chronological uncertainties of datasets where scientific dates (e.g., 14C and OSL) are unavailable, and observations are described by association to archaeological periods or phases. Although several advances have been made over the last 2 decades, the basic principle of this approach remains fundamentally the same. Temporal windows of analyses are first divided into regularly sized time blocks, and probability weight is assigned to each of these for every observation. Weights are then aggregated by time block, and the resulting vector of summed probabilities is interpreted as a curve representing changes in the intensity over time of a particular phenomenon. This paper reviews the basic principles and assumptions of aoristic analyses in archaeology, highlighting several issues with its application and interpretation, advocating for a Bayesian alternative implemented via baorista, a new package written in R statistical computing language. The robustness of the proposed solution is evaluated through a series of experiments based on simulated datasets, which showcase key advantages over aoristic analysis. Two specific solutions are considered: a parametric approach where data are fitted to specific growth models and a nonparametric approach that allows for the visualisation of the changing frequencies of events, accounting for sampling error and the peculiarities of archaeological periodisation.

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A Bayesian alternative for aoristic analyses in archaeology

Crema

2024

Archaeometry

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A Likelihood-Based Approach to Developing Effective Proactive Police Methods

L. Markwitz

2024

The UN Sustainable Development Goals and Provision of Security, Responses to Crime and Security Threats, and Fair Criminal Just

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Proactive policing methods are crucial to ensuring safety and security in line with the UN Sustainable Development Goals. This chapter considers aoristic crime data, where an event occurs within a known time interval, but at an unknown time. We introduce a Bayesian likelihood-based approach to estimate occurrence times of property crimes given a known time interval by modelling victim and offender behaviour as stochastic processes. The model can capture non-homogeneous behaviour by both the victim and the offender and underlying factors leading to patterns in crime occurrence times. We test our model on an open-source aoristic crime data set from the USA, comparing our approach to previous approaches. The model determines the most likely occurrence times through parameter estimation methods, finding potential hot spots, and allowing police to adapt proactive policing strategies. This ties in with SDG 16, which involves strengthening institutions and working towards safe and secure societies.

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State estimation for aoristic models

Cited by 2 publications

References 32 publications

A Bayesian alternative for aoristic analyses in archaeology

A Bayesian alternative for aoristic analyses in archaeology

A Likelihood-Based Approach to Developing Effective Proactive Police Methods

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