A new simulation tool for the assessment of subsurface testing: Dig It Check It

Way, Amy Mosig; Tabrett, Amy

doi:10.1016/j.jasrep.2018.06.034

Cited by 3 publications

(9 citation statements)

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“…In the 1980s, the demands of the burgeoning Cultural Resource Management field in the United States along with advances in computing technology enabled several foundational efforts that attempted to quantify the effectiveness of various STP strategies and provide a cost-benefit analysis of different approaches [ 3 , 6 – 9 ]. In spite of immense advances in the speed and power of personal computers in the ensuing decades, we are aware of only a handful of recent attempts [ 10 – 14 ] that use modern computing power in further service of this issue. Whether the dearth of studies in the 1990s and early 2000s is a consequence of the “Post-Processual Critique” or the rise of so-called “full-coverage” survey approach, as Banning [ 15 ] suggests for probability sampling approaches in archaeology more broadly, is unknown.…”

Section: Previous Attempts At Quantifying Survey Methodsmentioning

confidence: 99%

(DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey

2021

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Systematic survey is a crucial component of the archaeological field endeavor. In low visibility areas, systematic subsurface testing is required, most often in the form of shovel test pits or “STPs”. Decisions about the interval between STPs, and the size of such units, impact significantly both the effectiveness of survey for site location and the efficiency of such prospection efforts, and yet “cookie-cutter” survey strategies are often employed without a thorough examination of their costs and benefits. In this work, we present a simulation-based method (DIGSS, Determination of Intervals using Georeferenced Survey Simulation) by which archaeologists can simulate the effectiveness and efficiency of different survey strategies for both prospective and retrospective applications. Beyond permitting the design and implementation of survey strategies that both maximize the possibility of site detection in a given region and that husband precious resources (money and time), this method permits the generation of post hoc correction factors that make direct comparison of previous surveys possible. While DIGSS was designed with archaeological applications (artifacts and sites) in mind, it has potential ramifications in other fields of study where discrete spatial sampling is used as a means of determining the presence, absence, or abundance of discontinuous assemblages materials of interest in a survey region. As such, we can envision potential application in the fields of geology, ecology, and environmental/pollution monitoring.

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Section: Previous Attempts At Quantifying Survey Methodsmentioning

confidence: 99%

(DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey

2021

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“…The general concepts and method for how to estimate a detection rate were developed in the 1980s [1 , 2] but the software developed by Kintigh has for many years been impossible to use with modern computer operating systems. In response to this problem, Way and Tabrett [3] , [4] , [5] recently developed two free simulation tools to help archaeologists design an optimal subsurface sampling strategy for their survey area and check the effectiveness (in terms of detection rate) of any such strategy employed by themselves or others. While the statistical and geometric principles governing the creation of an optimally spaced test-pit grid have been discussed elsewhere [1 , 2 , 4] , here we describe in depth the how the simulation tool Dig It, Design It (DIDI) operates, point the reader to additional user resources available, and explain how several key improvements were incorporated into the programming.…”

Section: Methods Detailsmentioning

confidence: 99%

“…The grid of test units drawn by the archaeologists in the simulation is arranged in a sequence of adjacent equilateral triangles (i.e., a hexagonal grid) because this has been shown to be the most effective for rectangular survey areas [1] . At this point in time the model does not allow any variation to this layout (because the survey area must be square or rectangular), but the effectiveness of this layout for detecting sites in unusually shaped survey areas can be ascertained post hoc using Dig It, Check It [3] . DIDI is programmed to position this hexagonal grid of pits within the survey area, where the pits are spaced just close enough together to intersect a site of the size entered by the user.…”

Section: Design Conceptsmentioning

confidence: 99%

A new Monte Carlo simulation tool for designing an archaeological landscape sampling strategy

Tabrett

Way

2020

MethodsX

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This article describes in detail how the Dig It, Design It (DIDI) simulation tool operates to design a subsurface landscape sampling strategy and predict its likely effectiveness. The purpose of the DIDI model is to help archaeologists develop statistically sound subsurface sampling programs that maximise the number of sites found while minimising the number of sampling units used. It has been unusual for archaeological test-pitting programs to be theoretically tested or statistically justified by simulation prior to implementation. Previous research by Kintigh (1988) and Krakker et al. (1983) established the statistical principles underlying the subsurface sampling of a rectangular survey area, and Kintigh pioneered the use of Monte Carlo simulations to test the effectiveness of these sampling strategies. DIDI provides an updated version of this simulation approach that has three key benefits over the original version: It allows the user to model a larger range of possible archaeological conditions by providing an additional density distribution function (see below), and making the clustering parameter available with all distribution functions; DIDI gives the user the option of filling the previously unavoidable gaps around the edges of the survey area with additional, suitably placed test-units, thereby increasing the detection rate of a sampling strategy; and It is free to download and use and is compatible with modern operating systems.

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“…The data presented here is a NetLogo [2] model which is available online at http://www.digittools.net/ [1] . The model has three entities: survey area, test-pits and sites.…”

Section: Datamentioning

confidence: 99%

“… This data article describes how the Dig It Check It (DICI) model operates. The purpose of the DICI model is to assess the effectiveness of a specified subsurface sampling program in the detection of archaeological sites (Way and Tabrett, in press) [1] . Specifically, the aim of the model is to determine the inherent biases of the specified sampling program, i.e.…”

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confidence: 99%

The Dig It Check It model

Way

Tabrett

2018

Data in Brief

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This data article describes how the Dig It Check It (DICI) model operates. The purpose of the DICI model is to assess the effectiveness of a specified subsurface sampling program in the detection of archaeological sites (Way and Tabrett, in press) [1]. Specifically, the aim of the model is to determine the inherent biases of the specified sampling program, i.e. what is the probability that sites of a certain size, density and density-distribution will be, or were, missed by the sampling program. A simulation is run which randomly places sites, the characteristics of which can be varied between runs, in a sampling area with the specified test-pit layout to determine the probability of overlap between the test-pits and sites with the selected characteristics. When overlap does occur, the site is recorded as intersected. The model then calculates the site density at that point and compares it with the test-pit size to determine whether the density is great enough for detection to also occur.

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A new simulation tool for the assessment of subsurface testing: Dig It Check It

Cited by 3 publications

References 10 publications

(DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey

(DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey

A new Monte Carlo simulation tool for designing an archaeological landscape sampling strategy

The Dig It Check It model

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