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Designing a Sample of Cores to Estimate the Number of Features at a Site

Published online by Cambridge University Press:  16 January 2017

Paul Welch*
Affiliation:
Department of Anthropology, Southern Illinois University, Carbondale, 1000 Faner Drive, Carbondale, IL 62901 (pwelch@siu.edu)

Abstract

Previous literature dealing with designing samples of points on a grid focuses on finding all the targets in a sample area, as would be the case when shovel-testing to discover features or sites within an impact zone. That goal will be achieved most efficiently using offset-square or hexagonal grid patterns. However, if the goal is to estimate the number of features based on a probability sample, the optimum design is actually a square grid with a grid interval greater than the target diameter. This surprising but welcome result is due to the interaction of several nonlinear relationships between the grid interval, the target size, the number of sample points, and the probability of intersecting a feature, combined with the fact that square grids can avoid edge-effect biases more efficiently than the other designs. The square also requires the lowest total travel time. Substantial additional cost-efficiency can be gained by using a cluster design with at least five clusters.

La literatura anterior, relacionada con el diseño de muestras de puntos en una retícula, se centra en localizar todos los objetivos en un área muestreada, como es el caso de pozos de sondeo para descubrir elementos o sitios dentro de una zona de impacto. Esa meta se lograría de manera eficiente usando retículas en forma de triángulos isósceles y equiláteros. Sin embargo, si el objetivo es estimar el número de elementos basados en una muestra probabilística, el diseño óptimo es en realidad una retícula cuadrada con un intervalo mayor que el diámetro del objetivo. Este resultado sorprendente, pero esperado, se debe a la interacción de varias relaciones no lineales entre el intervalo de la cuadrícula, el tamaño del objetivo, el número de puntos muestreados, y la probabilidad de intersectar un elemento, combinado con el hecho de que las retículas cuadradas pueden evitar los efectos de los sesgos en los márgenes más eficientemente que otro tipo de diseños. El cuadrado también requiere el menor tiempo de recorrido. La eficiencia de los costos se puede añadir sustancialmente al utilizar un diseño agrupado con cinco grupos por lo menos.

Type
Research Article
Copyright
Copyright © Society for American Archaeology 2013

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References

References Cited

Banning, E. B. 2002 Archaeological Survey. Kluwer Academic/Plenum, New York.Google Scholar
Banning, E. B., Hawkins, A. L., and Stewart, S. T. 2006 Detection Functions for Archaeological Survey. American Antiquity 71:723742.Google Scholar
Barry, Jon, and Nicholson, Mike D. 1993 Measuring the Probability of Patch Detection for Four Spatial Sampling Designs. Journal of Applied Statistics 20:353362.Google Scholar
Bart, Jonathan, Fligner, Michael A., and Notz, William I. 1998 Sampling and Statistical Methods for Behavioral Ecologists. Cambridge University Press, New York.Google Scholar
Bellhouse, David R. 1981 Area Estimation by Point-Counting Techniques. Biometrics 37:303312.Google Scholar
Brennan, Tamira K. 2007 In-Ground Evidence of Above-Ground Architecture at Kincaid Mounds. In Architectural Variability in the Southeast, edited by Lacquement, Cameron H., pp. 73100. University of Alabama Press, Tuscaloosa.Google Scholar
Butler, Brian M., Berle Clay, R., Hargrave, Michael L., Peterson, Staffan D., Schwegman, John E., Schwegman, John A., and Welch, Paul D. 2011 A New Look at Kincaid: Magnetic Survey of a Large Mississippian Town. Southeastern Archaeology 30:2037.Google Scholar
Cochran, William G. 1978 Sampling Techniques. 3rd ed. Wiley, New York.Google Scholar
Cole, Fay-Cooper, Bell, Robert, Bennett, John, Caldwell, Joseph, Emerson, Norman, MacNeish, Richard, Orr, Kenneth, and Willis, Roger 1951 Kincaid: A Prehistoric Illinois Metropolis. University of Chicago Press, Chicago.Google Scholar
CRC Press 1975 CRC Standard Mathematical Tables. 23rd ed. CRC Press, Cleveland, Ohio.Google Scholar
Drennan, Robert D. 1996 Statistics for Archaeologists: A Commonsense Approach. Plenum, New York.Google Scholar
Gregoire, Timothy G., and Scott, Charles T. 2003 Altered Selection Probabilities Caused by Avoiding the Edge in Field Surveys. Journal of Agricultural, Biological, and Environmental Statistics 8:3647.Google Scholar
Kintigh, Keith 1988 Monte Carlo Evaluation of the Effectiveness of Subsurface Testing for the Identification of Archaeological Sites. American Antiquity 53:686707.Google Scholar
Krakker, James J., Shott, Michael J., and Welch, Paul D. 1983 Design and Evaluation of Shovel-Test Sampling in Regional Archaeological Survey. Journal of Field Archaeology 10:469480.Google Scholar
Lovis, William A. 1976 Quarter Section and Forests: An Example of Probability Sampling in the Northeast Woodlands. American Antiquity 41:364372.Google Scholar
MacManamon, Francis P. 1984 Discovering Sites Unseen. In Advances in Archaeological Method and Theory, vol. 7, edited by Schiffer, Michael B., pp. 223292. Academic Press, New York.Google Scholar
Matérn, Bertil 1986 Spatial Variation. 2nd ed. Springer, New York.Google Scholar
Müller, Werner G. 2001 Collecting Spatial Data: Optimum Design of Experiments for Random Fields. 2nd ed. Springer-Verlag, New York.Google Scholar
Murthy, Mankal N, and Rao, Talluri J. 1988 Systematic Sampling and Illustrative Examples. In Sampling, edited by Krishnaiah, Paruchuri R. and Radhakrishna Rao, C., pp. 147186. Handbook of Statistics, vol. 6, Elsevier, New York.Google Scholar
Nicholson, Mike, and Barry, Jon 1996 Survey Design for Detecting Patches. Journal of Applied Statistics 23:361368.Google Scholar
Orton, Clive 2000 Sampling in Archaeology. Cambridge University Press, New York.Google Scholar
Schreuder, Hans T., Gregoire, Timothy G., and Wood, Geoffrey B. 1993 Sampling Methods for Multiresource Forest Inventory. Wiley, New York.Google Scholar
Shott, Michael J. 1985 Shovel-Test Sampling as a Site Discovery Technique: A Case Study from Michigan. Journal of Field Archaeology 12:457468.Google Scholar
Stehman, Steven V., and Scott Overton, W. 2002 Environmental Sampling. In Encyclopedia of Environmetrics, vol. 4, edited by El-Shaarawi, A. H. and Piegorsch, W. W., pp. 19141937. Wiley, Chichester, UK.Google Scholar
Stevens, Don L. Jr. 2001 Edge Effect in Determining Means and Totals for Spatial Environmental Variables. In Encyclopedia of Environmetrics, vol. 2, edited by El-Shaarawi, A. H. and Piegorsch, W. W., pp. 624629. Wiley, Chichester, UK.Google Scholar
Thompson, Steven K. 2002 Sampling. 2nd ed. Wiley, New York.Google Scholar