Some Archaeological Applications of Kernel Density Estimates

Baxter, MJ; Beardah, C. C.; Wright, Richard

doi:10.1006/jasc.1996.0119

Cited by 160 publications

(106 citation statements)

References 9 publications

(6 reference statements)

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“…Finally, an outer border of dense site distribution was determined using the criterion of the maximal increase of space (Zimmermann et al 2004: 53-54). The alternative is to use kernel density estimation (Baxter et al 1974). The problem here is to derive a reliable bandwidth and to obtain a reasonable outer border.…”

Section: Methodological Advancesmentioning

confidence: 99%

Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800

2009

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We describe a combination of methods applied to obtain reliable estimations of population density using archaeological data. The combination is based on a hierarchical model of scale levels. The necessary data and methods used to obtain the results are chosen so as to define transfer functions from one scale level to another. We apply our method to data sets from western Germany that cover early Neolithic, Iron Age, Roman, and Merovingian times as well as historical data from AD 1800. Error margins and natural and historical variability are discussed. Our results for nonstate societies are always lower than conventional estimations compiled from the literature, and we discuss the reasons for this finding. At the end, we compare the calculated local and global population densities with other estimations from different parts of the world. Keywordssettlement archaeology, population density, site density, Bandkeramik settlements, LBK culture, Aldenhovener Platte, Rhineland, Germany, Neolithic. Cover Page FootnoteWe thank Irmela Herzog (LVR-Bodendenkmalpfl ege Rheinland, Bonn) for her kernel density estimation computer program; Irmela Herzog and Thomas Frank (Universität zu Köln) for the preparation of the actual Bandkeramik data set from the lower Rhine basin; and Niels Müller-Scheessel (Römisch-GermanischeKommission, Frankfurt am Main) for the isoline circumscribing the Hallstatt necropolises of southern Germany. The remarks of the referees were very useful for improving the text. And last but not least, we are grateful to the German Research Foundation (DFG) for funding the archaeological project of the Rhine-LUCIFS framework.This open access article is available in Human Biology: http://digitalcommons.wayne.edu/humbiol/vol81/iss2/13 Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800Andreas Zimmermann, 1 Johanna Hilpert, 1 and Karl Peter Wendt 1 Abstract We describe a combination of methods applied to obtain reliable estimations of population density using archaeological data. The combination is based on a hierarchical model of scale levels. The necessary data and methods used to obtain the results are chosen so as to defi ne transfer functions from one scale level to another. We apply our method to data sets from western Germany that cover early Neolithic, Iron Age, Roman, and Merovingian times as well as historical data from AD 1800. Error margins and natural and historical variability are discussed. Our results for nonstate societies are always lower than conventional estimations compiled from the literature, and we discuss the reasons for this fi nding. At the end, we compare the calculated local and global population densities with other estimations from different parts of the world.

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Section: Methodological Advancesmentioning

confidence: 99%

Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800

2009

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“…1B) was performed on each row of C in equation 2, C i , by first calculating a histogram, using 100 equally spaced bins, of scalp voltages from that channel. This histogram was then convolved (smoothed) with a Gaussian function (Baxter et al 1997) whose standard deviation was, 1.0592* n* s 71/5 where n was the number of time points in the epoch, and s the standard deviation of the values recorded from the i th channel during the epoch, i.e. C i (Fig.…”

Section: Experimental Methodsmentioning

confidence: 99%

A new method for detecting state changes in the EEG: exploratory application to sleep data

McKeown

Humphries

Achermann

et al. 1998

Journal of Sleep Research

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A new statistical method is described for detecting state changes in the electroencephalogram (EEG), based on the ongoing relationships between electrode voltages at different scalp locations. An EEG sleep recording from one NREM-REM sleep cycle from a healthy subject was used for exploratory analysis. A dimensionless function defined at discrete times t i , u(t i ), was calculated by determining the log-likelihood of observing all scalp electrode voltages under the assumption that the data can be modeled by linear combinations of stationary relationships between derivations. The u(t i ), calculated by using independent component analysis, provided a sensitive, but non-specific measure of changes in the global pattern of the EEG. In stage 2, abrupt increases in u(t i ) corresponded to sleep spindles. In stages 3 and 4, low frequency (& 0.6 Hz) oscillations occurred in u(t i ) which may correspond to slow oscillations described in cellular recordings and the EEG of sleeping cats. In stage 4 sleep, additional irregular very low frequency (& 0.05±0.2 Hz) oscillations were observed in u(t i ) consistent with possible cyclic changes in cerebral blood flow or changes of vigilance and muscle tone. These preliminary results suggest that the new method can detect subtle changes in the overall pattern of the EEG without the necessity of making tenuous assumptions about stationarity.

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“…The majority are focused on either spatial versions of well-known summary statistics (mean centre of distribution, standard distance; Mitchell, 2005) or the assessment of first order properties (kernel density estimates; Baxter et al, 1997). There are a number of alternative tools aimed at assessing second order properties, and capable of integrating weighted values such as weighted K functions (Getis, 1984) and pair correlation functions (Gavrikov and Stoyan, 1995).…”

Section: An Uncertainty-weighting Approachmentioning

confidence: 99%

A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record

Crema

Bevan

Lake

2010

Journal of Archaeological Science

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a b s t r a c tThe assessment of spatial patterns in archaeology is hampered by a number of constraints, one of the most serious of which is the intrinsic temporal uncertainty associated with most of the archaeological record. Different types of chronological definition or different degrees of temporal knowledge will suggest different kinds of spatial pattern, ultimately obscuring and restricting our interpretation of the background process, especially in cases where we are seeking a diachronic perspective. This paper addresses these problems by adopting both a probabilistic approach and a more standardised framework for diachronic analysis. First, we define the notion of temporal uncertainty and explore its analytical consequences. Second, we consider two methods by which it might be formally quantified, emphasising (a) the advantages of probability-weighted spatial analysis and (b) the comparison of alternative spatiotemporal patterns via Monte-Carlo simulation. Finally, we apply these methods to a case study that considers the distribution of Middle to Late Jomon (ca.5000-3000 BP) pithouses recorded in the Chiba New Town area of Japan.

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Some Archaeological Applications of Kernel Density Estimates

Cited by 160 publications

References 9 publications

Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800

Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800

A new method for detecting state changes in the EEG: exploratory application to sleep data

A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record

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