• Oliver Nakoinz
  • Daniel Knitter
Part of the Quantitative Archaeology and Archaeological Modelling book series (QAAM)


This chapter is dedicated to the question of how to detect structures in rather poor data. The identification of areas with different quantities in a certain reference system is a useful concept to reveal inherent structures in the data. For points without additional information in a coordinate system, density calculation is the preferred method. This chapter starts by exploring different concepts of density calculation in a one-dimensional space. Histograms are discussed as a pre-step to density calculations, before kernel techniques are developed based upon a critique of histograms. Time will serve as a one-dimensional space and it allows making connections to time series analysis, thus fostering an understanding of general concepts applied in different fields of applications. The second part of the chapter will apply the principles to a two-dimensional space. Kernel- and distance-based techniques are presented and their respective advantages and disadvantages are discussed. “Kernel density estimation” (KDE) and “empty circle density” (ECD) are described in detail, as the most common methods. Decomposition of density models represents the final point in this chapter.


Density Kernel density estimation Empty circle density Time series Histogram Fuzzy classes Distance-between-events approach 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Oliver Nakoinz
    • 1
  • Daniel Knitter
    • 2
  1. 1.University of KielKielGermany
  2. 2.Excellence Cluster TopoiFreie UniversitätBerlinGermany

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