Estimating population growth rates and instantaneous population from periodized settlement data

Keith W. Kintigh, Matthew A. Peeples

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Settlement surveys that yield counts of the number of houses or rooms dated to a chronological period can be used to indicate population and to estimate rates of population growth or decline. Population estimates and population growth rates are, of course, interpretively critical for describing and explaining many aspects of social dynamics. For this sort of survey data, population growth rates can be estimated by applying an ordinary, compound interest formula to room counts, standardized by period length, for successive time periods. This article offers a more nuanced approach that simulates a continuous process of room construction and abandonment, yielding a total number of rooms occupied during a period. The modeled growth rate is the value at which the model total most closely matches the value observed in the settlement data, given the set of model parameters. The model results are sensitive to both structure use-life and the founding room count. This sensitivity does not reflect a defect of the model. Instead it points to a major problem of equifinality—a great variety of different processes could account for the observed data. Exploration of three cases shows that reviewing the model results for a range of combinations of reasonable parameter values can provide demographic insights that are more informative—and can be markedly different—than those provided by the standard formula.

Original languageEnglish (US)
Pages (from-to)197-209
Number of pages13
JournalJournal of Computer Applications in Archaeology
Issue number1
StatePublished - 2020


  • Demography
  • Population Growth
  • Settlement Survey
  • Simulation
  • Southwest USA

ASJC Scopus subject areas

  • Computer Science Applications
  • Archaeology
  • Archaeology


Dive into the research topics of 'Estimating population growth rates and instantaneous population from periodized settlement data'. Together they form a unique fingerprint.

Cite this