Hierarchical spatial modeling for estimation of population size

Jarrett J. Barber, Alan E. Gelfand

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Estimation of population size has traditionally been viewed from a finite population sampling perspective. Typically, the objective is to obtain an estimate of the total population count of individuals within some region. Often, some stratification scheme is used to estimate counts on subregions, whereby the total count is obtained by aggregation with weights, say, proportional to the areas of the subregions. We offer an alternative to the finite population sampling approach for estimating population size. The method does not require that the subregions on which counts are available form a complete partition of the region of interest. In fact, we envision counts coming from areal units that are small relative to the entire study region and that the total area sampled is a very small proportion of the total study area. In extrapolating to the entire region, we might benefit from assuming that there is spatial structure to the counts. We implement this by modeling the intensity surface as a realization from a spatially correlated random process. In the case of multiple population or species counts, we use the linear model of coregionalization to specify a multivariate process which provides associated intensity surfaces hence association between counts within and across areal units. We illustrate the method of population size estimation with simulated data and with tree counts from a Southwestern pinyon-juniper woodland data set.

Original languageEnglish (US)
Pages (from-to)193-205
Number of pages13
JournalEnvironmental and Ecological Statistics
Issue number3
StatePublished - Sep 2007


  • Coregionalization
  • Generalized linear model
  • Hierarchical model
  • Log-linear model
  • Model-based geostatistics
  • Multivariate spatial random effects

ASJC Scopus subject areas

  • Statistics and Probability
  • Environmental Science(all)
  • Statistics, Probability and Uncertainty


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