R. Mac Nally, “Hierarchical Partitioning as an Interpretative Tool in Multivariate Inference,” Australian Journal of Ecology, Vol. 21, No. 2, 1996, pp. 224-228. doi:10.1111/j.1442-9993.1996.tb00602.x

has been cited by the following article:

• TITLE: Interpolating Socioeconomic Data for the Analysis of Deforestation: A Comparison of Methods

  AUTHORS: Michelle Farfán, Jean François Mas, Laura Osorio

  KEYWORDS: Interpolation; Socioeconomic Variables; Deforestation; Hierarchical Partition

  JOURNAL NAME: Journal of Geographic Information System, Vol.4 No.4, August 31, 2012

  ABSTRACT: This study compares local-level socioeconomic variables interpolated with three different methods: 1) Thiessen polygons, 2) Inverse distance weighting, and 3) Areas of influence based on cost of distance. The main objective was to determine the interpolation technique capable of generating the most efficient variable to explain the distribution of deforestation through two statistical approaches: generalized linear models and hierarchical partition. The study was conducted in two regions of western Mexico: Coyuquilla River watershed, and the Sierra de Manantlan Biosphere Reserve (SMBR). For SMBR it was found that the Thiessen polygons and areas of influence were the techniques that interpolated variables with greatest explanatory power for the deforestation process, in Coyuquilla it was inverse distance weighting. These differences are related to the distribution and the spatial correlation of the values of the variables.