摘要：Abstract
With the increasing availability of data, geoscience
provides many methods to model the spatial extent
of various phenomena.Acquiring representative, high
quality data is the most important criterion to assess the
value of any spatial analysis, however, there are many situations
in which these criteria cannot be fulfilled. Archived
data, collected in the past, for which analysis cannot be
repeated or supplemented is a very common information
source. Archaeological data collected at a regional extent
during years of field work and superficial observations are
an additional example. Such data rarely provide representative
samples and are usually imbalanced; only very few
examples contain useful data, while many examples remain
without any archaeological traces. In spite of these
limitations archaeological information presented in the
form of maps can be a useful and helpful tool to analyse
the spatial patterns of some phenomena and, from a more
practical point of view, a tool to predict the location of
undiscovered occurrences. The primary goal of this paper
is to present a methodology for modelling spatial patterns
based on imbalanced categorical data which do not fulfil
the criteria of spatial representation and incorporates
uncertainty in its decision process. This concept will be
discussed using a collection of Stone Age sites and set of
environmental variables from the postglacial lowlands in
Western Poland. We will propose a machine-learning system
which adopts CART through bootstrap simulation to
incorporate uncertainty into the spatial model and utilise
that uncertainty in the decision-making process. Finally,
we will describe the relationships between the model and
environmental variables and present our results in cartographic
form using the principles of decision-tree cartography.
关键词：Keywordsuncertainty spatial modelling machine learning
stone age geomorphometry
