  The use of spatially explicit land use and land cover (LULC) change models is
widespread in environmental sciences and of interest in public decision-help.
However, it appears that these models suffer from significant biases and
shortcomings, the sources of which can be mathematical, conceptual or
algorithmic. We formalize a modeling environment that distinguishes a
calibration-estimation module and an allocation module. We propose an accurate
calibration-estimation method based on kernel density estimation and detail an
unbiased allocation algorithm. Moreover, a method of evaluation of LULC change
models is presented and allows us to compare them on various fronts (accuracy,
biases, computational efficiency). A case study based on a real land use map
but with known (enforced) transition probabilities is used. It appears that the
estimation error of the methods we propose is substantially improved over the
best existing software. Moreover, these methods require the specification of
very few parameters by the user, and are numerically efficient. This article
presents an overview of our LULC change modeling framework; its various formal
and algorithmic constituents will be detailed in forthcoming papers.
