A vector-based geographic cellular automata model

In this research, a new vector-based cellular
automata model is proposed to overcome the cell size
and neighbourhood configuration sensitivity of the
classical raster based CA models and the limitations
of the previous implementations of vector-based CA
models. In this model, space is represented as a
collection of geographic objects where each object
corresponds to a real-world entity of irregular
shape and size that evolves through time. This
evolution is expressed by the change of shape and
size of the objects and it is performed using a
geometric transformation procedure. This procedure
reduces the area of an object in the region nearest
to the neighbour that exerts an influence on it and
increases the area of that neighbour. The
neighborhood is not rigid and restricted to a set of
adjacent polygons; rather, it is dynamic, it
includes the whole geographic space, and it is
specific to each object. Their results showed that
the geometrical transformations defined in the model
generate an adequate evolution of the geometry of
the objects, producing more realistic spatial
patterns than the ones produced by a raster-based CA
model.

Autorentext

Water Modelling Analyst of Alberta Environment, Canada. She completed a MSc. degree in Computer Science (2001) from University of Los Andes, Venezuela, and obtained A Ph.D. in Geomatics Enginnering (2008) at the University of Calgary, Canada. Her research is focused on the development of GIS application to Water and Land-use Modelling.

Klappentext

In this research, a new vector-based cellular automata model is proposed to overcome the cell size and neighbourhood configuration sensitivity of the classical raster based CA models and the limitations of the previous implementations of vector-based CA models. In this model, space is represented as a collection of geographic objects where each object corresponds to a real-world entity of irregular shape and size that evolves through time. This evolution is expressed by the change of shape and size of the objects and it is performed using a geometric transformation procedure. This procedure reduces the area of an object in the region nearest to the neighbour that exerts an influence on it and increases the area of that neighbour. The neighborhood is not rigid and restricted to a set of adjacent polygons; rather, it is dynamic, it includes the whole geographic space, and it is specific to each object. Their results showed that the geometrical transformations defined in the model generate an adequate evolution of the geometry of the objects, producing more realistic spatial patterns than the ones produced by a raster-based CA model.