  We present a new technique for choosing spatial regions for X-ray
spectroscopy, called "contour binning". The method chooses regions by following
contours on a smoothed image of the object. In addition we re-explore a simple
method for adaptively smoothing X-ray images according to the local count rate,
we term "accumulative smoothing", which is a generalisation of the method used
by FADAPT. The algorithms are tested by applying them to a simulated cluster
data set. We illustrate the techniques by using them on a 50 ks Chandra
observation of the Cassiopeia A supernova remnant. Generated maps of the object
showing abundances in eight different elements, absorbing column density,
temperature, ionisation timescale and velocity are presented. Tests show that
contour binning reproduces surface brightness considerably better than other
methods. It is particularly suited to objects with detailed spatial structure
such as supernova remnants and the cores of galaxy clusters, producing
aesthetically pleasing results.
