  Measures of association play a role in selecting 2x2 tables exhibiting strong
dependence in high-dimensional binary data. Several measures are in use
differing on specific tables and in their dependence on the margins.
  We study a 2-dimensional group of margin transformations on the 3-dimensional
manifold T of all 2x2 probability tables. The margin transformations allow
introducing natural coordinates that identify T with the real 3-space such that
the x-axis corresponds to log(sqrt(odds-ratio)) and margins vary on planes
x=const. We use these coordinates to visualise and compare measures of
association with respect to their dependence on the margins given the
odds-ratio, their limit behaviour when cells approach zero and their weighting
properties.
  We propose a novel measure of association in which tables with single small
entries are up-weighted but those with skewed margins are down-weighted
according to the relative entropy among the tables of the same odds-ratio.
