  The Takacs--Fiksel method is a general approach to estimate the parameters of
a spatial Gibbs point process. This method embraces standard procedures such as
the pseudolikelihood and is defined via weight functions. In this paper we
propose a general procedure to find weight functions which reduce the Godambe
information and thus outperform pseudolikelihood in certain situations. The new
procedure is applied to a standard dataset and to a recent neuroscience
replicated point pattern dataset. Finally, the performance of the new procedure
is investigated in a simulation study.
