  The definition of spacings associated to a sequence of random variables is
extended to the case of random vectors in [0,1]^2. Beirlant & al. (1991) give
an alternative proof of the Le Cam (1958) theorem concerning asymptotic
normality of additive functions of uniform spacings in [0,1]. I adapt their
technique to the two-dimensional case, leading the way to new directions in the
domain of Complete Spatial Randomness (CSR) testing.
