  Sampling from very large spatial populations is challenging. The solutions
suggested in recent literature on this subject often require that the randomly
selected units are well distributed across the study region by using complex
algorithms that have the feature, essential in a design-based framework, to
respect the fixed first-order inclusion probabilities for every unit of the
population. The size of the frame, $N$, often causes some problems to these
algorithms since, being based on the distance matrix between the units of the
population, have at least a computational cost of order $N^2$. In this paper we
propose a draw-by-draw algorithm that randomly selects a sample of size $n$ in
exactly $n$ steps, updating at each step the selection probability of
not-selected units depending on their distance from the units already selected
in the previous steps. The performance of this solution is compared with those
of other methods derived from the {\it spatially balanced sampling} literature
in terms of their root mean squared error (RMSE) using the simple random
sampling (SRS) without replacement as benchmark. The fundamental interest is
not only to evaluate the efficiency of a such different procedure, but also to
understand if similar results can be obtained even with a notable reduction in
the computational burden needed to obtain more efficient sampling designs.
Repeated sample selections on real and simulated populations support this
perspective. An application to the Land Use and Land Cover Survey (LUCAS) 2012
data-set in an Italian region is presented as a concrete and practical
illustration of the capabilities of the proposed sample selection method.
