Network optimization algorithms and scenarios in the context of automatic mapping

Many different algorithms can be used to optimize spatial network designs. For spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria. The objective of this work is to compare the performance of different optimization algorithms for both criteria. Both adding to and deleting measurements from an existing network are considered. We applied four algorithms to three datasets with known variogram models, in all cases taking the mean universal kriging variance (MUKV) as the interpolation accuracy measure. Preliminary results show that greedy algorithms that minimize the entropy perform best, both in computing time and MUKV.