In deformation analysis, it is important to know whether the points detected by conventional deformation analysis (CDA) are really displaced or not, and also if there are any more displaced points in the network. It is impossible to answer these questions unless the actual positions of the displaced points before the analysis are known. Moreover, the efficacy of the analysis method used must be known. The efficacy of CDA methods can be measured using the mean success rate (MSR). When a displacement occurs at a point, both the observations that belong to the displaced point and to those undisplaced points closest to it are affected. The least-squares estimation (LSE) spreads these effects to various degrees over all the coordinates. As a result, the actual displacements are not exactly reflected in the coordinates of that point. Consequently, CDA methods may wrongly identify a point as being displaced. Also the F-test is known not to be successful in some cases. Hence, the MSRs of the CDA methods are smaller than what can be expected. To eliminate the smearing effect of the LSE and the indifference of the F-test, in order to obtain more specific results a new strategy that works on absolute deformation monitoring network has been developed based on division into subnetworks.