This thesis consits of four papers dealing with distance based (non-Euclidean) tests for spatial clustering in inhomogeneous populations.
The density adjusted distance (DAD), which considers the underlying density, is defined in the first paper. The proposed distance can be used together with any of the old distance based methods developed for traditional homogeneous spatial patterns.
The test statistics in distance based tests can all be seen as a weighted sum of distance measures for distances between n cases with known co-ordinates. DAD based test statistics are developed and their performance is compared with the performance of previously suggested tests by simulation in the second paper. The tests are compared in different types of data set and for various kinds of clustering. It is shown that no test is the optimal choice for all alternative hypotheses and that the tests are unequally sensitive to the structure of the underlying data. Tests based on the DAD are often a good alternative.
Test statistics and graphical tools for the Empirical Distribution Function of DAD are developed and examined in the third paper. We show that the result of an EDF test combined with EDF plots provides more information about the possible nature of clustering in a sample than the result of a parametric test only.