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Detecting changes in spatial datasets is important for many fields such as early warning systems that monitor environmental conditions, epidemiology, crime monitoring, and automatic surveillance. The goal of the presented research is the development of methodologies for change analysis in spatial datasets. An approach to change analysis is presented that analyses how the interesting regions in one time frame differ from the interesting regions in the next time frame with respect to a given interestingness perspective. Users are allowed to define their own interestingness perspectives that are captured in form of a reward-based fitness function. A change analysis framework is proposed in which reward-based fitness functions are maximized by clustering algorithms to find interesting regions for each dataset. Next, the relationships between interesting regions are analyzed. Two approaches to analyze correspondence between interesting regions are proposed. The first approach directly compares cluster models and strongly relies on analyzing relationships between polygons that capture the scope of an interesting region. The second approach compares cluster extensions that are computed by re-clustering. Finally, a knowledge base of change predicates is provided that allows analyzing various aspects of change, and report generators that are associated with particular predicates are proposed to generate summaries of change. Moreover, in contrast to most existing research, the framework does not assume that the identity of objects in the analyzed environment is known. The proposed framework is demonstrated in a case study that analyzes changes in earthquake patterns.