Data warehousing and On-line Analytical Processing (OLAP) are powerful tools for processing and analyzing business and analytical data. It is estimated that 80% of the data stored in data warehouses have some spatial components. It is our belief that there is a need for powerful OLAP tools that are capable of processing and analyzing spatial data. This thesis explores the design and implementation of Spatial OLAP (SOLAP) systems and describes approaches to support the characteristic features of OLAP while seamlessly integrating spatial data into the analysis process. In particular, we analyze the evaluation of OLAP queries in the presence of asymmetric, multiple-alternative, generalized, and non-strict spatial dimension hierarchies. We introduce a new pipeline-based query evaluation model that is comprehensive and powerful in that it provides a uniform approach to the expression of spatial OLAP queries that address all major dimension hierarchy types while permitting a uniform treatment of both spatial and non-spatial data. A reference implementation called "LISA" validates the objectives of our model and demonstrates favorable scalability and performance on modern multi-processor and multi-core hardware platforms. We also describe a new "geoCUBE" index, to address the fundamental problem of how to represent, index and efficiently query data that is defined by a mix of spatial and categorical attribute values. The geoCUBE index extends existing methods for indexing OLAP data to spatial data types. The effectiveness of the geoCUBE data structure is confirmed through evaluation. Lastly, we propose algorithms that facilitate OLAP-like analysis of moving object data. We introduce a new class of GROUP BY operators specifically targeted to the OLAP analysis of trajectories and to answering aggregate queries with respect to the spatio-temporal movement of a set of objects. Through an experimental evaluation we show our operators can be used to reliably identify groups of related trajectories when applied to synthetic and real world moving object data.