  Data aggregation in Geographic Information Systems (GIS) is only marginally
present in commercial systems nowadays, mostly through ad-hoc solutions. In
this paper, we first present a formal model for representing spatial data. This
model integrates geographic data and information contained in data warehouses
external to the GIS. We define the notion of geometric aggregation, a general
framework for aggregate queries in a GIS setting. We also identify the class of
summable queries, which can be efficiently evaluated by precomputing the
overlay of two or more of the thematic layers involved in the query. We also
sketch a language, denoted GISOLAP-QL, for expressing queries that involve GIS
and OLAP features. In addition, we introduce Piet, an implementation of our
proposal, that makes use of overlay precomputation for answering spatial
queries (aggregate or not). Our experimental evaluation showed that for a
certain class of geometric queries with or without aggregation, overlay
precomputation outperforms R-tree-based techniques. Finally, as a particular
application of our proposal, we study topological queries.
