On Line Analytical Processing (OLAP) is a technology that extends conventional database technology by enabling rapid analysis of aggregated data. Like most information technology, OLAP comes with its own vocabulary. Whereas data in a traditional database is stored in two-dimensional tables, OLAP databases store data in multi-dimensional cubes that enable people to quickly change their view of aggregated data with less effort. The cube is made up of numeric facts called measures – like the ‘number of packages of widgets shipped to a client’. Measures are grouped into dimensions. Some typical dimensions might include time, product categories, delivery areas and so on.
OLAP cubes can be queried in a similar manner to a conventional database, but while most databases use Structured Query Language (SQL), their OLAP brethren have their own language, called MultiDimensional eXpressions (MDX). You wouldn’t want to use MDX to run your sales transaction database, but it’s ideally suited to create a report such as ‘Total Packages Delivered by Route by Product Source per Quarter for the last 5 years’.
The output of an MDX query can be represented in all of the traditional ways including tables and charts, but we are obviously interested in the geography and maps. While OLAP systems have been used in large businesses to analyze sales and other data for many years, their use with geographic data has been limited. Geospatial information has special properties that are not captured in most OLAP systems, such as proximity and cartographic hierarchies (like various zoom levels). The distribution of events in space and time has much to say about those events, and the spatial part of that equation is not yet incorporated fully in many of the tools on the market today. By incorporating these special properties into OLAP cubes, more powerful data analysis can be performed, revealing new and important patterns in information. My research seeks to bring spatial analysis into the OLAP world and broaden the power and applicability of this technology. I am particularly interested in real estate data and am working with several years of Vermont real estate sales.
