What are the challenges regarding the construction and utilization of spatial data warehouses?

There are several challenging issues regarding the construction and utilization of spatial data warehouses. The first challenge is the unification of spatial information from heterogeneous sources and systems. Spatial data are usually stored in different industry firms and government agencies using various data formats.

Data formats are not only structure-specific (e.g., raster- vs. vector-based spatial data, object-oriented vs. relational models, different spatial storage and indexing structures), but also vendor-specific (e.g., ESRI, MapInfo, Intergraph). There has been huge work on the unification and exchange of heterogeneous spatial data, which has paved the way for spatial data integration and spatial data warehouse construction.

The second challenge is the realization of fast and flexible online analytical processing in spatial data warehouses. The star schema model is the best choice for modeling spatial data warehouses because it supports a concise and organized warehouse structure and supports OLAP services. But, in a spatial warehouse, both dimensions and measures can include spatial elements.

There are three types of dimensions in a spatial data cube which are as follows −

Non-spatial dimension − A non-spatial dimension contains only non-spatial data. Nonspatial dimensions temperature and storm can be generated for the warehouse. For example, because each includes non-spatial data whose generalizations are nonspatial (including “hot” for temperature and “wet” for precipitation).

Spatial-to-Non-Spatial dimension − A spatial-to-non-spatial dimension is a dimension whose primitive-level data are spatial but whose generalization, starting at a certain high level, becomes non-spatial.

Spatial-to-Spatial dimension − A spatial-to-spatial dimension is a dimension whose primitive level and all of its high-level generalized data are spatial. For example, the dimension equi _temperature region includes spatial data, as do all of its generalizations, including with regions covering 0-5 degrees (Celsius), 5-10 degrees, etc.

There are two types of measures in a spatial data cube which are as follows −

Numerical Measure − A numerical measure contains only numerical data. For example, one measure in a spatial data warehouse can be the monthly revenue of a region, so that a roll-up can evaluate the total revenue by year, by county, etc. Numerical measures can be classified into distributive, algebraic, and holistic.

Spatial Measure − A spatial measure contains a collection of pointers to spatial objects. For example, in a generalization (or roll-up) in the spatial data cube of the regions with the same range of temperature and precipitation will be grouped into the same cell, and the measure so formed includes a set of pointers to those areas.

Non-spatial data cube − A non-spatial data cube includes only non-spatial dimensions and numerical measures. If a spatial data cube includes spatial dimensions but no spatial measures, its OLAP operations, including drilling or pivoting. It can be performed in an aspect similar to that for non-spatial data cubes.