Modern data warehouses are becoming more and more powerful. In the past, data warehouses can only present statistical reports in flat table formats. But today’s data warehouses can supply data and, in conjunction with other application such as geographic information systems (GIS), reports can be done in graphical format that shows geographic maps. This can be very useful when presented to a business organization’s decision and policy makers because they can visually see how the business operates in relations to certain locations around the world.
A lot of GIS systems could allow for the separation of different geographic features in logical categories. These categories are called map layers. Each of these layers contains information taken from one geographic feature or from a selected small group that has related features. Because data is separated in layers, they can be easily manipulated and spatially analyzed.
The analysis can be done with one layer or with a combination of other layers. The separation of information in layers and its ability to be in combination with other layers is where GIS systems draw most of its analytic power. It is just but logical that when data layers are separate, it could be easier to handle spatial data in the database.
A data layer represents a set of spatial objects, coverage or themes within the spatial database. It acts as a model or entity reflecting real world topics, events and entities that are of interest to the organization implementing the spatial database.
Since the data layer is a set consisting of entities within a certain limit, it could be possible that some data cannot be included in the set within the data layer. This is the Data Layer Exclusion.
For example, a spatial database containing important information about urban grown model is being implemented in order to be able to predict the dynamics of urban growth. This database may have an exclusion layer that defines which particular portion of the urban areas where human population will never occur so that the database may not spend resources by allocating space for them.
Example of the Data Layer Exclusion would include bodies of water and natural reserves. It is would be good to note that while the data layer would include a certain enclosed area, the bodies of water or natural reserve within the enclosed area would constitute for the Data Layer Exclusion.
The Data Layer Exclusion can give a lot of benefits and advantages to the GIS and spatial database in particular and in the information system in general. Because a Data Layer Exclusion specifies in detail which portion of a data layer extent for which data are not captured and stored, the GIS and spatial database may not have to allot additional storage space for the particular area.
This could mean a lot of savings for the company since hard disk and other storage devices could be costly. Imagine the scenario if the spatial database is implemented for a data warehouse. Hundreds or thousands of Data Layer Exclusion could translate into so much saved space storage.
For the information system in general, having Data Exclusion Layer could mean reduced processing load because the system will simply skip the exclusion portion. This could mean improved in overall processing speed and performance as well as reduced network traffic because only the important and relevant spatial data can be extracted from the GIS system as the Data Layer Exclusion specification has already defined which portions to skip.