In GIS raster data and vector data both represent features in a map. Vector data does this by creating lines, points and polygons, the vertices of which are digitised as accurately as possible to outline or pinpoint the feature. It can be an accurate way of representing map features. The downside is if a large number of features need to be mapped the files can become large in size. Large vector files can consume a lot system resource in order to render on screen, also, editing and spatial queries can become memory intensive.
Enter raster data. In these situations raster data can be used to speed up rendering and querying dramatically. In this example, the vector data represents a network coverage map. The prediction used to generate these maps also work using raster data for the aforementioned reason. Generally these raster coverage maps will be vectorised so they can be used in GIS systems as this is generally considered the default format to use. This creates a much larger file that may need to be split and tiled for different regions in order for the GIS to handle the entire dataset effectively.
Using raster handlers means this raster data can be opened in its native format without the requirement for conversion to vector format. This on its own reduces the workflow steps.
As an example below, the vector map opens quickly and can be drawn on screen in seconds, but performance degrades when zooming and panning. In this case point in poly queries are not practical without splitting the dataset in to smaller regions as the map is made up of nearly half a million complex polygons and will be run against 1.5 million address points. The result would be a point in poly query showing all addresses that fall within a polygon with the attribute “No Coverage”. This allowing a sum of the population not covered to be calculated. Unfortunately using these two vector files it is not feasible to return the result in a practical time.
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The same coverage map as a raster file is not only much smaller in terms of physical file size, but far more efficient to render on screen and query.
The raster version takes less than one second to render in the map window. To run the equivalent of a point in poly query the Raster operation Point Inspection can be used. The Point Inspection operation applies the underlying raster cell value to the overlying point. So in this example, it will assign the predicted coverage value for that address to a new column in the address point file. The vector address file can then be queried easily using SQL Select to find only the addresses not covered by the network and the count.
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In this example the Point Inspection takes 42 seconds to assign 1.5 million addresses a network coverage reading of either No Coverage or Good Indoors and Outdoors. A quick Filter using the browser can then be applied to query only the addresses not covered by the network.
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The resultant query shows the addresses in green, they can be clearly seen to sit inside the areas of the coverage map that indicate No Coverage in red.
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This process takes less than a minute, using vector data the process needs to broken down it to multiple steps and processed over a much longer period of time. The raster method opens up new ways to query large datasets without complex work processes.
(The network used in this example is fictitious data)