Manage data in datasets
In GeolinQ spatial and administrative data are managed in datasets. The structure of the data managed in a dataset and the metadata of the dataset are defined by class definitions. The attributes of the class definitions describe the data structure and the metadata of the dataset. The class definitions support inheritance. As a result different types of objects derived from the same parent can be stored in a single dataset.
In GeolinQ three types of data are managed in datasets:
- Rasters and point clouds
- Feature- or vector data
- Administrative data
Rasters and point clouds
Rasters are data structured in a regular grid like for example areal views. Point clouds are individual geodetic points on arbitrary locations like for example Lidar terrain data. Raster and point clouds are easily imported and visualized in maps using colour scales or a RGB value in GeolinQ. Rasters and point clouds are divided in tiles during import automatically for an appropriate performance on every map scale.
Multiple rasters and points clouds are merged together using the Seamless Point Surface (SPS) to a single continuous dataset. In the SPS only the hulls of the datasets are stored and the tiles of the underlying datasets are reused. As a result the SPS requires little overhead in storage, but has the same performance as the underlying datasets.
Rasters and point clouds are processed in processes where a point cloud is resampled or contours are calculated and a difference dataset of two datasets is calculated. Very large dataset containing billions of points are processed easily, because GeolinQ streams the rasters and point clouds using scalable algorithms.
Feature- or vector data
Feature or vector data are data with a spatial component (a geometry) and attribute values. Features are imported and are visualized using the Styled Layer Description (SLD). The styling of the features is described by style rules and symbolizers in the SLD. SLD's can be created, imported, edited and exported in GeolinQ.
Feature datasets are linked to other feature- and administrative datasets by using references. New derived feature- and administrative datasets are created using views to satisfy immediately the information needs of the end-user.
Administrative data are data with attribute values without a spatial component. Administrative data are often part of spatial data collection like core registrations. Administrative data are visualized in lists and are referenced from other datasets.
Administrative data collections are linked to spatial data using views and joins. Once the administrative data is joined to spatial data the administrative data can be visualized on a map.