Custom information products

Users need information products to carry out work processes and make decisions. Information products are based on source data obtained from the organization. In GeolinQ, the integration of source data into information products consists of a chain of derived datasets and processing processes. In the flexible data model data model, changes in the source data are automatically processed in the chain so that the information products are immediately updated when the source data has changed.

Combining source data

Data from different data sources can be combined into an information product by linking on a common object characteristic. Self-managed data can, for example, be linked to the BAGid from the Key Register of Addresses and Buildings (BAG) so that your own data with the geometries from the BAG can be displayed on the map. In GeolinQ, data sources in the flexible data model are linked to each other by means of references.

Decentralized data can be combined into one information product. Municipal spatial planning plans of a province can, for example, be combined into a single spatial planning information product at provincial level. In GeolinQ's flexible data model, source data sets are merged into union dataset.

Data from different data sources can also be combined based on a geometry. By cutting the location of a cable with soil types, it becomes clear in which soil types the cable will be laid. Based on the excavation costs per soil type, the excavation costs for laying the cable can be easily calculated in GeolinQ. With the intersection process, the intersection of datasets based on geometry is easy to configure in GeolinQ.

Selection of source data

Information products can consist of a selection of relevant source data. Examples of information products where a selection has been made on source data are:

The real estate owners from the BRK with real estate with a value higher than € 500,000
The high voltage cables from the KLIC report in the area of planned work.

In view datasets, selections from source data in GeolinQ can be made by configuring conditional expressions.

An information product based on point cloud or grid data consists of several source data sets if the information product covers several survey areas. An example is one dataset with the most up-to-date height data for an area based on multiple measured point clouds. With ‘Seamless Point Surface’ (SPS ), overlapping point cloud and grid data sets can be merged into one information product by selecting and prioritizing the available data sets based on the metadata. The overlap between survey areas is automatically removed so that a continuous information product is created with optimal data quality within the area.

Aggregation of source data

Information products are often based on aggregation calculations with feature data such as:

The average home value in a neighborhood
The total length of the roads in a municipality
The total area of green spaces in a municipality

With view datasets, pivot tables can also be configured in GeolinQ's flexible data model where the source data can be aggregated using aggregation functions such as summation, average, standard deviation.

Point clouds and raster data obtained with, for example, laser scans are often very extensive and are much more detailed than is necessary for an information product. An example is the AHN3 altitude file of the Netherlands consisting of more than 500 GB of altitude data. An information product for the flood risk with contours of areas below sea level contains only a fraction of the source data. Aggregation processes in GeolinQ for reducing point clouds and grid data to an information product are:

Calculate contours such as height contours based on height measurements.
Aggregation of point cloud data or grid data by interpolation.

Different observations can be aggregated to a total score per area plane by aggregating source data to a raster information product. An example is the calculation of an environmental score per 500m area by aggregating feature datasets with observations of air and soil pollution. With the raster process in GeolinQ, feature source data can be aggregated into a raster information product with a fixed surface layout.

Sum and difference of grid data

In order to provide insight into the changes in an area, the differences between measurements in a time period are recorded in an information product. An example is the determination of subsidence based on the difference between the current height measurements compared to the height measurements of 10 years ago. With configurable difference calculation processes, these information products are easy to configure in GeolinQ.

Discover the strength of configuring custom information products using GeolinQ