Conflation is a process by which two digital data layers, usually of the same area at different points in time, or two different data layers of the same area, are geographically "corrected" through geometrical and rotational transformations so that the different layers can be overlaid on one another. Also called "rubber-sheeting ", our operators adjust the coordinates of all features on a data layer to provide a more accurate match between known locations and a few data points within the base data set.

Two important kinds of conflation are Version Matching and Feature Alignment.

Version Matching-- The source datasets consist of different versions of the same features. The conflation process is intended to identify matching features. Attributes may be transferred between matched features, and unmatched features may be transferred in their entirety. An example of this is matching different versions of road networks for the same geographical area.

Feature Alignment-- The source datasets consists of features from two or more different feature classes that bear some defined relationship to each other. An example of this is aligning the boundaries of different kinds of feature classes such as municipal districts and lot parcels.

Conflation is a human-assisted process and generally can be broken down into the following common subtasks. Depending on the nature and quality of the data in a specific conflation problem, some of these subtasks may be trivial or not required.

Data Pre-processing-- This step normalizes the input datasets to ensure that they are compatible. For instance, they must have the same coordinate system. This may also involve format translation and any other basic preparation of the datasets.

Data Check-- During this step the internal consistency of the datasets is verified and if necessary improved.

Dataset Alignment-- When datasets are sufficiently misaligned, an initial alignment process is required to allow more precise conflation to be carried out. This alignment is typically coarse grained in nature, not descending to the level of aligning individual features.

Feature Matching-- During this step common features between the datasets are matched. After this phase has been performed the discrepancies between the datasets will have been identified. It is often useful to provide statistical summaries of data quality, or to visualize the discrepancies.

Geometry Alignment removes discrepancies between geometries.

Information Transfer involves updating one dataset with information from the other. This information can be either attributes or geometry to be added to an existing feature, or entire features to be added to the dataset.

This Example shows street network matching between two types of representations of streets in the same region.