Positional Data Shifting

Shifting data is required when positional accuracy of some data is known to be poor, or at least not good enough for its intended use.

Techniques used to capture spatial data have improved over the years, especially in terms of the positional accuracy. This causes problems both for data suppliers who need to update their existing data in order to use more accurate capture techniques and also for data consumers if their own data becomes misaligned when the base data positioning is improved. For example, utility pipes that run within a road surface polygon within the base data may no longer run along inside the road after the data has been shifted.

Similar issues occur when performing data conflation (i.e. bringing together data from multiple sources). If the positional accuracy of the data is not the same, a straight integration can result in erroneous relative positions between features.

In order to resolve the inconsistencies caused by variations in positional accuracy, the best solution is to improve the low accuracy data to match or at least sufficiently approach the accuracy of the high accuracy data.

Shifting data to improve position is more complex than simply applying the same transformation to all features as the shifts required vary across the data. 1Validate uses Shift Vectors to optimise how data is shifted.