Data can be stored in a Digital Nautical Chart (DNC) Nautical Information System (NIS) database for editing and maintenance. The DNC NIS is a multiscale database where the same real-world feature may exist multiple times—with different geometric representations—at different compilation scales.
When you initially load the DNC NIS with data from existing DNC VPF libraries, the results often contain multiple database features representing the same real-world feature. For example, if you load data from four overlapping DNC VPF libraries into the NIS, you could have four buoy features—one at each relevant DNC scale—representing the same real-world buoy. The same thing can happen with wrecks, lights, and many other features.
When this occurs, you have two choices:
- Retain all real-world representations of the same feature in the NIS and associate each one with its appropriate DNC scale value.
- Combine two or more representations of the same feature into a single feature in the DNC NIS and use that feature in multiple products at different scales (conflation).
The first approach—retaining multiple copies of the same feature—requires no preparatory work. However, over time, as data is maintained, an easy change, such as the color or shape of a buoy, must be repeated for each representation of the feature at multiple scales.
The second approach—combining copies of a feature into a single feature—does require preparatory work but should be more efficient in the long run. An edit would only have to be performed once for all scales.
The process of combining multiple sources of information about a feature into a single record is called conflation. You may be interested in conflating information about real-world features compiled at different scales like buoys and wrecks derived from various sources, such as existing chart products and new surveys.
The DNC schema has four library scales: Harbor, Approach, Coastal, and General. Every feature in the DNC NIS has a LIBRARY_CHART_TYPE field showing the DNC library scale at which the feature was collected. For point geometry, you have 10 options for attributing the feature:
For a given set of features, the one with the Harbor value has the largest compilation scale. The geometry of a feature compiled at a larger scale (Harbor) is usually more precise than that of the same feature compiled at a smaller scale (General).
Conflating point features in the NIS
An example of simple conflation is a buoy that appears on three separate DNC scale charts (1:10,000 – Harbor, 1:80,000 – Approach, and 1:250,000 – Coastal). You could conflate the three buoy features, retaining the 1:10,000 – Harbor feature. The conflated buoy feature is manually assigned a new LIBRARY_CHART_TYPE value of 6 – Harbor-Coastal. As a result of the conflation process, the Harbor buoy is retained, and the remaining Approach and Coastal scaled buoy features are deleted.
Conflating point features that contain notes.rat information
If a feature contains notes.rat information, there are additional factors to consider. The decision to conflate must be consistent. If you decide to conflate the feature, you must also conflate its notes. For example, you might have a TideDataPoint (TideP) that appears with a notes.rat on three different DNC scale products (Harbor, Approach, and Coastal). You could conflate the three TideDataPoint features, retaining the Harbor scale feature, and also conflate the three notes.rat. The conflated TideDataPoint and notes.rat both get a new LIBRARY_CHART_TYPE value of 6 – Harbor-Coastal. This allows the three products to show the conflated TideDataPoint and notes.rat once populated and updated.
On the TideDataPoint feature, refer to the GFID value. The TideDataPoint GFID value directly links to the GFID value found in the notesjointable (FCsubtype = TideDataPointPS). In that specific TideDataPoint record of the notesjointable, the RAT_ID field links to the GlobalID field found in the envnotestable. For the products to be properly conflated, each LIBRARY_CHART_TYPE field will receive the value of Harbor-Coastal. Refer to the diagram for additional information.
- Identify the set of features that represent a single real-world feature.
- Decide which subset of those features you want to conflate.
If you have four candidate features, you can conflate two, three, or all four. If you choose to conflate less than the full set, the features you choose must be consecutive in the list of scales. For example, if you have four features (Harbor, Approach, Coastal, and General) and choose to conflate two of them, you can conflate the Harbor and Approach features, or Approach and Coastal, or Coastal and General. You cannot conflate the Harbor and Coastal features and retain the Approach feature as well.
- Determine which of those features has the best geometry.
Typically, this is the feature with the largest DNC scale. For example, a scale of 1:10,000 - Harbor is greater than 1:250,000 - Coastal. This is the record that you retain.
- Compare the attributes of the features to be conflated and if you find significant differences, edit the feature that you retain to correct all its attributes in the Update Attributes window.
- In the retained feature, set the LIBRARY_CHART_TYPE value to the scale of the largest DNC scale on which the conflated feature should appear (for example, 5 – Harbor-Approach, if conflating Harbor and Approach).