Scale Representation using the VisibilityFilter Attribute
Page navigation: Methods for Using the VisibilityFilter Attribute | VisibilityFilter Domains, Codes and Queries | Known Issues | Citations
Methods for Using the VisibilityFilter Attribute
The VisibilityFilter attribute allows for filtering of vector data features at eight approximate scales. A given VisibilityFilter coded-value indicates that the feature is appropriate for use at approximately the defined scale and all larger scales. Two separate methods to display NHD features with the VisibilityFilter attribute for NHDFlowline, NHDWaterbody, NHDArea, and NHDLine feature classes are described below.
►These instructions are specific to Esri ArcMap as general examples, but can be adapted to perform in other GIS software.
Method 1: Definition Query
To view features at a desired scale in Esri ArcMap:
- Open the Layer Properties and go to the “Definition Query” tab
- Select the “Query Builder” button
- Double click the “VisibilityFilter” field and then click the “>=” operator
- Select “Get Unique Values” for a list of available scales
- Double click on the desired scale and click “OK.” The desired definition query should be visible in the “Definition Query” field
- Click “OK” to apply the query and close the Layer Properties
Method 2: New Data Layer
To create a data layer with a desired scale in Esri ArcMap:
- Under the Selection menu, open the “Select By Attributes” too
- Double click the “VisibilityFilter” field and then the “>=” operator
- Select “Get Unique Values” for a list of available scales
- Double click on the desired scale and click “OK.” The desired features should now be selected
- Right click on the layer, choose “Data” and “Export Data”
- Ensure the “Export:” dropdown is set to “Selected features”. Choose a coordinate system option and location/name for the new dataset and click “OK”
VisibilityFilter Domains, Codes and Queries
Below is a list of Visibility Filter domains, codes, and queries for reference
- <1:24,000 Scale | VisibilityFilter: 0 | Queries: N/A
- 1:24,000 Scale | VisibilityFilter: 24000 | Queries: VisibilityFilter >= 24000
- 1:50,000 Scale | VisibilityFilter: 50000 | Queries: VisibilityFilter >= 50000
- 1:100,000 Scale | VisibilityFilter: 100000 | Queries: VisibilityFilter >= 100000
- 1:250,000 Scale | VisibilityFilter: 250000 | Queries: VisibilityFilter >= 250000
- 1:500,000 Scale | VisibilityFilter: 500000 | Queries: VisibilityFilter >= 500000
- 1:1,000,000 Scale | VisibilityFilter: 1000000 | Queries: VisibilityFilter >= 1000000
- 1:2,000,000 Scale | VisibilityFilter: 2000000 | Queries: VisibilityFilter >= 2000000
- 1:5,000,000 Scale | VisibilityFilter: 5000000 | Queries: VisibilityFilter >= 5000000
The VisibilityFilter attribute currently is in a testing phase. Issues will be listed here as they are discovered. Known issues will be resolved as VisibilityFilter attribute population is updated to an automated process prior to NHDPlus HR creation. The new process will allow the VisibilityFilter attribute to be populated when a geometry change to any of the NHD feature classes with the VisibilityFilter attribute is detected, ensuring NHD and NHDPlus HR data VisibilityFilter attributes are maintained and current.
- 0 or “Unspecified” Attribute Value: Some features have a VisibilityFilter attribute value of 0 or “Unspecified” incorrectly assigned. This occurs when, for a given feature, there is a mismatch between the Permanent_Identifier in the NHD dataset used to apply the VisibilityFilter attribute to the NHDPlus HR and the Permanent_Identifier in the NHDPlus HR dataset itself. In these cases, the VisibilityFilter attribute values are not properly applied from the NHD dataset to the NHDPlus HR dataset. The incorrect 0 or “Unspecified” attribute values may result in network gaps because those features are not included in the VisibilityFilter attribute query.
There are two reasons this mismatch in Permanent_Identifier occurred:
- Editing took place causing a change to the Permanent_Identifier in the NHD dataset used to apply the VisibilityFilter attribute to the NHDPlus HR dataset, resulting in different Permanent_Identifiers for the feature.
- Gridding along quad boundaries resulted in divided NHDFlowlines, which, when combined, caused a loss of the Permanent_Identifier associated.
The methods to determine the VisibilityFilter attribute for NHDFlowline, NHDLine, NHDWaterbody, and NHDFlowline features are detailed in the papers listed below.
Buttenfield, B. P., Stanislawski, L. V., and Brewer, C. A., 2011, Adapting generalization tools to physiographic diversity for the United States National Hydrography Dataset: Cartography and Geographic Information Science, v. 38, no. 3, p. 289-301, http://www.tandfonline.com/doi/abs/10.1559/15230406382289, last accessed 05/09/2017.
Stanislawski, L. V., 2009, Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset: Computers, Environment and Urban Systems, v. 33, p. 325-333, http://www.sciencedirect.com/science/article/pii/S0198971509000520, last accessed 05/09/2017.
Stanislawski, L. V. and Buttenfield, B. P., 2011, A raster alternative for partitioning line densities to support automated cartographic generalization: Proceedings of the 25th International Cartographic Conference, Paris, France, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.468.8004&rep=rep1&type=pdf, last accessed 05/09/2017.
Stanislawski, L.V., Doumbouya, A.T., Miller-Corbett, C.D., Buttenfield, B.P. and Arundel, S.T., 2012, Scaling stream densities for hydrologic generalization: Proceedings, 7th International Conference on Geographic Information Science, September 18-21, 2012, Columbus, Ohio, 6 p.
Stanislawski, L.V., Buttenfield, B.P., and Doumbouya, A., 2015, A rapid approach for automated comparison of independently derived stream networks: Cartography and Geographic Information Science, 42(5): 435-448, DOI: 10.1080/15230406.2015.1060869
Stanislawski, L.V., Falgout, J., Buttenfield, B.P., 2015, Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing: The Cartography Journal 52(2):185-192
Stanislawski, L.V., Survila, K., Wendel, J., Liu, Y., and Buttenfield, B.P., 2017, An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions: Cartography and Geographic Information Science, DOI: 10.1080/15230406.2017.1337524
Stauffer, A.J., Finelli, E., and Stanislawski, L.V., 2016, Moving from generalization to the 'Visibility Filter Attribute': Leveraging database attribution to support efficient generalization decisions: American Water Resources Association 2016 Summer Specialty Conference, GIS & Water Resources IX, July 11-13, 2016, Sacramento, California.