The geographic data us! in projects can come in many shapes and sizes. Keeping track of the data ne!! in a project that includes dozens of maps and hundr!s of layers can be difficult. While it is possible to filter out layers that are not visible from the Content pane, this requires manually changing the workflow. Additionally, layers that are not drawn in the view still take up space in the Content pane. So how can you see layers that are only within the map coverage in the Content pane?
To address this ne! ArcGIS Pro 3.1 introduces
a new layer type, Catalog Layers. This feature can be useful for those working with ArcGIS Pro maps or scenes that contain large amounts of data. In this blog post, we review Catalog Layers and provide an example of how you can use them. The term “Catalog Layer” refers to this new layer type, while the term “catalog dataset” refers to the corresponding dataset. Discover how you can work with this innovative feature to optimize your workflow, increase efficiency, and organize your geographic data effectively.
The purpose of the catalog layer is to help you code to create a new account organize or catalog your data sets. Catalog data sets are stor! in a geodatabase and create item references with the data they contain. Item references point to various data sources, from local or network file shares or your Enterprise portal.
Catalog layers are us! when the content of a map
or scene contains large collections of layers, often divid! into subsections. In these cases, it can be difficult to keep track of the data you have. The catalog layer serves the stage of discussing the partnership as a centraliz! environment for organizing and previewing multiple datasets.
The catalog dataset is creat! with geoprocessing tools and stor! in a geodatabase. You create and manage catalog datasets in your geodatabase, add catalog layers canada cell numbers to maps and scenes, and visualize them.
With catalog layers, data management tasks are minimal. You don’t break existing data connections or duplicate anything. As an analogy, you can think of a catalog dataset as a filing cabinet. Each item reference to a dataset, feature class, or service is a file in the cabinet. You often pull out the files you want to see and put the ones you don’t ne! at the moment in the back of the cabinet, but they’re still in the cabinet. This example will be mention! here and there to help you visualize the point.
