I have a large (10 G) feature class of 1 ft. contours derived from LiDAR that I need to identify, select, query depending on what I'm mapping. How do I manage such a large file within ArcMap? I would like to keep it as one file, as opposed to several chop

[DarcyBoellstorff]

I have a large (10 G) feature class of 1 ft. contours derived from LiDAR that I need to identify, select, query depending on what I'm mapping. How do I manage such a large file within ArcMap? I would like to keep it as one file, as opposed to several chopped up bits. However, even when the settings for drawing (e.g. do not draw unless zoomed in to 1:2,000), it takes too long to render when moving around the map. Any ideas on how to best manage this?

[George_Thompson]

Where is the data located (i.e. being stored)?

What is the end result of the contours in terms of use, i.e. visualization, analysis, etc.?

If the contours were created from LiDAR it is possible that they have tons of "extra" vertices that are adding to the lines. You may want to look at creating a contour dataset that is Generalized and/or Smoothed for general use and viewing.

[DarcyBoellstorff]

Thank you so much for this.

The data are stored locally for this testing - on the C drive and using ArcMap locally (not through a virtual server or anything). The end result of use is some analysis, so a basemap layer doesn't work.

I will try the smoothing / generalization. I didn't think of this. This is a great idea. thank you again.

[George_Thompson]

Is the data in a file geodatabase (my guess)?

Even for analysis, you may not need all of the vertices that are created when the source is LiDAR. It may take a few different passes to figure out the "sweet spot" for performance and analysis.

I used to deal with large contour sets like this for floodplain management and we always generalized/smoothed to make the contour look more natural.

#lidar data‌ #contours‌

[JoshuaBixby]

Given the density of your data set, make sure you are not projecting on the fly.  On-the-fly projection is handy, but it is a performance killer when dealing with dense data sets.

Contour lines pose a challenge for spatial indexes because a single contour line tends to span very large distances so the efficacy of using spatial indexes is reduced.  I commonly take dense data sets like contour lines and break them up using a grid, e.g., USGS 1:24,000 topographic map index grid.  Splitting the contour lines allows for better spatial index efficacy and quicker draw times when zoomed into small areas.  For analysis, some of how contours are selected might need to be modified, but results are not impacted once the selections are made correctly.