Tips on Serving Geospatial Data

My colleague at Esri Canada wrote a very useful essay containing 9 tips to ensure high quality open data downloads. This includes how to organize and publish your data services, paying attention to the number of features, titles, and data descriptions, fields and field names, showing and hiding fields, tags, licensing information, thumbnails, and metadata. As such, the essay is germane to the focus of this geospatial data blog. The article focuses on the ArcGIS platform but applies throughout the GIS professional community as a set of sound principles.

Documenting geospatial information, as part of the best practices for serving data.

In addition, this set of workshop slides from Esri provides additional information on practices, plus one that is not in the above essay, having to do with simplifying data if your data is dense with vertices: ArcGIS Hub- Open Data Best Practices (esri.com).

In addition to the above resources, this topic presents a good opportunity to remind the reader about the following guidelines:

• How to manage metadata in ArcGIS Online: https://doc.arcgis.com/en/arcgis-online/manage-data/metadata.htm
• The ArcGIS metadata format:
  https://desktop.arcgis.com/en/arcmap/latest/manage-data/metadata/the-arcgis-metadata-format.htm#
• Updating metadata to ArcGIS Pro: https://pro.arcgis.com/en/pro-app/latest/help/metadata/upgrade-metadata-to-the-arcgis-format.htm

I hope this is helpful and I look forward to your comments below.

–Joseph Kerski

Be critical of and aware of default settings in GIS software

We recently wrote about another reason to be critical of the data–especially imagery–when it can be misinterpreted and when it can be deliberately faked. Included in that essay was a brief paragraph encouraging the community to also be aware and critical of default settings in GIS software when rendering and analyzing imagery. Why? Default settings are there to accommodate a wide variety of users, but can lead to conclusions that are at best, not as rigorous or as accurate as they could be, or, at worst, in error.

Below are images from my Esri colleague and one of my favorite people in all of geospatial, cartographer John Nelson, that represent a set of 8 NASA images. Says John, “They are designed to mosaic together and the native images match each other perfectly. But because of the image appearance default settings assigned by, in this case, ArcPro from Esri, you can easily see seams between them. The default “percent clip” stretch type eliminates 5% of each end of the image histogram, throwing out 10% of the data. Because each image histogram is slightly different, this inherently introduces variability between them. The default “gamma” setting is dynamic based on image and is different for each, in an attempt to find an ideal visual contrast. A gamma of 1 renders the image in its native value. Most of the eight images in this map were given different gamma values (ranging from 1.4 to 1.6) so the visual variability between images is especially stark.” See one of John’s videos illustrating the benefits of and how to quickly and powerfully override the defaults in one imagery example, here.

If a GIS user were to manually reset all of these overrides, which in this case are not ideal, the eight images render cohesively, as designed. Says John, “There is no way to opt out of default rendering overrides, and there is no way to multi-select the image layers and re-set their parameters all at once. If (the GIS user) wants to now adjust their stretch and gamma settings in unison, they have to do that individually or create a new mosaic.”

This obviously applies to any GIS and remote sensing software–all software has default settings and those settings need to be understood. Certainly, in many cases, the defaults are useful, time saving, and appropriate. But knowing what they are–from smart mapping symbology or rendering imagery to many more GIS workflows–are a critical part to our central message of our book and this blog–be critical of the data. I would also argue that since part of misinterpretation of imagery is a result of the lack of knowledge of the electromagnetic spectrum and images rendered in specific bands–all the more reason to include remote sensing in educational curricula!

–Joseph Kerski