QGIS Compared: Visualization

By Gretchen Peterson

Any GIS professional who’s been paying attention to the professional chatter in recent years will be wondering about QGIS and whether or not it might meet some or all of their needs. QGIS is open source, similar to proprietary GIS software, runs on a variety of operating systems, and has been steadily improving since its debut in 2002. With easy-to-install packagesOpenGeo Suite integration, and reliable support offerings, we obviously see QGIS as a viable alternative to proprietary desktop GIS software such as Esri’s ArcGIS for Desktop.

But will it work for you? The short answer is: most likely yes for visualization of most formats of spatial data, probably for analysis of raster and vector data, probably for geographic data editing, and probably for cartography. Those are all very subjective assertions based on my personal experience using QGIS for the past seven months but I have been using proprietary GIS for over fourteen years as an analyst and cartographer and have written a couple of books on the subject.

By all means give QGIS a try: download and install it, drag-and-drop some data into it, and give it a spin. This is definitely a good time to evaluate it and consider adopting it across your organization.

Visualizing spatial data in QGIS

In this first post, I’m going to focus on visualizing spatial data in QGIS. These basic functions are straightforward and easy to do in QGIS:

  1. adding datasets

  2. moving datasets up and down in the layer hierarchy

  3. zooming around the map

  4. selecting features based on simple point-and-click

  5. selecting features based on complex selection criteria

  6. viewing attributes

  7. creating graduated color schemes

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Strength: Versatile and efficient format support

In fact, QGIS is an effective means of viewing and exploring spatial data of almost any type. If you have complex data, you might be interested to hear that the newest release of QGIS boasts very fast, multi-threaded, rendering of spatial data that may even make it faster than leading competitors. When I began creating the map shown above, I accidentally added all of the Natural Earth 1:10m Cultural Vectors in triplicate to the project, causing some minor heart-palpitations as I realized it was going to try to render close to 100 vector layers all at once. However, my fears were unfounded as it took only a few seconds for them to render once they were all added. In the realm of visualization, it does most of the other tasks that a GIS professional would expect as well, including support for custom symbol sets (in SVG format). Adding GeoJSON data is simple, just drag a geojson file onto the Layers list. Here, we show a portion of James Fee’s GeoJSON repository of baseball stadiums:

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Mixed results: Raster visualization

That said, raster visualization can yield unexpected results depending on what is desired. Some raster datasets have tables that associate bands with RGB values such that specific cell-types are rendered certain colors. Often, landcover datasets will have this kind of structure so that, for example, the raster is rendered with blue for water, green for grass, white for ice, and so on. Unfortunately, QGIS doesn’t yet support rendering based on associated table files for rasters. Another slight irritation is the continuing use of binary ARC/INFO GRID formats by some agencies who distribute raster data to the public. If you have one of these datasets, QGIS can open it but you must point to the w001001.adf file using the raster data import button.

Mixed results: On-the-fly reproduction

One of the most important ways to make GIS user-friendly is to support on-the-fly projection. I still remember when projecting on-the-fly became a part of the software that I used to use. It was the end of 1999, and life was so much easier when multiple datasets from multiple agencies in multiple projections could all be jammed together into a single project, producing a map where all the data layers were in the correct projected space. This was because reprojecting not only added extra steps requiring you to reproject everything into a common coordinate system even if all you wanted to do was visualize the data, it also meant maintaining multiple copies of the same dataset, which contributed to folder clutter and using up of valuable disk space. QGIS supports reprojection on-the-fly but it is an option that must be set in the project properties dialog. Some glitches with projections still seem to occur from time to time. Zooming in, for example, sometimes causes the map to zoom to a different place than expected. However, this unexpected behavior is inconsistent, not a showstopper, and may be fixed soon.

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Hidden gem: Context

The other important aspect of visualizing data is having enough underlying context for the data. Country boundaries, city labels, roads, oceans, and other standard map data are crucial. Proprietary GIS software generally contains basemap layers that can easily be turned on and off to support visualization in this manner. QGIS also has this capability, in the form of the OpenLayers plugin, which serves up Google, OpenStreetMap, Bing, and Yahoo basemaps at the click of a button. The OpenLayers plugin is free and installs just like any other QGIS plugin—you search for it in thePlugins menu, press “install,” and make your basemap choice in the Web menu.

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While QGIS may need a small amount of improvement when it comes to raster visualization and on-the-fly projection, these aren’t hindrances to a typical visualization workflow and are only mentioned here out of respect for a fair and balanced assessment. By and large, my testing has convinced me that the robust visualization capabilities that QGIS offers provide more than enough impetus for many organizations to make the switch to QGIS. In later posts, I’ll discuss how QGIS performs with respect to analysisediting, and cartography.

Reprinted with permission from the Boundless Blog.

Published Monday, November 17th, 2014

Written by Gretchen Peterson

Published in

Open Source

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