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Archive for February, 2019

The Top 10 Most Useful Geospatial Data Portals, Revisited

February 18, 2019 7 comments

We have been writing this geospatial data column for 7 years now, beginning when our book The GIS Guide to Public Domain Data, was published.  Over those years, in addition to keeping issues such as data quality, copyright, privacy, and fee vs. free at the forefront of the conversation, we have tested and reviewed many geospatial data portals.  Some of these portals promise more than they deliver, some have been frustrating, but many have been extremely valuable in GIS work.  Back in 2017 we listed 10 of those that we have found most useful, rich with content, easy to use, and with metadata that is available and understandable.  A few are no longer functioning, and a few have emerged that merit inclusion in the top 10 list.  In creating such a list, we realize that “most useful” really depends on the application that one is using GIS for, but the list below should be useful for GIS users across many disciplines. Some allow for data to be streamed from web servers into your GIS software, and all allow data to be downloaded.

  1. The Open Data portal based on ArcGIS Hub technology.   This portal’s simple “what” and “where” interface is the entry point to a vast, curated, and growing list of valuable open data sites, along with a helpful story map described here.
  2. The Esri Living Atlas of the World is an expanding, curated set of data and maps on thousands of topics that can be used and also contributed to by the GIS community.
  3. The European Space Agency’s Sentinel Online data portal includes a wide variety of image-related data sets on the five themes of land, marine, atmosphere, emergency, and security.
  4. CIESIN at Columbia University has been serving data for over 20 years on climate, population, soil, econonics, land use, biodiversity, and other themes, including its Socioeconomic Data and Applications Center (SEDAC).
  5. The Atlas of the Biosphere serves global data, largely in grid format, of human impact, land use, ecosystems, and water resources themes.
  6. Natural Earth is a public domain dataset at small scale (1:10,000,000, 1:50,000,000, and 1:110,000,000) for the globe, in vector and raster formats that are easily ingestible in GIS software.
  7. The World Resources Institute hosts a variety of data geospatial data sets for specific areas of the world, such as Kenya and Uganda.
  8. The FAO GeoNetwork.  This portal contains global to regional scale data from administrative  boundaries and agriculture to soils, population, land use, and water resources.
  9. OpenTopography.   This NSF data facility from UC San Diego focuses on “Earth science-related, research-grade, topography and bathymetry data”, including a mountain-load of Lidar data.
  10. Many “lists of data sites” have appeared over the years.  Most are not kept up to date and end up being less useful over time.  However, those that are still quite helpful that we have reviewed are Dr Karen Payne’s list from the University of Georgia, and Robin Wilson’s list of free spatial data.  A few others that are useful are this list from the USGS that I started back when I worked at that organization, and this list from Stanford University.

A few others “almost make the top 10” :  The National Map from the USGS, data.gov from the US Government (though I am still frustrated that they removed the zebra mussels data that I used to access all the time), environmental and population data from TerraPopulusDiva-GIS’s data layers for each country, the UNEP Environmental Data Explorer, the NEO site at NASA Earth Observations, and OpenStreetMap (which besides roads, also includes buildings, land use, railroads, and, waterways)

For more details on any of these resources, search the Spatial Reserves blog for our reviews, remain diligent about being critical of the data you are considering using, and as always, we welcome your feedback.

lidar

Working with Lidar data obtained from the USGS National Map data portal. 

–Joseph Kerski

Track on Track, Revisited: Spatial Accuracy of Field Data

February 4, 2019 3 comments

Back in 2014, I tested the accuracy of smartphone positional accuracy in a small tight area by walking around a track.  During a recent visit to teach GIS workshops at Carnegie Mellon University, I decided to re-test, again on a running track.  My hypothesis was that triangulation off of wi-fi hotspots, cell phone towers, and the improved GPS constellation would have improved the spatial accuracy of my resulting track over those intervening years.

After an hour of walking, and collecting the track on my smartphone with a fitness app (Runkeeper), I uploaded my track as a GPX file and created a web map showing it in ArcGIS Online.  Open this map > use bookmarks > navigate to the Atlanta and Pittsburgh (Carnegie Mellon University) locations (also shown on the graphic below on the left and right, respectively).   Once I mapped my data, my hypothesis was confirmed:  I kept to the same lane on the running track, and the width of the resulting lines averaged about 5 meters, as opposed to 15 meters on the track from four years ago.  True, the 2014 track variability was no doubt in part because I was surrounded by tall buildings on three sides (as you can see in my video that I recorded at the same time) , while the building heights on the Carnegie Mellon campus were much lower.  However, you can measure for yourself on the ArcGIS Online map linked above and see the improvement over those two tracks taken just 4 years apart.

I did another test while at Carnegie Mellon University–during my last lap on the track, I moved to the inside lane.   This was 5 meters inside the next-to-outer lane where I completed my other laps.  I wanted to see whether this shift would be visible on the resulting map.  It is!  The lane is clearly visible on the map and on the right side of the graphic below, which I labeled as “inside lane.”

To explore further, on the map above, go to > Contents, to the left of the map, and turn on the World Imagery Clarity layer.   Then use the Measure tool to determine how close the track is to the satellite imagery (which isn’t perfect either, but see teachable moments link below).  You will find that at times the track was 0.5 meters from the image underneath Lane 1, and at other times 3.5 meters away.

Both tracks featured “zingers” – lines stretching away from the actual walking tracks, resulting from points dropped as I exited the nearby buildings and walked outside, as my location based service first got its bearing.  But again, an improvement was seen:  The initial point was 114 meters off in 2014, but in 2018, only 21.5 meters.  In both cases, as I remained outside, the points became more accurate.  When you collect data, the more time you spend on the point you are collecting, typically the more spatially accurate that point is.

tracks_comparison

Comparison of tracks taken with the same application (RunKeeper) on a smartphone four years apart illustrate the improvements in positional accuracy over that time. 

To dig deeper into issues of GPS track accuracy and precision, see my related essay on errors and teachable moments in collecting data, and on comparing the accuracy of GPS receivers and smartphones and mapping field collected data in ArcGIS Online here.

Location based services on the smartphone still do not yet deliver the spatial accuracy for laying fiber optic cable or determining differences in closely-spaced headstones in cemeteries (unless a device such as Bad Elf or a survey-grade GPS is used).  Articles are appearing that predict spatial accuracy improvements in smartphones.  Even today, though, I was quite pleased with my track’s spatial accuracy, particularly in 2018.  I was even more pleased considering that I had the phone in my pocket most of the time I was walking.  I did this in part because it was cold, and cold temperatures tend to rapidly deplete my cell phone’s battery (which is unfortunate, and the subject of other posts, many of which sport numerous adds, so they are not listed here).   Happy field data collection and mapping!

–Joseph Kerski