Posts Tagged ‘fieldwork’

Testing positional accuracy underwater

April 15, 2019 2 comments

I recently had the opportunity to test positional accuracy while underwater.  And I did not even have to get wet!  While I was doing some GIS work with the excellent faculty at the University of Hamburg, I walked through the St. Pauli Elbe Tunnel while collecting a track.  As I did so, I reflected on the fascinating cultural and physical geography of this 1911 engineering masterpiece that is still in use:  The tunnel is 426 m (1,398 ft) long; it was a technical sensation when constructed; photos at the entrance show Kaiser Wilhelm II dedicating it.  It connected central Hamburg on the north side of the river with the docks and shipyards on the south side of the river Elbe.  The most amazing part was the four massive elevators, capable of carrying bikes and whole vehicles, and of course, 100 years ago, carriages and horses.  These elevators and tunnel are still functional and being used today!

While pondering these thoughts, I collected a track in the Runkeeper app, and mapped it as a GPX file in ArcGIS Online as a 2D webmap and as a shapefile in a 3D scene.  I wanted to test how spatially accurate a track underwater would be, in the x and y dimensions, but also in the z dimension.  First, let’s consider the x and y:  As I walked through the tunnel 24 m (80 ft) beneath the surface through one of the two 6 m (20 ft) diameter tubes, I expected the my app to lose sight of the GPS, Wi-Fi, and cell phone towers, but I did not know how far off my position would be.   My recent experiments on an above-ground track gave me a ray of hope that perhaps my position would be recorded as somewhere in Germany rather than in the North Sea or the Atlantic Ocean.

I was told by a local source who said that the tunnels are 8 m below the bottom of the river, making the water 16 m deep here (this depth here allowed Hamburg to become of the largest container ports in the world).  Thus, above me was 8 m of sediment (glacial, in this area), and 16 m of water for a total of 24 meters above me.  The elevation at the water surface here is approximately 5 m above sea level.  Thus, my elevation in the tunnel should be 5 – 24 = -19 meters, minus 3 more meters because I was standing on the bottom of the tunnel rather than the top, so -22 meters.

My results as a 2D webmap and as a 3D scene are shown below.  As is evident, the recorded elevations are all above sea level, at around 4 or 5 meters, so they were 22+5=27 meters off of my tunnel elevation.


A 2D map in ArcGIS Online showing the results of my experiment, with elevations in meters above sea level shown as labels. 

Feel free to open and interact with the data!  For example, to test the X and Y:  Using the measure tool, measure the distance between the tunnel as shown on the OpenStreetMap basemap and the position recorded by my track.  As I left the train station on the north side, my position was fairly accurately recorded, but once I descended the stairs into the tunnel, my position was off to the east by about 140 meters, and then shifted to the west and was off by about 240 meters.  But as I continued walking south, for the last 1/3 of my trek through the tunnel, my XY positional accuracy was only off by 50 meters.   I ascended the stairs and circled the parking lot on the south side, and was only 1 to 2 meters off once more.  I descended into the tunnel and walked north.  This time, my position was about 100 meters off, becoming worse as I kept walking.  My position overcorrected 80 meters to the north as I ascended the stairs, and “settled back” to being a few meters off as I walked to the train station.

To test the Z position:  The elevations were, as I suspected, not displaying their correct number below sea level; that is, 19 meters below sea level. However, you can see that the elevations are actually quite close to the elevation of the surface of the river in this area; at about 4.5 meters.


A 3D scene in ArcGIS Online showing the results of my experiment, with elevations in meters above sea level shown as labels and symbolized as cylinders.   Feel free to open and interact with this 3D scene!

Overall, with only a smartphone and a fitness app, displaying the data in ArcGIS Online, I was rather pleased with the fact that my positions all around were usually only in the tens or a few dozen meters off of true. This aligns with my recent reports of above-ground experiments and is further evidence of the improvements in spatial accuracy with all location based services.

Interested in further exploration?  See the evidence of my field trip in the photographs below.

tunnel1.jpgThe enormous elevators that carry pedestrians, bicycles, and even vehicles from the street level to the level of the tunnels.  This one is at the north side of the river with a photo of the opening ceremony with Kaiser Wilhelm II dedicating it.

tunnel2Standing at the entrance to the tunnel; photo also shows one of the glazed terra cotta art sculptures.

Now, go conduct your own accuracy experiments!

–Joseph Kerski


Be Critical of the Data–Especially When it is Your Own!

July 26, 2015 3 comments

A theme running throughout our book The GIS Guide to Public Domain Data is to be critical of the data that you are using–even data that you are creating.  Thanks to mobile technologies and the evolution of GIS to a Software as a Service (SaaS) model, anyone can create spatial data, even from a smartphone, and upload it into the GIS cloud for anyone to use.  This has led to incredibly useful collaborations such as Open Street Map, but this ease of data creation means that caution must be employed more than ever before, as I explain in this video.

For example, analyze a map that I created using Motion X GPS on an iPhone and mapped using ArcGIS Online.  It is shown below, or you can interact with the original map if you prefer.  To do so, access (ArcGIS Online) and search for the map entitled “Kendrick Reservoir Motion X GPS Track” or go directly to  Open the map.  This map shows a track that I collected around Kendrick Reservoir in Colorado USA.  This map was symbolized on the time of GPS collection, from yellow to gradually darker blue dots as time passed.

GPS track around Kendrick Reservoir

GPS track around Kendrick Reservoir.

Note the components of the track to the northwest of the reservoir. These pieces were generated when the smartphone was just turned on and the track first began, indicated by their yellow color.  They are erroneous segments and track points.  Notice how the track cuts across the terrain and does not follow city streets or sidewalks.  Change the base map to a satellite image.  Cutting across lots would not have been possible on foot given the fences and houses obstructing the path. When I first turned on the smartphone, not many GPS satellites were in view of the phone.  As I kept walking and remained outside, the phone recorded a greater number of GPS satellites, and as the number of satellites increased, the triangulation was enhanced, and the positional accuracy improved until the track points mapped closely represented my true position on the Earth’s surface.

Use the distance tool in ArcGIS Online to answer the following question: How far were the farthest erroneous pieces from the lake? Although it depends on where you measure from, some of the farthest erroneous pieces were 600 meters from the lake.  Click on each dot to access the date and time each track point was collected.  How long did the erroneous collection continue?  Again, it depends on which points you select, but the erroneous components lasted about 10 minutes.  At what time did the erroneous track begin correctly following my walk around the lake? This occurred at 11:12 a.m. on the day of the walk.  [Take note of the letters I drew along the southwest shore of the reservoir!]

This simple example points to the serious concern about the consequences of using data without being critical of its source, spatial accuracy, precision, lineage, date, collection scale, methods of collection, and other considerations.  Be critical of the data, even when it is your own!

BioBlitz and Citizen Science: Implications for Data Users

August 27, 2012 Leave a comment

During the past few days, I had the opportunity to participate in BioBlitz 2012 at Rocky Mountain National Park, Colorado, USA.  BioBlitz ( is a 10-year partnership between the US National Park Service and National Geographic with 3 goals:  Highlight the diversity of national parks by conducting a taxonomic inventory, public outreach, and to inspire young people to pursue careers in science and geography.  The citizen science focus to the event reinforced the concepts that Jill Clark and I wrote about in the book The GIS Guide to Public Domain Data.   Nowhere was this clearer than when I went into the field to collect and categorize macroinvertebrates in a montane stream in the shadow of Longs Peak with 40 students aged 11 to 13.

Collecting macroinvertebrates in stream, Rocky Mountain National Park

Collecting macroinvertebrates in stream, Rocky Mountain National Park.

After collecting the data over a period of five hours, the macroinvertebrate data was then identified by the students according to a detailed classification chart.  I was very impressed by the students’ diligence and teamwork.  The data was  then  input into a web-GIS called FieldScope, created by National Geographic and based in part on Esri technology, and viewable that evening online by anyone on the web.

Citizen science data being input into FieldScope

Citizen science data being input into FieldScope.

All told, hundreds of students, over 100 scientists, and thousands of the general public collected data for two days, resulting in over 400 bird, fungi, macroinvertebrate, animal, and vascular plant species that had never been documented in this particular national park before.

As citizen science projects gain in popularity, enabled by powerful yet easy-to-use web-GIS and field collection instruments, the challenge becomes:  How can data collected by a wide variety of people with a wide variety of backgrounds be managed and cataloged in such a way that is not only useful, but also, through metadata, allows people to understand who collected it, and how it was collected, categorized, and input into the GIS?