eBird App GPS Accuracy

Motivation

In the summer of 2017, eBird introduced the ability to record tracks — GPS points recorded at regular intervals over the course of collecting observations for a checklist — in the Android version of the eBird app.  GPS units, especially consumer-grade GPS units like those found in smartphones, are not perfectly accurate.  As a result, the records of distances travelled will not be entirely accurate.  I wanted to understand whether there are systematic sources of error in GPS travel distances, in order to be able to better use the GPS information being recorded during the analyses of eBird data.

 

The Take-Home Messages

Based on analyses of a set of data that I gathered, over 1000 data points from 2 Android smartphones, here is what I have learned about sources of error in the GPS travel distances being recorded.  First here are some general conclusions:

Now, here are some specific findings for the two phones that I have been using:

There are also a few other observations that I have, based on paying attention to the GPS data, and looking at the tracks that I have produced:

While I think that these conclusions are generally valid, I think that it would be useful to look at the details regarding the data and analyses on which I am basing the above statements.  So, keep reading…

 

Collecting the Data

The specific, quantitative details of my findings depend on the range of conditions under which I collected the information on which I am basing my conclusions.  Conclusions about accuracy  outside of those conditions can be extrapolated, but there’s no way of knowing how appropriate those extrapolated conclusions are.  Probably the most relevant limitations are that the information comes from relatively short-duration counts on which I neither travelled fast nor far.  Here are what I think are the most relevant details about how I collected the information:

This information is accurate as of the data that I collected up to the end of 12 July 2017.

 

Analysing the Data

The previous section describes the raw materials that went into my analyses. Now, here are the gory details of the statistical analyses whose output I used to make my conclusions:

The variables whose names are in grey were only used in preliminary analyses.  All other predictor variables were used in the final model on which I based the conclusions listed further up on this page.  Here are the estimated effects of the fixed-effect predictor variables in the final model, and the statistical probabilities that the effects would not be observed by chance alone:

Predictor

Variable

Parameter

Estimate

Std. Error

P-value

Intercept -0.0023 0.0028 0.40
Count Duration (min) 0.0002 0.0002 0.19
Distance (km) -0.0043 0.0086 0.62
Speed (km/hr) 0.0010 0.0012 0.43
Stationary Count (yes) 0.0038 0.0025 0.13
Phone (Nexus 5X) 0.0050 0.0009 0.00000003