Issue 65

Geospatially Analyze Sports Performance in Surfer 10 and Didger 4

You can use Golden Software’s Didger 4 and Surfer 10 to geospatially analyze sports performance data acquired by a GPS-enabled sports watch. With Didger 4 you can digitize graphical sports performance data and resample it so that it matches the associated latitude and longitude GPS tracks. Then use Surfer 10’s worksheet to import and pair the resampled data from Didger with the associated Lat/Long GPS tracks. Using the processed data from the Surfer worksheet, you can create a classed post map in Surfer using the longitude and latitude values from the GPS tracks as your X and Y values, and using the dependent variable from your graphical data as the Z value, which will group the data into discrete classes. Next, use Surfer to create a 3D surface map of the area from which the data was taken. Last, overlay the classed post layer onto the 3D surface map from the area that it was taken, so that you can geospatially analyze athletic performance. I will demonstrate how to create a 3D surface map that is overlain with a post layer that is classed by speed, using cycling data that was recorded with a Garmin GPS-enabled sports watch as my example.

This process was detailed in full in three articles on Golden Software’s blog. See the blog articles for full step by step details.

Acquiring the data from the GPS

Training Center exports GPS tracks as a GPX file. In order to use the GPS tracks in Surfer 10, you must convert the GPX file to an ASCII data file. Use GPSies.com to convert the exported GPX file from Training Center to a CSV file.

Once I am finished digitizing, I will select the line in the Data Manager and select Tools | Resample Polyline. In theResample Polyline dialog I will select Resample Along the X Axis. I will change the Starting Value: to 0, the Ending value: to 17.3, and the Increment Value: to .04209, and will then select OK. This will ensure that I have 412 data points to correspond with the 412 GPS points.

I can now select the newly created polyline in the Data Manager and export it as a DAT file so that I can import it into the Surfer worksheet.

Editing the data in Surfer

Creating the maps in Surfer

Now that I have finished processing my data in the Surfer worksheet, I need to acquire geographic data for Boulder, CO so that I can build a 3D surface map in Surfer 10. The United States Geologic Survey (USGS) provides its Seamless Vieweras a tool to locate and download free geographic data. Using a web browser, I will visit seamless.usgs.gov, and use the USGS Seamless Viewer to download a National Elevation Dataset (NED) of Boulder, CO. Once the NED of Boulder, CO is downloaded, I can use any of the associated ADF files to create a 3D surface map in Surfer 10.

Once I’ve created a 3D surface map of Boulder, I am now ready to use the processed data from the Surfer worksheet to create and overlay a classed post layer. To class my data by speed, I must select the classed post layer in the Object Manager and then in the Property Manger select the General tab and expand the Worksheet Columns section and change the Z coordinates to Column E: Speed.

I have now successfully paired graphical sports performance data with its associated GPS tracks in the Surfer worksheet. From this data I was able to create a classed post map, which is based on speed, binned into three categories, and overlain onto a 3D surface map of the area from which the data was taken.

Conclusion

I have demonstrated how to use Didger 4 to spatially reference, calibrate, digitize, resample, and export sports performance data acquired by a GPS-enabled sports watch to a DAT file. Using Surfer 10’s worksheet, I have demonstrated how to import, process, and pair GPS tracks with its related sports performance data set. Last, I exhibited the ability to create a classed post map from the combined data set that is classed by speed into three bins, and demonstrated the ability to overlay this map onto a 3D surface map. I can now use this 3D surface map, overlain with the classed post layer to geospatially analyze athletic performance based on speed. By observing the color-coded “tracks,” I can identify areas where athletic performance, based on speed, was high (green), medium (deep yellow), or low (red). Utilizing the 3D surface map I can gain a better perspective as to what may have caused either a dip or rise in performance. I can geospatially analyze the GPS tracks to identify where performance was affected by hills, wind and weather conditions, duration, and more.

With the power of Golden Software’s Didger 4 and Surfer 10 you have the ability gain exceptional insight into sports performance through the geospatial analysis of a classed post map derived from sports performance data acquired by a GPS-enabled sports watch, overlain onto a 3D surface map.