Optimizing audit-logging parameters for position data

Hello KoBo Community,

We have just completed our first field campaign using the KoBo tool suite that employs the audit-logging feature. For this project, we are measuring plant characteristics in study plots at desert sites around the greater Phoenix, Arizona, USA metropolitan area. We had planned to use the audit-logging location data to address data-entry errors in the field (i.e., to use the logged positions to verify positions reported on survey forms). However, success was mixed with background logging of position data seeming to work quite well at some locations but poorly or non-existent at others. Accuracy was never really a problem, rather the issue was that logging did not seem to register at some locations. Survey work was spread across multiple days employing several Android tablets, and there does not seem to be any correlations among logging success based on day or device.

Our logging parameters are set as:
track-changes=true location-priority=balanced location-min-interval=300 location-max-age=600.

Here is an example where logging worked quite well. For this survey, field technicians are in each of the four survey plots (blue polygons) for ten to twenty minutes. In this map, the blue polygons are the survey locations and the green points are positions reported in the audit log.

Conversely, there are other examples where logging performed poorly (i.e., logged position data were sparse). Again, technicians would have spent ten to twenty minutes in each of the four plots (blue polygons) but there are very few logged points (green dots), and none at all in many of the sampled plots. To clarify, data collection worked fine and was never a problem, and we have data from all four plots as entered by the technicians, it is only the logged position data that are lacking.

I assume this issue might be related to the parameters we have set for auditing but it is not clear which parameters to tweak to resolve this. Ideally, we would prefer a relatively higher rater of logging to ensure that we have logged data in each of the study plots where technicians are surveying, and would prioritize robust logging over accuracy.

Thanks for any advice!


Welcome back to the community, @srearl! GPS performance is always affected by the weather (precision should be good on a sunny day compared to a cloudy day). In some cases, the device itself (a premium device should be able to capture the GPS better than a cheaper one) should also affect the precision. Besides the weather, the obstructions (tall trees in a jungle covering the sky when collecting GPS data) should also affect the accuracy.

Apart from that, the parameter used, location-priority=balanced should also affect the precision. In your case, maybe you will also need to try location-priority=high-accuracy. For detailed settings, refer to this documentation here.

Our experienced community should also be able to provide you with their valuable experiences to tackle this problem.