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Mobile computing and global positioning technology are in widespread use in forestry and environmental field sampling work. Improvements in hardware cost, computing speed and memory, and reliability of GPS in extreme conditions have advanced the prevalence of field computing for natural resource organizations. Global positioning systems and sampler metadata from proprietary software currently used in some forestry sampling efforts permit network analysis of how work was completed in terms of field sample travel sequences. Preliminary analysis using a beta field software release indicated that field sampler travel distances may be excessive compared to those that have been optimized for distance using network analysis tools.
This study focused on whether network analysis of actual field sample travel sequences would reveal a statistically significant inefficiency compared to optimized travel for the same samples. Two projects, using software which recorded GPS time and date and sampler name for all sample visits, provided data to examine sampler travel distances. Working datasets consisted of 8,852 sample points visited by a variety of samplers with a range of experience in performing field sampling. Sample datasets were prepared for networks creation in order to model actual and optimized travel distances. Resulting empirical (actual) and optimized scenarios were compared to each other to examine travel distance changes through classical hypothesis testing. The conclusion is that a 14% reduction in travel distancing using optimization is statistically significant indicating that field sampling software may benefit from tools which aid travel distance optimization.