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Analyzing Tobler’s Hiking Function and Naismith’s Rule Using Crowd-Sourced GPS Data

Analyzing Tobler’s Hiking Function and Naismith’s Rule Using Crowd-Sourced GPS Data. Association of American Geographers Annual Meeting April 9, 2014. Erik Irtenkauf , Master’s Candidate The Pennsylvania State University. Background. Terrain has a big effect on human movement

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Analyzing Tobler’s Hiking Function and Naismith’s Rule Using Crowd-Sourced GPS Data

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  1. AnalyzingTobler’s Hiking Function and Naismith’s RuleUsing Crowd-Sourced GPS Data Association of American Geographers Annual Meeting April 9, 2014 Erik Irtenkauf, Master’s Candidate The Pennsylvania State University

  2. Background • Terrain has a big effect on human movement • Modeling movement is important • Helps explain how humans interact with our environment • Two common methods in Geography/GIS • Tobler’s Hiking Function • Naismith’s Rule

  3. Tobler and Naismith • Both methods estimate walking speed/time based on slope • Dr. Waldo Tobler published his hiking function in 1993, based on empirical data from Imhof (1950) • Naismith’s Rule developed by mountaineer William Naismith in 1892, amended by Langmuir • Used for: • archaeology • recreation • resource management • public safety

  4. Methodology Goal: Analyze both rules using hiking GPS tracks shared on the internet • Methodology: • Download a sample of 120 GPS tracks from www.wikiloc.com • Model Tobler and Naismith in a GIS to calculate predicted hiking times for each track • Analyzepredicted vs. actual hiking times

  5. Crowd-Sourced GPS Data • Offers the chance to quickly gather data from a diverse range of environments and conditions :

  6. Findings • Predicted times for each method are strongly correlated across ecoregion divisions: Marine Regime Mountains .98 Warm Continental Regime Mountains .98 .99 Temperate Steppe Regime Mountains Hot Continental Division Mountains .99 Correlation Between Tobler and Naismith Predicted Hiking Times

  7. Findings • Accuracy ranges can be determined • Predicted times are generally accurate

  8. Findings • Accuracy varies across ecoregion divisions • Available data does not fully explain these differences 28.34 23.52 Warm Continental Regime Mountains 16.14 16.84 22.02 21.34 Marine Regime Mountains Temperate Steppe Regime Mountains 17.69 17.18 Hot Continental Division Mountains Tobler Naismith Average Difference (%) Between Predicted and Actual Times

  9. Conclusions • Both models work well, with some caveats • Crowd-sourced GPS data is a rich data source • Lack of additional information limits usefulness • Questions remain about generalizing this sample to a larger population

  10. Erik Irtenkauf, Master’s Candidate The Pennsylvania State University eji107@psu.edu irtenkauf@gmail.com Project Advisor: Dr. Doug Miller Permission to use this project data was obtained from www.wikiloc.com, their contribution is gratefully acknowledged.

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