Upload
emily-morris
View
225
Download
1
Tags:
Embed Size (px)
Citation preview
AnalyzingTobler’s Hiking Function and Naismith’s
RuleUsing
Crowd-Sourced GPS Data
Erik Irtenkauf, Master’s CandidateThe Pennsylvania State University
Association of American Geographers Annual MeetingApril 9, 2014
Background• Terrain has a big effect on human movement
• Modeling movement is importanto Helps explain how humans interact with our environment
• Two common methods in Geography/GISo Tobler’s Hiking Functiono Naismith’s Rule
Tobler and Naismith
-70 -50 -30 -10 10 30 50 700
1
2
3
4
5
6
7
Naismith-Langmuir Tobler
Slope (Degrees)
Velo
cit
y (k
m /
hr)
• 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
MethodologyGoal: 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
• Analyze predicted vs. actual hiking times
-70 -50 -30 -10 10 30 50 70
0
2
4
6
8
10
12
14
Slope (Degrees)
Velo
city
(K
m/H
r)
Crowd-Sourced GPS Data• Offers the chance to quickly
gather data from a diverse range of environments and conditions :
Spring Summer Fall Winter
20
55
37
8
Tracks by Season
2006 2007 2008 2009 2010 2011 2012 2013
14
11
1815
21 21
29Tracks by Year
Developed
Barren
Deciduous F
orest
Evergr
een Fo
rest
Mixed Fo
rest
Scrub/Sh
rub
Grassla
nd0
10
20
30
40
50
60
70
80Tracks by Land Cover Type
Marine
Warm Cont.
Hot Cont.
Temp. Steppe
• Predicted times for each method are strongly correlated across ecoregion divisions:
MarineRegime
Mountains
TemperateSteppeRegime
MountainsHot
ContinentalDivision
Mountains
WarmContinentalRegime
Mountains
.98.98
.99
.99
Correlation BetweenTobler and Naismith
Predicted Hiking Times
Findings
• Accuracy ranges can be determinedo Predicted times are generally accurate
Findings
35%
70%93%
• Accuracy varies across ecoregion divisionso Available data does not fully explain these differences
MarineRegime
Mountains
TemperateSteppeRegime
MountainsHot
ContinentalDivision
Mountains
WarmContinentalRegime
Mountains
28.3423.52
Average Difference (%)Between Predicted and Actual Times
Findings
22.0221.34
17.6917.18
16.1416.84
ToblerNaismith
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
Erik Irtenkauf, Master’s CandidateThe Pennsylvania State University
[email protected]@gmail.com
Project Advisor: Dr. Doug Miller
Permission to use this project datawas obtained from www.wikiloc.com,
their contribution is gratefully acknowledged.