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A frame work for analysing tracking data – applications in recreation planning
Hans Skov-Petersen ([email protected])Forest & LandscapeUniversity of Copenhagen
Program of the presentation
Agendaa) People and placesb) Local and focal… description and inference: a proposed
analytical frameworkc) Choice modelling: Data model and scoped) Examples from the working days assignment
People or places?
Places• (Temporal profiles of..) loads on infrastructure• (Temporal profiles of..) loads on places (entries, points of interest, areas)• Where/when encounters occur
… and people• Behavior and activities (speed, stops, duration, distances)• Preferences… where do they go?• Choices… taking alternatives into account
Source: Van der Spek
Analytical framework
Description Inference
Locations only Additional layers
LocalIndividual points
Where is (x, y)?What is the PDOP of..?
Distance to paths’ and points of interest.
Where do stops occur?
FocalSpatial/temporal
How fast?Stop/go?
How steep? Speed/slope relationsChoice of ‘next point’ (relative to options)
ZonalSingle track/tours/routs
How far?Round trip?Average speed? Altitude difference?
Min/max altitude along a trackLand cover distribution
Choice of route (relative to options)
GlobalAll tours, for an individual or all respondents
Data mining Spatial/temporal clusteringArea of interest
Path pressureKernel distribution
Relation of congested locations
Assignment
Base applicationa) Written in Pythonb) Main imports: ORG (vector handling) and GDAL (raster handling)c) Reads and handles GPS tracks from one - or all shape files in a folderd) Reads rasters in ArcInfo ASCII formate) Sorts points in temporal orderf) Breaks up points into subtracks according to the stops indicated in the
materialg) Avoids subtracks with less than xx points (in the present example: 50)h) Writes results in comma separated files
Assignment: Application IZonal Statistics
Description Inference
Locations only Additional layers
LocalIndividual points
Where is (x, y)?What is the PDOP of..?
Distance to paths’ and points of interest.
Where do stops occur?
FocalSpatial/temporal
How fast?Stop/go?
How steep? Speed/slope relationsChoice of ‘next point’ (relative to options)
ZonalSingle track/tours/routs
How far?Round trip?Average speed? Altitude difference?
Min/max altitude along a trackLand cover distribution
Choice of route (relative to options)
GlobalAll tours, for an individual or all respondents
Data mining Spatial/temporal clusteringArea of interest
Path pressureKernel distribution
Relation of congested locations
Assignment: Application IZonal statistics
Etc, etc….
Assignment: Application IIPath pressure
Description Inference
Locations only Additional layers
LocalIndividual points
Where is (x, y)?What is the PDOP of..?
Distance to paths’ and points of interest.
Where do stops occur?
FocalSpatial/temporal
How fast?Stop/go?
How steep? Speed/slope relationsChoice of ‘next point’ (relative to options)
ZonalSingle track/tours/routs
How far?Round trip?Average speed? Altitude difference?
Min/max altitude along a trackLand cover distribution
Choice of route (relative to options)
GlobalAll tours, for an individual or all respondents
Data mining Spatial/temporal clusteringArea of interest
Path pressureKernel distribution
Relation of congested locations
Assignment: Application IIPath pressure
…. Just an average GIS analysis.
Assignment: Application IIISpeed/Slope
Description Inference
Locations only Additional layers
LocalIndividual points
Where is (x, y)?What is the PDOP of..?
Distance to paths’ and points of interest.
Where do stops occur?
FocalSpatial/temporal
How fast?Stop/go?
How steep? Speed/slope relationsChoice of ‘next point’ (relative to options)
ZonalSingle track/tours/routs
How far?Round trip?Average speed? Altitude difference?
Min/max altitude along a trackLand cover distribution
Choice of route (relative to options)
GlobalAll tours, for an individual or all respondents
Data mining Spatial/temporal clusteringArea of interest
Path pressureKernel distribution
Relation of congested locations
Assignment: Application IIISpeed/Slope
Assignment: Application IVRevealed Choice experiment
Description Inference
Locations only Additional layers
LocalIndividual points
Where is (x, y)?What is the PDOP of..?
Distance to paths’ and points of interest.
Where do stops occur?
FocalSpatial/temporal
How fast?Stop/go?
How steep? Speed/slope relationsChoice of ‘next point’ (relative to options)
ZonalSingle track/tours/routs
How far?Round trip?Average speed? Altitude difference?
Min/max altitude along a trackLand cover distribution
Choice of route (relative to options)
GlobalAll tours, for an individual or all respondents
Data mining Spatial/temporal clusteringArea of interest
Path pressureKernel distribution
Relation of congested locations
Assignment: Application IVRevealed Choice experiment
A single pointIt’s alternativesAll points and alternativesA different locationAnd it’s alternativesSampling distance at next point
Assignment: Application IVRevealed Choice experiment
Resulting file
Assignment: Application IVRevealed Choice experiment
Result from logit regressionVery, very preliminary results!!!
Data models and scope
Scope of choice
Focal Global
Data model
Vector Actual choice vs alternative edges at choice locations (junctions) in an infrastructure
Actual choice vs alternative routes through an infrastructure
Raster Actual choice (locations) vs alternatives ‘in field’
Actual choice (route) vs alternatives ‘in field’ (e.g. a corridor).
Revealed Path Preference (local perception)
Proposed framework:• Procedural steps • Finding the stops and routes
• Calculating route index• Finding choice locations
Revealed Path Preference (local perception)
Proposed framework:• Choice locations
Identification Choice attributesChoice
Choice location
Edge ID
1:Traffic
load
2: Vegetatio
n
3: Directio
n
4.Shortest
path
1 23 1 1 1 1.21 0
1 26 1 2 2 1.65 1
1 12 2 2 2 1.00 0
1 89 3 2 3 2.30 0
2 26 1 2 2 1.12 1
2 11 1 1 1 1.00 0
2 17 3 3 3 1.43 0
3 17 3 3 3 1.00 0
3 44 3 3 1 2.70 1
Etc.
Example:• Traffic load• Vegetation• Direction
Revealed Path Preference (global knowledge)
For each route (path between origon and destinations) of the GPS survey:
• Aggregates of the different attributes (e.g. Length, percentage of the route with bicycle lane, etc.) will be compiled for a number of alternative trips (e.g. all paths’ shorter than the double of the shortes possible)
• The path actually taken will be statistically compared to the set of alternative paths’
• Hereby the effect of the attributes can be assessed for entire paths’
Stated Route Preference (choice experiment)
A frame work for analysing tracking data – applications in recreation planning
That’s itThank you for your attention
Hans Skov-Petersen ([email protected])Forest & LandscapeUniversity of Copenhagen
