A frame work for analysing tracking data – applications in recreation planning Hans Skov-Petersen...

A frame work for analysing tracking data – applications in recreation planning

Hans Skov-Petersen (hsp@life.ku.dk)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 (hsp@life.ku.dk)Forest & LandscapeUniversity of Copenhagen