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

Hans Skov-Petersen Forest & Landscape University of Copenhagen

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**Program of the presentation**

Agenda People and places Local and focal… description and inference: a proposed analytical framework Choice modelling: Data model and scope Examples from the working days assignment

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**People or places? Places … and people**

(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

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**Analytical framework Description Inference Locations only**

Additional layers Local Individual points Where is (x, y)? What is the PDOP of..? Distance to paths’ and points of interest. Where do stops occur? Focal Spatial/temporal How fast? Stop/go? How steep? Speed/slope relations Choice of ‘next point’ (relative to options) Zonal Single track/tours/routs How far? Round trip? Average speed? Altitude difference? Min/max altitude along a track Land cover distribution Choice of route (relative to options) Global All tours, for an individual or all respondents Data mining Spatial/temporal clustering Area of interest Path pressure Kernel distribution Relation of congested locations

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**Assignment Base application Written in Python**

Main imports: ORG (vector handling) and GDAL (raster handling) Reads and handles GPS tracks from one - or all shape files in a folder Reads rasters in ArcInfo ASCII format Sorts points in temporal order Breaks up points into subtracks according to the stops indicated in the material Avoids subtracks with less than xx points (in the present example: 50) Writes results in comma separated files

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**Assignment: Application I Zonal Statistics**

Description Inference Locations only Additional layers Local Individual points Where is (x, y)? What is the PDOP of..? Distance to paths’ and points of interest. Where do stops occur? Focal Spatial/temporal How fast? Stop/go? How steep? Speed/slope relations Choice of ‘next point’ (relative to options) Zonal Single track/tours/routs How far? Round trip? Average speed? Altitude difference? Min/max altitude along a track Land cover distribution Choice of route (relative to options) Global All tours, for an individual or all respondents Data mining Spatial/temporal clustering Area of interest Path pressure Kernel distribution Relation of congested locations

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**Assignment: Application I Zonal statistics**

Etc, etc….

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**Assignment: Application II Path pressure**

Description Inference Locations only Additional layers Local Individual points Where is (x, y)? What is the PDOP of..? Distance to paths’ and points of interest. Where do stops occur? Focal Spatial/temporal How fast? Stop/go? How steep? Speed/slope relations Choice of ‘next point’ (relative to options) Zonal Single track/tours/routs How far? Round trip? Average speed? Altitude difference? Min/max altitude along a track Land cover distribution Choice of route (relative to options) Global All tours, for an individual or all respondents Data mining Spatial/temporal clustering Area of interest Path pressure Kernel distribution Relation of congested locations

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**Assignment: Application II Path pressure**

…. Just an average GIS analysis.

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**Assignment: Application III Speed/Slope**

Description Inference Locations only Additional layers Local Individual points Where is (x, y)? What is the PDOP of..? Distance to paths’ and points of interest. Where do stops occur? Focal Spatial/temporal How fast? Stop/go? How steep? Speed/slope relations Choice of ‘next point’ (relative to options) Zonal Single track/tours/routs How far? Round trip? Average speed? Altitude difference? Min/max altitude along a track Land cover distribution Choice of route (relative to options) Global All tours, for an individual or all respondents Data mining Spatial/temporal clustering Area of interest Path pressure Kernel distribution Relation of congested locations

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**Assignment: Application III Speed/Slope**

ShapeFile SubTrack Distance FromID ToID Speed Slope trip_000157_ shp 1 3.72 3.78 5.71 3.95 5.37 4.11 6.85 4.20 7.06 4.30 7.31 4.52 7.39 4.99 6.69 5.29 6.74 5.65 6.76 6.06 6.78 6.51 6.84 7.05 6.88 7.62 7.01 8.00 8.16 6.56 8.28 6.46 8.44 10.40 8.80 9.16

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**Assignment: Application IV Revealed Choice experiment**

Description Inference Locations only Additional layers Local Individual points Where is (x, y)? What is the PDOP of..? Distance to paths’ and points of interest. Where do stops occur? Focal Spatial/temporal How fast? Stop/go? How steep? Speed/slope relations Choice of ‘next point’ (relative to options) Zonal Single track/tours/routs How far? Round trip? Average speed? Altitude difference? Min/max altitude along a track Land cover distribution Choice of route (relative to options) Global All tours, for an individual or all respondents Data mining Spatial/temporal clustering Area of interest Path pressure Kernel distribution Relation of congested locations

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**Assignment: Application IV Revealed Choice experiment**

A single point It’s alternatives All points and alternatives A different location And it’s alternatives Sampling distance at next point

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**Assignment: Application IV Revealed Choice experiment**

Resulting file

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**Assignment: Application IV Revealed Choice experiment**

Result from logit regression Very, very preliminary results!!!

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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).

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**Revealed Path Preference (local perception)**

Proposed framework: Procedural steps Finding the stops and routes Calculating route index Finding choice locations

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**Revealed Path Preference (local perception)**

Proposed framework: Choice locations Example: Traffic load Vegetation Direction Identification Choice attributes Choice Choice location Edge ID 1: Traffic load 2: Vegetation 3: Direction 4. Shortest path 1 23 1.21 26 2 1.65 12 1.00 89 3 2.30 1.12 11 17 1.43 44 2.70 Etc.

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**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’

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**Stated Route Preference**

(choice experiment) Eksempel på et valg-eksperiment (stated preference – altså hvad folk siger de vil vælge.. 20

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A frame work for analysing tracking data – applications in recreation planning That’s it Thank you for your attention Hans Skov-Petersen Forest & Landscape University of Copenhagen

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