methodological issues around gps and cycling

8/3/2019 Methodological issues around GPS and Cycling

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The Forge Mini Symposium: 'Transport and Well-Being:

Connecting Research Agendas'.

Godwin YeboahNorthumbria University

School of the Built & Natural Environment

METHODOLOGICAL ISSUESAROUND GPS AND CYCLING

Monday 30 January 2012,

Politics Department, University of Sheffield

Presentation by

Godwin Yeboah

Overview

Brief background of research Research question and one of the objectives

Why primary data collection? Data collection and methodological issues Tracked sample size

Actual data collection

Data challenges

Anonymization of data

Brief results of everyday cycling travel survey Results 1 - Outcome of fieldwork survey

Preliminary Results 2 Visualization of data using GeoTime,ArcGIS & Excel

Concluding remarksGodwin Yeboah, Northumbria University


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Brief background of research

PhD research title: Constraints and enablers for cycling

in urban environments: An integrated model approach Main research question: How does the built environment

constrain or support active transport - especially cycling?

One of the objectives: collects primary data on cyclists

perception and actual route choice experiences and

preferences using GPS devices and diaries

Supervisors: Dr. Seraphim Alvanides & Dr. Emine

Mine Thompson

Funding: Northumbria University ScholarshipGodwin Yeboah, Northumbria University

Why primary data collection?

Secondary data is aggregated or not detailed

enough (e.g. census data; surveys; more recently DfT)

Lack of detailed quality data affects research ofthis nature.

To make available new data on actual and revealed

route choice preferences of utility cyclists within the

research area; not existing previously.

To enable further research towards understandingconstraints and enablers for cycling; especially in

relation to transport and (indirectly) well-being.Godwin Yeboah, Northumbria University


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Tracked sample size

UK National Travel Survey (NTS) GPS Feasibility study (DfT)

The fieldwork was done in two waves; 66 adults in one wave(October-November) and 68 adults in the second wave (January-March). In all 96 adults were interviewed face-to-face across thetwo waves for the NTS study.

TU Deft project in the town of Almere

15 families initially agreed to participate. However, in the end,40 participants out of 13 families from three neighbourhoodsparticipated in the study by carrying GPS devices for one week.

This work (Northumbria project within Tyneside conurbation): One wave: October-November 2011

118 initially agreed to participate.

In the end: 81 participants out of 111utility cyclists

Godwin Yeboah, Northumbria University

Evaluation, procurement & preparation

of survey instruments

Evaluation: Four GPS devices were evaluated: i-gotU GT-600; Atmel

BTT08; Canmore GT-750 (L); and Qstarz BT-Q1000XT

Procurement: 54 Qstarz BT-Q1000XT

Survey instruments

GPS devices

Questionnaire forms and travel diaries; partly adopted from

(Lemieux & Godin, 2009; DfT, 2011)

Three web pages were designed using Survey Monkey web service

Consent form and research statement (in accordance with

Northumbria University policy on Ethics)

A leaflet containing frequently asked questions (FAQ) as well as

important issues on the use of the GPS device

Flyers, brochures and postersGodwin Yeboah, Northumbria University


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Field campaign (in September 2011)

Online

Email was sent to 350 potential cyclists (included email-lists of bicycle user groups of both Northumbria and

Newcastle Universities, and other face-to-face contacts)

Twitter services

Offline

Posters with flyers on notice boards on campus

Distribution of flyers and brochures to potential cyclistsat cycling related events


Actual data collection GPS & diaries

Use of SPSS and Excel to extract three-stage

representative samples from sample (due to limited

GPS devices and time factor); 10 45 56

Participants comments on use of GPS & diaries

Participant ID-1010XT11600002: No problems

whatsoever. All smashing.

Participant ID-1480XT11500947: No problem

into day 2 cycled in today despite the weather



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Actual data collection GPS & diaries

Add your procedure here

Key assumptions

Add your assumptions here

Work done (update) and planned workWhat is a messy dataset? its Meaning? Structure? Scale? Noise?


Another example of a messy dataGodwin Yeboah, Northumbria University


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The Challenge idea:

To isolate cycling paths from messy GPS tracks withoutany or few additional information such as an example of

each participants cycle track.

How to implement this idea

As a contribution/proposal to call for contribution in

Workshop on Future Research Directions in MOVEment:

COST IC0903 MOVE meeting, 1516 March 2012, TU

Delft, Netherlands

Data challenge


Anonymization of data

Central reality of data collection: data can either be useful or

perfectly anonymous but never both.

-- Law professor Paul Ohm: Source; http://arstechnica.com/tech-policy/news/2009/09/your-secrets-live-online-in-databases-of-ruin.ars

Challenge: Balancing usefulness of data and anonymity

Suggestions for anonymizing GPS-generated data

100m(?) buffer around home, work or school locations?

Publishing maps without detailed basemap & coordinate system?

For example maps shown in Van der Spek, S., Van Schaick, J.,

De Bois, P. & De Haan, R. (2009) 'Sensing Human Activity: GPSTracking', Sensors, 9 (4), pp. 3033-3055. (map on next slide)



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Example of maps shown in Van der Spek, S., Van Schaick,

J., De Bois, P. & De Haan, R. (2009) 'Sensing Human

Activity: GPS Tracking', Sensors, 9 (4), pp. 3033-3055.

Figure 7. Two extreme types of GPS tracksfrom the Almere experiment: degree of

match between both ways on a return trip.

Background map is based on the Almere street

pattern.

Figure 5a. GPS tracking results from Lhrcenter(parking for 1,400 cars) and from Gorresplatz

(parking for 386 cars) in Koblenz: superimposition

of one week of data

collection from both locations. All track points are

logged at 5 seconds frequency on devices carried

by pedestrians that on the same day access the city

centre from the car park and return to their car.Godwin Yeboah, Northumbria University

Results 1 outcome of survey

0

50

100

150

200

250

300

350

400Summary graph of completed field data collection

Number



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Preliminary result 2 - Visualization of cycling

data using GeoTime, ArcGIS & Excel

DEMO VIDEO - GOTO:


My blog: http://godwinyeboah.blogspot.com/

My YouTube Channel: UNDERSTANDING

CYCLING DATA USING GeoTime, ArcGIS, Excel

and Bing Maps:http://www.youtube.com/SpatialScience

Concluding remarks

This kind of research design has only been implemented in

very few studies

Further challenges: e.g.:data cleansing and analysis,

reconstructing travel behaviour based on actual route

choices, purpose of journeys, cycle infrastructure, etc.

Thinking of modelling, simulation & visualisation

techniques

Towards an integrated model aiming to explainconstraints and/or enablers to cycling in an urban

environment


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References

Anderson, T., Abeywardana, V., Wolf, J. & Lee, M. (2010) National Travel

Survey GPS Feasibility Study

Lemieux, M. & Godin, G. (2009) 'How well do cognitive and environmental

variables predict active commuting?', International Journal of Behavioral

Nutrition and Physical Activity, 6 (1), p. 12.

DfT (2011). In Confidence - National Travel Survey (Questionaire form). .

London, Dft with National Centre for Social Research (NatCen).

Van der Spek, S., Van Schaick, J., De Bois, P. & De Haan, R. (2009) 'Sensing

Human Activity: GPS Tracking', Sensors, 9 (4), pp. 3033-3055.

Law professor Paul Ohm: Source; http://arstechnica.com/tech-policy/news/2009/09/your-secrets-live-online-in-databases-of-ruin.ars

Questions and Discussion

methodological issues around gps and cycling

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