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Methodologies for collection
and analysis of GPS data
for health research
Yan Kestens
Montreal University, Social and Preventive Medicine
Montreal Hospital University Research Center (CRCHUM)
SPHERE Lab .org
Spatial analysis of GPS data, Exeter University, UK
16th May 2013
Context
CHANGES
• Recent push in health research along the ‘space-time’
continuum
• A consequence/correlate of society’s ‘space-time
convergence’ ?
• Space: From ‘place based’ to ‘people based’
• Time: From snapshots to continuous measures, from delay
between collection and results to ‘real-time’
• Convergence of space and time, convergence of fields
• New methods, new developments, new continuums
Context
POTENTIAL
• Local trap / residential trap
• Space-time geography
• Potential path areas
• Activity spaces
• Network of usual places
• Multiple exposures
Context
TECHNOLOGICAL CHANGES
• Wearable sensors
• Ubiquity
• Connectedness
• 7 billion sensors
• Quantified Self - mHealth
Context
‘More data more often’
vs.
‘Less is more’
Spatial data collection for health
CAPTURE
PROCESSINGUSAGE
Web server
Acquisition server
Outputs /
Applications
End users
GISAlgorithms
GSM towerSensors
Issues with GPS data capture
CAPTURE
Participation/adherence: privacy, participation burden
Device usage: co-occurrence, lose vs. on body, device
manipulation
Device performance: Battery life, data storage space,
precision/validity of data points (TTFF, drift,
indoor/outdoor)
Temporal aspects: Epoch, survey duration, linkage with
other sensors and GIS data
GPS data capture
CAPTURE
Most current devices
GPS trackers
Low battery life
Pb of integration with additional sensors
Limited capacity of data transmission
Not designed for health research
GPS in cellphones
Battery life major hinder
Simultaneous usage with other applications not always
possible
GPS data capture
CAPTURE
Attempts to address these issues
Collaborations with engineers
Validation requirements
SenseDoc Multisensor Device
CAPTURE
Central Unit
GPS GPRS
Accelerometer
Acquisition server
Central Unit
GPS GPRS
Accelerometer
ANT Module
Memory
SPHERE Lab .org
125 g
137 g
96 * 80 mm
115 * 59 mm
SenseDoc Multisensor Device
CAPTURE
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
Acquisition server
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
SenseDoc Multisensor Device
CAPTURE
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
Acquisition server
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
Glucose
monitor
Galvanic skin
response
Accele-
rometer
HR
monitorBlood
pressure
Other
SenseDoc Multisensor Device
CAPTURE
Acquisition server
GPS – SIRF IV
GPS performance validation
Spatial accuracy
Time to First Fix (TTFF)
Indoor – Outdoor
Fixed - Moving
CAPTURE
Average of dist_moy Column Labels
Row Labels Etrex HTC MS Qstarz Grand Total
Indoor
cold 13,6 9,0 7,7 16,7 12,3
Brick building, hallway 14,1 5,7 4,1 15,4 10,4
Brick building, window 14,4 12,4 7,6 15,5 12,5
Concrete building, window 12,3 11,6 19,4 14,4
hot 12,9 11,3 10,0 15,5 12,6
Brick building, hallway 11,2 7,2 6,3 15,8 10,5
Brick building, window 7,6 5,0 6,9 12,8 8,5
Concrete building, window 19,9 21,8 16,9 17,9 18,7
warm 14,0 13,5 20,3 15,8 16,1
Brick building, hallway 10,4 15,6 22,1 11,1 14,8
Brick building, window 7,8 10,4 21,7 13,2 13,7
Concrete building, window 23,8 12,2 17,0 23,0 20,0
Outdoor
cold 7,8 16,6 11,0 17,6 13,0
Narrow streets 21,4 20,0 16,2 35,3 23,2
Open surroundings 2,8 12,0 1,4 1,1 4,3
Residential areas 2,4 4,1 0,9 2,5
Sky scrapers 4,7 17,8 22,2 33,0 19,4
hot 5,5 10,6 3,4 4,8 6,1
Narrow streets 12,9 18,6 4,9 9,5 11,5
Open surroundings 1,5 1,8 2,2 1,8 1,8
Residential areas 3,2 3,4 1,9 3,1 2,9
Sky scrapers 4,4 18,4 4,6 4,8 8,5
warm 8,6 9,1 6,5 10,0 8,5
Narrow streets 26,7 21,9 16,2 20,5 21,3
Open surroundings 3,0 5,4 3,3 4,1 3,9
Residential areas 4,1 4,6 2,8 5,0 4,1
Sky scrapers 5,0 8,9 7,4 15,2 9,1
Grand Total 10,3 11,4 9,5 13,0 11,0
CAPTURE
Average of ttff Column Labels
Row Labels Etrex HTC MS Qstarz Grand Total
Indoor
cold 136,3 255,0 33,2 86,3 102,3
Brick building, hallway 68,0 104,0 12,5 23,0 44,4
Brick building, window 252,0 406,0 9,5 193,0 187,9
Concrete building, window 89,0 77,5 43,0 69,8
hot 18,5 181,3 5,5 13,5 36,6
Brick building, hallway 6,5 82,0 6,0 2,5 16,0
Brick building, window 41,0 143,0 4,0 35,0 43,3
Concrete building, window 8,0 319,0 6,5 3,0 50,6
warm 101,7 293,3 46,5 204,7 149,5
Brick building, hallway 27,0 563,5 0,0 69,0 164,9
Brick building, window 107,0 26,0 84,5 191,0 113,0
Concrete building, window 171,0 20,0 55,0 354,0 168,6
Outdoor
cold 37,8 171,7 26,0 40,5 62,1
Narrow streets 44,0 247,0 36,0 40,0 91,8
Open surroundings 39,0 104,0 37,0 57,0 59,3
Residential areas 26,0 20,0 26,0 24,0
Sky scrapers 42,0 164,0 11,0 39,0 64,0
hot 16,5 36,1 21,9 10,1 21,5
Narrow streets 11,5 110,0 29,0 12,5 40,8
Open surroundings 10,5 15,0 4,5 1,0 7,8
Residential areas 8,5 10,0 7,5 3,0 7,3
Sky scrapers 35,5 9,5 46,5 38,0 31,6
warm 26,4 46,8 39,4 31,6 36,1
Narrow streets 40,0 45,0 45,0 40,0 42,5
Open surroundings 21,0 36,0 45,0 35,0 34,3
Residential areas 30,0 68,5 44,5 29,5 43,1
Sky scrapers 11,0 16,0 18,0 24,0 17,3
Grand Total 55,8 130,6 28,2 65,2 65,3
SenseDoc Multisensor Device
CAPTURE
Accelerometer
Marie-Lyse Bélanger, M.Sc. Student in kinesiology
Accelerometer validation using indirect calorimetry
Lab – 14 controlled exercises from sedentary to vigouros PA
Eleven adult subjects
Calculation of Vertical Magnitude Acceleration (VMAG)
Testing of various bandpass filters
Comparison with Actigraph GT3X performence
Best results obtained with Bandpass filter 0.1 Hz – 3.5 Hz
Modelling of Energy Expenditure: Adj. R-square of .79
Use of Vector Body Dynamic Acceleration (VEDBA)
SenseDoc Multisensor Device
CAPTURE
Battery life
Strong battery (3200 maH)
Axelle Chevallier, M.Sc. Student in
electrical engineering
Mohamad Sawan,
Battery optimisation algorithm
- Movement
- Location and movement
SenseDoc Multisensor Device
CAPTURE
Acquisition server
Battery life
SenseDoc Multisensor Device
CAPTURE
Data transmission
GPS Data sent over the air (cellphone network) every 30 minutes
Possible alerts depending on
- Location
- Activity
- Time
Connection to other sensors (2.4 GHz ANT+) Heart rate monitor,
footpod, RFID tags, etc.
SenseDoc Multisensor Device
CAPTURE
Challenges in developping new hardware
Hardware / Software / User Interface
Miniaturisation
From prototype to market
Challenges
Web server
Acquisition server
Outputs /
Applications
End users
GISAlgorithms
GSM towerSensors
CAPTURE
PROCESSING
USAGE
Spatial data collection for health
CAPTURE
PROCESSINGUSAGE
Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
Issues in data processing
PROCES
SING
Transforming raw GPS data into meaningful and useful
information
- ‘Putting things into context’
- Activity locations
- Trips between locations
Activity location detection
PROCES
SING
Development of kernel-density based algorithm to
transform raw data into history of activities and trips
ArcGIS ArcToolBox (see www.spherelab.org tools
section)
- Input: raw GPS data
- Output:
- Location of activity places
- Activity places timetable
- Trip timetable with origins and destinations
Thierry et al. (2013) IJHG
Issues in data processing
PROCES
SING
Activity location algorithm validation method
- Artificial track generation with controlled
parameters (noise, stop time)
- Testing of algorithm performance in relation to
track characteristics and algorithm parameters
Thierry et al. (2013) IJHG
Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
(a)Meannumberofstopsfou(b)Meandistancetotruestop
o
*10m radius missing
*10m radius missing
Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundstop
*10m radius missing
(b)Meandistancetotru
(c)Averagetimediff.relativeto
truestopduration
*10m radius missing
Noise categories Noise categories
Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
Issues in data processing
PROCES
SING
Linkage of GPS locations with additional information
- Temporal linkage:
- Additional wearable sensors (Accelerometer,
Heart rate monitor, continuous glucose
monitor)
- Spatial linkage: GIS data – exposure at any given
location/time – descriptive vs. causal
understanding
- Spatio-temporal linkage – spatio-temporal GIS
Issues in data processing
PROCES
SING
Mapping – visualisation
Tool for communication / counseling, etc.
Issue of privacy – artificial blurring
Usage
Using GPS to locate behaviour and assess exposure
Improving the understanding of mechanisms linking
environments to health behaviours and profiles
Using GPS to prompt recall and gain additional insight
Using GPS to support qualitative studies (go-along, geo-
ethnography, geo-tagged photos, environmental
perception, etc.)
Using GPS data to assist clinical practice (mHealth)
USAGE
Usage: Prompted recall
GPS / accelerometer data provides limited information
on:
- What people are actually doing
- Decision processes
- How they feel
GPS-prompted recall can help gather additional
information
USAGE
Usage: Prompted recall
Example 1: Bike share study (PI: Gauvin)
Pilot study (n=25) on combined use of cellphones and
accelerometers for gathering of:
- GPS data
- Nature of activities
- Transportation modes
- Accelerometry (PA)
- Momentary Impact Assessment (feelings)
USAGE
Usage: Prompted recall
Bike share study
N=25, study period=7 days
Accelerometer:
- PA assessment
Cellphone:
- GPS data – sent to server every hour
- Feelings (real-time questionnaires)
Daily online prompted recall data collection using the
MWM (Mobility Web Mapping) application:
- History of mobility
- Nature of activities
- Transportation modes
USAGE
Usage: Prompted recall
Example 2: RECORD GPS Study (Chaix & Kestens)
GPS + Accelerometer
MWM prompted recall survey (Mobility Web Mapping)
after reception and processing of GPS data:
- Validation of activity places and trips
- Nature of activities
- Transportation modes
USAGE
Usage: Prompted recall
MWM prompted data a useful tool to improve activity
location algorithm / data collection
- Match/mismatch between algorithm detection and
reported timetable (locations/times)
- Preliminary analyses: N=80
USAGE
88.5%
11.5%
GPS raw data
GPS data
Missing data
Usage: Prompted recall
Analyses comparing activity locations, trips and
corresponding timetables obtained through:
- Spherelab GPS algorithm
- MWM GPS-prompted recall
N=80
Median of 88.5% of survey period with usable (raw and
interpolated) GPS data (11.5% of period with missing data)
USAGE
88.5%
11.5%
Proportion of survey time with GPS
data
GPS data
Missing data
Usage: Prompted recall
USAGE
MWM Algorithm
A A <50m
A A >50m
A T
T A
T T
AA >50m Misplaced activity
AA-AT-TT Early departure
AA-AT-AA False trip
AA-TA-AA Missing trip
AA-TA-TT Late departure
TT-AT-TT Late arrival
TT-TA-TT False positive
TT-AT-TT False negative
TT-TA-AA Early arrival
Usage: Prompted recallUSAGE
96.0%
Correctly classified
Misplaced activity location
Early departure
False trip
Missing trip
Late departure
Late arrival
False positive
False negative
Early arrival
Other
4%
Proportion of valid GPS time with match / mismatch with
prompted recall data
USAGE
+ +
Trimble Juno
SC GPS +
Arcpad
Actigraph
GT3X
Polar HR
monitor
7-day data collection
Usage: Support for clinical interventions
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: MH Henderson)
Spatio-behavioural
indicators -
ArcToolBox
Interactive map-
based web
application
Application supports
lifestyle counseling
USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
How does GPS compare to regular
destinations?
Comparing spatial distribution of
- 7-day GPS data
- Regular destinations collected through an online interactive
mapping questionnaire (VERITAS)
How does GPS compare to regular
destinations?
89 participants of the RECORD GPS Study
VERITAS activity locations
• Total of 1,314 locations
• Median of 14 loc./ind.
How does GPS compare to regular
destinations?
89 participants of the RECORD GPS Study
7-day continuous GPS monitoring
0
10
20
30
40
50
60
70
80
90
100
Percentageofsurveyduration
withGPSfixes
Proportion of GPS survey duration with
valid GPS data
5 Days & 07:07:15
3 Days & 10:25:25
6 Days & 04:45:20
5 Days & 07:07:15
Yan Kestens - Methodologies for collection and analysis of GPS data  for health research
1000
250
500
100
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Within 100 m Within 250 m Within 500 m Within 1000m
Proportion of total survey time spent within
range of VERITAS locations
87%
85%
78%
66%
1000
250
500
100
Yan Kestens - Methodologies for collection and analysis of GPS data  for health research
0
20
40
60
80
100
120
140
Shortest distance between a GPS
detected location (unspecified category)
and a VERITAS location (specified
category)
(median value; n=1,314)
VERITAS CATEGORIES
Distanceinmeters
CONCLUSIONS
GPS opens great possibilities
Capture – Processing – Usage
Multidisciplinarity – Health – Transportation - Geography –
Engineering
Applications very diverse
For epidemiology – validity is key
Thank you!
SPHERE Lab .org
Benoit Thierry from SPHERELAB Claire Merrien from RECORD
Participants of all the studies!

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Yan Kestens - Methodologies for collection and analysis of GPS data for health research

  • 1. Methodologies for collection and analysis of GPS data for health research Yan Kestens Montreal University, Social and Preventive Medicine Montreal Hospital University Research Center (CRCHUM) SPHERE Lab .org Spatial analysis of GPS data, Exeter University, UK 16th May 2013
  • 2. Context CHANGES • Recent push in health research along the ‘space-time’ continuum • A consequence/correlate of society’s ‘space-time convergence’ ? • Space: From ‘place based’ to ‘people based’ • Time: From snapshots to continuous measures, from delay between collection and results to ‘real-time’ • Convergence of space and time, convergence of fields • New methods, new developments, new continuums
  • 3. Context POTENTIAL • Local trap / residential trap • Space-time geography • Potential path areas • Activity spaces • Network of usual places • Multiple exposures
  • 4. Context TECHNOLOGICAL CHANGES • Wearable sensors • Ubiquity • Connectedness • 7 billion sensors • Quantified Self - mHealth
  • 5. Context ‘More data more often’ vs. ‘Less is more’
  • 6. Spatial data collection for health CAPTURE PROCESSINGUSAGE
  • 7. Web server Acquisition server Outputs / Applications End users GISAlgorithms GSM towerSensors
  • 8. Issues with GPS data capture CAPTURE Participation/adherence: privacy, participation burden Device usage: co-occurrence, lose vs. on body, device manipulation Device performance: Battery life, data storage space, precision/validity of data points (TTFF, drift, indoor/outdoor) Temporal aspects: Epoch, survey duration, linkage with other sensors and GIS data
  • 9. GPS data capture CAPTURE Most current devices GPS trackers Low battery life Pb of integration with additional sensors Limited capacity of data transmission Not designed for health research GPS in cellphones Battery life major hinder Simultaneous usage with other applications not always possible
  • 10. GPS data capture CAPTURE Attempts to address these issues Collaborations with engineers Validation requirements
  • 11. SenseDoc Multisensor Device CAPTURE Central Unit GPS GPRS Accelerometer Acquisition server Central Unit GPS GPRS Accelerometer ANT Module Memory SPHERE Lab .org 125 g 137 g 96 * 80 mm 115 * 59 mm
  • 12. SenseDoc Multisensor Device CAPTURE Central Unit GPS GPRS Memory Accelerometer ANT Module Acquisition server Central Unit GPS GPRS Memory Accelerometer ANT Module
  • 13. SenseDoc Multisensor Device CAPTURE Central Unit GPS GPRS Memory Accelerometer ANT Module Acquisition server Central Unit GPS GPRS Memory Accelerometer ANT Module Glucose monitor Galvanic skin response Accele- rometer HR monitorBlood pressure Other
  • 14. SenseDoc Multisensor Device CAPTURE Acquisition server GPS – SIRF IV GPS performance validation Spatial accuracy Time to First Fix (TTFF) Indoor – Outdoor Fixed - Moving
  • 15. CAPTURE Average of dist_moy Column Labels Row Labels Etrex HTC MS Qstarz Grand Total Indoor cold 13,6 9,0 7,7 16,7 12,3 Brick building, hallway 14,1 5,7 4,1 15,4 10,4 Brick building, window 14,4 12,4 7,6 15,5 12,5 Concrete building, window 12,3 11,6 19,4 14,4 hot 12,9 11,3 10,0 15,5 12,6 Brick building, hallway 11,2 7,2 6,3 15,8 10,5 Brick building, window 7,6 5,0 6,9 12,8 8,5 Concrete building, window 19,9 21,8 16,9 17,9 18,7 warm 14,0 13,5 20,3 15,8 16,1 Brick building, hallway 10,4 15,6 22,1 11,1 14,8 Brick building, window 7,8 10,4 21,7 13,2 13,7 Concrete building, window 23,8 12,2 17,0 23,0 20,0 Outdoor cold 7,8 16,6 11,0 17,6 13,0 Narrow streets 21,4 20,0 16,2 35,3 23,2 Open surroundings 2,8 12,0 1,4 1,1 4,3 Residential areas 2,4 4,1 0,9 2,5 Sky scrapers 4,7 17,8 22,2 33,0 19,4 hot 5,5 10,6 3,4 4,8 6,1 Narrow streets 12,9 18,6 4,9 9,5 11,5 Open surroundings 1,5 1,8 2,2 1,8 1,8 Residential areas 3,2 3,4 1,9 3,1 2,9 Sky scrapers 4,4 18,4 4,6 4,8 8,5 warm 8,6 9,1 6,5 10,0 8,5 Narrow streets 26,7 21,9 16,2 20,5 21,3 Open surroundings 3,0 5,4 3,3 4,1 3,9 Residential areas 4,1 4,6 2,8 5,0 4,1 Sky scrapers 5,0 8,9 7,4 15,2 9,1 Grand Total 10,3 11,4 9,5 13,0 11,0
  • 16. CAPTURE Average of ttff Column Labels Row Labels Etrex HTC MS Qstarz Grand Total Indoor cold 136,3 255,0 33,2 86,3 102,3 Brick building, hallway 68,0 104,0 12,5 23,0 44,4 Brick building, window 252,0 406,0 9,5 193,0 187,9 Concrete building, window 89,0 77,5 43,0 69,8 hot 18,5 181,3 5,5 13,5 36,6 Brick building, hallway 6,5 82,0 6,0 2,5 16,0 Brick building, window 41,0 143,0 4,0 35,0 43,3 Concrete building, window 8,0 319,0 6,5 3,0 50,6 warm 101,7 293,3 46,5 204,7 149,5 Brick building, hallway 27,0 563,5 0,0 69,0 164,9 Brick building, window 107,0 26,0 84,5 191,0 113,0 Concrete building, window 171,0 20,0 55,0 354,0 168,6 Outdoor cold 37,8 171,7 26,0 40,5 62,1 Narrow streets 44,0 247,0 36,0 40,0 91,8 Open surroundings 39,0 104,0 37,0 57,0 59,3 Residential areas 26,0 20,0 26,0 24,0 Sky scrapers 42,0 164,0 11,0 39,0 64,0 hot 16,5 36,1 21,9 10,1 21,5 Narrow streets 11,5 110,0 29,0 12,5 40,8 Open surroundings 10,5 15,0 4,5 1,0 7,8 Residential areas 8,5 10,0 7,5 3,0 7,3 Sky scrapers 35,5 9,5 46,5 38,0 31,6 warm 26,4 46,8 39,4 31,6 36,1 Narrow streets 40,0 45,0 45,0 40,0 42,5 Open surroundings 21,0 36,0 45,0 35,0 34,3 Residential areas 30,0 68,5 44,5 29,5 43,1 Sky scrapers 11,0 16,0 18,0 24,0 17,3 Grand Total 55,8 130,6 28,2 65,2 65,3
  • 17. SenseDoc Multisensor Device CAPTURE Accelerometer Marie-Lyse Bélanger, M.Sc. Student in kinesiology Accelerometer validation using indirect calorimetry Lab – 14 controlled exercises from sedentary to vigouros PA Eleven adult subjects Calculation of Vertical Magnitude Acceleration (VMAG) Testing of various bandpass filters Comparison with Actigraph GT3X performence Best results obtained with Bandpass filter 0.1 Hz – 3.5 Hz Modelling of Energy Expenditure: Adj. R-square of .79 Use of Vector Body Dynamic Acceleration (VEDBA)
  • 18. SenseDoc Multisensor Device CAPTURE Battery life Strong battery (3200 maH) Axelle Chevallier, M.Sc. Student in electrical engineering Mohamad Sawan, Battery optimisation algorithm - Movement - Location and movement
  • 20. SenseDoc Multisensor Device CAPTURE Data transmission GPS Data sent over the air (cellphone network) every 30 minutes Possible alerts depending on - Location - Activity - Time Connection to other sensors (2.4 GHz ANT+) Heart rate monitor, footpod, RFID tags, etc.
  • 21. SenseDoc Multisensor Device CAPTURE Challenges in developping new hardware Hardware / Software / User Interface Miniaturisation From prototype to market Challenges
  • 22. Web server Acquisition server Outputs / Applications End users GISAlgorithms GSM towerSensors CAPTURE PROCESSING USAGE
  • 23. Spatial data collection for health CAPTURE PROCESSINGUSAGE
  • 24. Issues in data processing PROCES SING Continuous monitoring = Huge pile of data!!!
  • 25. Issues in data processing PROCES SING Continuous monitoring = Huge pile of data!!!
  • 26. Issues in data processing PROCES SING Continuous monitoring = Huge pile of data!!!
  • 27. Issues in data processing PROCES SING Transforming raw GPS data into meaningful and useful information - ‘Putting things into context’ - Activity locations - Trips between locations
  • 28. Activity location detection PROCES SING Development of kernel-density based algorithm to transform raw data into history of activities and trips ArcGIS ArcToolBox (see www.spherelab.org tools section) - Input: raw GPS data - Output: - Location of activity places - Activity places timetable - Trip timetable with origins and destinations Thierry et al. (2013) IJHG
  • 29. Issues in data processing PROCES SING Activity location algorithm validation method - Artificial track generation with controlled parameters (noise, stop time) - Testing of algorithm performance in relation to track characteristics and algorithm parameters Thierry et al. (2013) IJHG
  • 30. Issues in data processing PROCES SING Activity location detection kernel-based algorithm Thierry et al. (2013) IJHG Aft Akd (a)Meannumberofstopsfoundp *10m radius missing
  • 31. Issues in data processing PROCES SING Activity location detection kernel-based algorithm Thierry et al. (2013) IJHG Aft Akd (a)Meannumberofstopsfoundp *10m radius missing (a)Meannumberofstopsfou(b)Meandistancetotruestop o *10m radius missing *10m radius missing
  • 32. Issues in data processing PROCES SING Activity location detection kernel-based algorithm Thierry et al. (2013) IJHG Aft Akd (a)Meannumberofstopsfoundstop *10m radius missing (b)Meandistancetotru (c)Averagetimediff.relativeto truestopduration *10m radius missing Noise categories Noise categories
  • 33. Issues in data processing PROCES SING Activity location detection kernel-based algorithm Thierry et al. (2013) IJHG Aft Akd (a)Meannumberofstopsfoundp *10m radius missing
  • 34. Issues in data processing PROCES SING Linkage of GPS locations with additional information - Temporal linkage: - Additional wearable sensors (Accelerometer, Heart rate monitor, continuous glucose monitor) - Spatial linkage: GIS data – exposure at any given location/time – descriptive vs. causal understanding - Spatio-temporal linkage – spatio-temporal GIS
  • 35. Issues in data processing PROCES SING Mapping – visualisation Tool for communication / counseling, etc. Issue of privacy – artificial blurring
  • 36. Usage Using GPS to locate behaviour and assess exposure Improving the understanding of mechanisms linking environments to health behaviours and profiles Using GPS to prompt recall and gain additional insight Using GPS to support qualitative studies (go-along, geo- ethnography, geo-tagged photos, environmental perception, etc.) Using GPS data to assist clinical practice (mHealth) USAGE
  • 37. Usage: Prompted recall GPS / accelerometer data provides limited information on: - What people are actually doing - Decision processes - How they feel GPS-prompted recall can help gather additional information USAGE
  • 38. Usage: Prompted recall Example 1: Bike share study (PI: Gauvin) Pilot study (n=25) on combined use of cellphones and accelerometers for gathering of: - GPS data - Nature of activities - Transportation modes - Accelerometry (PA) - Momentary Impact Assessment (feelings) USAGE
  • 39. Usage: Prompted recall Bike share study N=25, study period=7 days Accelerometer: - PA assessment Cellphone: - GPS data – sent to server every hour - Feelings (real-time questionnaires) Daily online prompted recall data collection using the MWM (Mobility Web Mapping) application: - History of mobility - Nature of activities - Transportation modes USAGE
  • 40. Usage: Prompted recall Example 2: RECORD GPS Study (Chaix & Kestens) GPS + Accelerometer MWM prompted recall survey (Mobility Web Mapping) after reception and processing of GPS data: - Validation of activity places and trips - Nature of activities - Transportation modes USAGE
  • 41. Usage: Prompted recall MWM prompted data a useful tool to improve activity location algorithm / data collection - Match/mismatch between algorithm detection and reported timetable (locations/times) - Preliminary analyses: N=80 USAGE 88.5% 11.5% GPS raw data GPS data Missing data
  • 42. Usage: Prompted recall Analyses comparing activity locations, trips and corresponding timetables obtained through: - Spherelab GPS algorithm - MWM GPS-prompted recall N=80 Median of 88.5% of survey period with usable (raw and interpolated) GPS data (11.5% of period with missing data) USAGE 88.5% 11.5% Proportion of survey time with GPS data GPS data Missing data
  • 43. Usage: Prompted recall USAGE MWM Algorithm A A <50m A A >50m A T T A T T AA >50m Misplaced activity AA-AT-TT Early departure AA-AT-AA False trip AA-TA-AA Missing trip AA-TA-TT Late departure TT-AT-TT Late arrival TT-TA-TT False positive TT-AT-TT False negative TT-TA-AA Early arrival
  • 44. Usage: Prompted recallUSAGE 96.0% Correctly classified Misplaced activity location Early departure False trip Missing trip Late departure Late arrival False positive False negative Early arrival Other 4% Proportion of valid GPS time with match / mismatch with prompted recall data
  • 45. USAGE + + Trimble Juno SC GPS + Arcpad Actigraph GT3X Polar HR monitor 7-day data collection Usage: Support for clinical interventions Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine Pediatric Hospital (PI: MH Henderson) Spatio-behavioural indicators - ArcToolBox Interactive map- based web application Application supports lifestyle counseling
  • 46. USAGE Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine Pediatric Hospital (PI: M. Henderson) Usage: Support for clinical interventions
  • 47. USAGE Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine Pediatric Hospital (PI: M. Henderson) Usage: Support for clinical interventions
  • 48. USAGE Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine Pediatric Hospital (PI: M. Henderson) Usage: Support for clinical interventions
  • 49. How does GPS compare to regular destinations? Comparing spatial distribution of - 7-day GPS data - Regular destinations collected through an online interactive mapping questionnaire (VERITAS)
  • 50. How does GPS compare to regular destinations? 89 participants of the RECORD GPS Study VERITAS activity locations • Total of 1,314 locations • Median of 14 loc./ind.
  • 51. How does GPS compare to regular destinations? 89 participants of the RECORD GPS Study 7-day continuous GPS monitoring 0 10 20 30 40 50 60 70 80 90 100 Percentageofsurveyduration withGPSfixes Proportion of GPS survey duration with valid GPS data 5 Days & 07:07:15 3 Days & 10:25:25 6 Days & 04:45:20 5 Days & 07:07:15
  • 54. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Within 100 m Within 250 m Within 500 m Within 1000m Proportion of total survey time spent within range of VERITAS locations
  • 57. 0 20 40 60 80 100 120 140 Shortest distance between a GPS detected location (unspecified category) and a VERITAS location (specified category) (median value; n=1,314) VERITAS CATEGORIES Distanceinmeters
  • 58. CONCLUSIONS GPS opens great possibilities Capture – Processing – Usage Multidisciplinarity – Health – Transportation - Geography – Engineering Applications very diverse For epidemiology – validity is key
  • 59. Thank you! SPHERE Lab .org Benoit Thierry from SPHERELAB Claire Merrien from RECORD Participants of all the studies!