“by 2025, when most of today’s psychology
undergraduates will be in their mid-30s, more
than 5 billion people on our planet will be using
ultra-broadband, sensor-rich smartphones far
beyond the abilities of today’s iPhones, Androids,
GPS / Wi-Fi
Phone / Text Logs
Social Media APIs
Accelerometer | Physical Activity
GPS / Wi-Fi | Mobility
Gyroscope | Orientation
Bluetooth | Co-Location
Microphone | Ambient Audio
Humidity | Environment
Temperature | Environment
Phone / Text Logs | Socialising
Device Logs | Network
Social Media APIs | Socialising
App Usage | /Information Needs
Case 1: Public Transport
N. Lathia, L. Capra. Tube Star: Crowd-Sourced Experiences on Public
Transport. In 11th
International Conference on Mobile and Ubiquitous
Systems. London, December 2014.
Conclusion: potential for smartphones as a near-
real time passenger surveying tool to collect
qualitative trip data.
Major Limitation: amount of data received. Still
relies on reported behaviour.
GPS / Wi-Fi (location)
Microphone (audio context)
Phone / Text Logs (sociability)
Device Logs (e.g., network)
Social Media APIs (crowdsourcing)
App Usage (information seeking)
1. Collect Wi-Fi scans that match
“Virgin Media Wi-Fi”
2. Manually label “unknown”
stations (“Where are you?”)
3. Apply heuristic-based clustering
algorithm to determine station
visits, paths, travel times.
34 users; 234,769 Wi-Fi scans,
Piccadilly Line to Holborn
Central Line to West Ruislip
Piccadilly Line to King's Cross
Victoria Line to Oxford Circus
Central Line to West Ruislip
Capturing Routes: Given an O-D pair, count the % of times that
another station appears as an intermediary
Observing Mistakes? E.g., 2.18% of trips from Pimlico to Victoria
Station go via Green Park (wrong direction).
Non-Adjacent Pairs of Wi-Fi Connections
Continued (lessened) limitations:
Data is not “complete” - phones do not always connect.
Data is now “noisy” by capturing route errors, “strange”
Transport route choices in individuals
With more scale:
Granular origin-destination + distributions of route data.
With more data:
I.e., precise locations of Wi-Fi hotspots (e.g., platform,
With more sensors:
What actual behaviours are occurring?
Case 2: Subjective Wellbeing & Behaviour
N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance:
Design Bias in Sensor-Based Experience Sampling Methods. In ACM
International Joint Conference on Pervasive and Ubiquitous Computing.
Zurich, Switzerland. September 2013.
N. Lathia, G. Sandstrom, P. Rentfrow, C. Mascolo. Happy People Live
Active Lives. In prep.
“A sample of 222 undergraduates was screened
for high happiness using multiple confirming
assessment filters. We compared the upper 10%
of consistently very happy people with average
and very unhappy people. The very happy people
were highly social, and had stronger romantic and
other social relationships than less happy
Diener, Seligman. Very Happy People. In Psychological Science 13 (1). Jan 2002.