Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop, 26-27 June 2014, Cambridge

1. Tracking Social Practices
with Big(ish) data
Dr Ben Anderson
Sustainable Energy Research Centre,
Faculty of Engineering & Environment
www.energy.soton.ac.uk
26th June 2014 @dataknut

2. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Contents
§ Background
– Practices – the view from here
§ Tracking them down
– TimeTraces
– TechnoTraces
§ Challenges
2

3. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Contents
§ Background
– Practices – the view from here
§ Tracking them down
– TimeTraces
– TechnoTraces
§ Challenges
3

4. @dataknut: Tracking Social Practices with Big(ish) data #pbes
So what are practices?
a temporally unfolding and
spatially
dispersed nexus of doings and
sayings
Schatzki, 1996
‘habits’, ‘bodily and mental routines’
‘permanent dispositions’
Reckwitz, 2002;
Entities
Performance
habituation, routine, practical
consciousness,
tacit knowledge, tradition
Performance often neither fully
conscious
nor reflective
Warde, 2005
Why people don’t do
what they ‘should’ - Jim Skea, 2011
Embodied habits & competencies (skills),
Meanings/ conventions (image)
Material artefacts (stuff)
Shove & Pantzar, 2005

5. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Can we observe them?
(an empiricist’s
response)
Image: Anthony B. Wooldridge
Image: Eric Shipton
“The recurrent enactment of specific
practices leaves all sorts of “marks” –
diet shows up in statistics on obesity;
heating and cooling practices have effect
on energy demand, and habits of laundry
matter for water consumption.
Identifying relevant “proxies” represents
one way to go.”
ESRC Sustainable Practices Working
Group (SPRG) Discussion Paper, 2011

6. @dataknut: Tracking Social Practices with Big(ish) data #pbes
§ Tried:
• Shadowing/tracking/observation
– Small n, can ask why, investigator effects (?)
– Historical?
• Time use surveys (diaries, e.g. UK ONS 2000, MTUS)
– Big n, non response issues, can’t ask why, complex data
– Rarely longitudinal, sometimes historical (MTUS)
§ Relatively Untried:
• Expenditure Surveys
– Big n, proxies for practices, can’t ask why, complex data
– e.g. http://link.springer.com/article/10.1007/s11269-012-0117-y
• TechnoTraces (Savage & Burrows, 2007; 2009; 2014)
– Transactions/meters/bills, proxies for practices, complex data, difficult to
process
How to detect ‘marks’ & proxies?

7. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Contents
§ Background
– Practices – the view from here
§ Tracking them down
– TimeTraces
– TechnoTraces
§ Challenges
7

8. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Time Traces
§ Large sample time-use
surveys
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0
10
20
30
40
50
60
70
80
90
100
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00:00
Time
%
Phone/email friends
Travel
Computer
Hobbies/other
Going out
Friends/Family at home
Sport/exercise
Reading
TV/radio
shopping
adult care
child care
civic acts
education
work
housework
eating/drinking
washing
sleeping
Data: % of sample reporting activity
Source: ONS 2005 UK Time Use Survey, all 16+

9. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Time Traces
§ Large sample time-use
surveys
9
Credit: Mathieu Durand-Daubin (EDF R&D) drawing on INSEE (2012) “Le temps de l’alimentation en France”

10. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Time Traces
§ Large sample time-use
surveys
§ Over time
– E.g. Laundry
10
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
04:00
05:30
07:00
08:30
10:00
11:30
13:00
14:30
16:00
17:30
19:00
20:30
22:00
23:30
01:00
02:30
Sunday
1974
2005
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
04:00
05:30
07:00
08:30
10:00
11:30
13:00
14:30
16:00
17:30
19:00
20:30
22:00
23:30
01:00
02:30
Monday
1974
2005
Data: % of reported laundry being done at given time
Source: Multinational Time Use Survey Dataset (UK, 1974-2005,
all 18+)

11. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Cooking
0%
2%
4%
6%
8%
10%
12%
14%
16%
00:00
01:00
02:00
03:00
04:00
05:00
06:00
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
%respondents(weighted)
UK
Italy
Germany
Norway
Bulgaria
Time Traces
§ Large sample time-use
surveys
§ Over time
– E.g. Laundry
§ Internationally
11
§ Source: E-living Survey (2002) n ~= 1100 per country
(Norway, UK , Bulgaria, Germany, Italy)

12. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Contents
§ Background
– Practices – the view from here
§ Tracking them down
– TimeTraces
– TechnoTraces
§ Challenges
12

13. @dataknut: Tracking Social Practices with Big(ish) data #pbes
TechnoTraces: Practice Hunting
§ Inspiration:
– Qualitative study of telephone calling
– Lacohee & Anderson (2000) Interacting with the telephone, doi:10.1006/ijhcs.
2000.0439
§ Call types
– Duty calls: generally to family members and were made because the caller felt a
sense of duty to keep in touch
– Maintenance calls: real motivation was to maintain a friendship
– Grapevine calls: a series of calls often prompted by a call e.g. passing on news
– Batch calls: making a series of outgoing calls e.g. cheap rate, bored or lonely
§ Question: can we identify them in a call records dataset?
– c. 1.5 million incoming/outgoing phone call records (time, duration) linked to
surveys of c 1000 GB households 1999-2001
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14. @dataknut: Tracking Social Practices with Big(ish) data #pbes
TechnoTraces: Practice Hunting
§ Algorithm:
– Sequence identifier
– Flexible ‘gap’ parameter
§ Batch calls (Out, Out, Out…)
– 20:00 -> late
– Not Thursdays or Fridays
– Sunday evenings
§ Grapevine calls (In, Out, Out…)
– 18:00 – 19:30
– Sunday evenings
14
Source: BT HomeOnline Survey (2000), n calls ~= 1.5 million from c. 310 households
http://repository.essex.ac.uk/2294/
Data processing by Dr David Hunter (ECS, University of Essex)

15. @dataknut: Tracking Social Practices with Big(ish) data #pbes
TechnoTraces: Practice Hunting
§ Contrasts
§ Requires
– ‘Labeled’ data
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Source: BT HomeOnline Survey (2000), n calls ~= 1.5 million from c. 310 households
http://repository.essex.ac.uk/2294/
Data processing by Dr David Hunter (ECS, University of Essex)

16. @dataknut: Tracking Social Practices with Big(ish) data #pbes
TechnoTraces: Applied to energy?
§ Contrasting gas consumption
16
Source: EPSRC DANCER Project baseline gas consumption monitoring - http://www.dancer-project.co.uk/
§ Gas consumption per 5 minutes, identical dwellings in South East UK, same street, both couples with 3 children, male
partner working, female partner not
§ December 2012 – February 2013

17. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Using linked mixed methods?
§ E.g. TechnoTraces & TimeTraces!
17
Electricity
Source: Small scale energy diary
and consumption monitoring study
lead by Kathryn Buchanan,
University of Essex
http://www.dancer-project.co.uk/
GasElectricity

18. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Contents
§ Background
– Practices – the view from here
§ Tracking them down
– TimeTraces
– TechnoTraces
§ Challenges
18

19. @dataknut: Tracking Social Practices with Big(ish) data #pbes
‘Big’ Data Challenges
§ Provenance:
– Who did what to ‘my’ data?
§ Quality:
– It’s never clean
§ Samples
– What (or who) does it represent?
§ Sampling
– Do we really need it all?
§ Linkage
– Multiple methods & multiple views
19
It might be big but
is it clever?
Are people the only
agents?
And the bigger it is
the harder to clean
What & why?

20. @dataknut: Tracking Social Practices with Big(ish) data #pbes
Thank you
§ Questions?
– b.anderson@soton.ac.uk
– @dataknut
§ http://www.energy.soton.ac.uk/
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