This document summarizes research conducted by the ESRC on public views regarding the use of alternative data sources. The ESRC conducted public dialogues in 2013 and 2015 to understand concerns about linking administrative data and using private sector data for research. The dialogues found support for these practices if the data is used for socially beneficial purposes, is fully anonymized and kept secure, and no commercial gain is involved. However, participants needed extensive information to feel these conditions would be met. The dialogues uncovered several public concerns including lack of transparency about data collection and use, data being passed to third parties, ensuring data security, and intentions for long-term data storage. Key factors affecting public acceptance included trust in governance processes,

1. Opportunities for alternative
data sources
Chair: Helen James, Head of
Department of Law, University of
Winchester

2. Opportunities for alternative
data sources to support the
Census
Jane Naylor, 29th June

3. Data sources for official statistics
• Surveys – eg of businesses and households
• Census – every 10 years
• Administrative data – by-product of
Government process
• Big Data?
‘Data that is difficult to collect, store or process within
the conventional systems of statistical organizations.
Either, their volume, velocity, structure or variety
requires the adoption of new statistical software
processing techniques and/or IT infrastructure to
enable cost-effective insights to be made.’
(UNECE, 2013)

4. Demographics, population flows :
mobile phone data
4

5. Twitter
Rationale: Using geo-located
Twitter to gain new insights
mobility and migration
• 7 months of geo-located tweets
within Great Britain (about 100
million data points)
• Methodology to infer place of usual
residence:
- Identify user ‘anchor points’ by
clustering tweets using a DBSCAN
algorithm
- Identify residential anchor points using
AddressBase and nearest neighbour
analysis
Geolocated
penetration rates
by local
authority

6. Use case: Student mobility

7. Twitter - sentiment analysis

8. Twitter - sentiment analysis

9. Electricity: smart meter
9
Half hourly electricity consumption over 7 days at one meter,
through 28 consecutive 7 day periods.

10. Housing websites: Zoopla

11. Aerial imagery
11

12. Opportunities and challenges
Current Census priorities:
•Mobile phone data for population mobility
•Intelligence on addresses

13. Using data from property websites and
aerial imagery to support the Census
Karen Gask
ONS Big Data team

14. Plan for today
• How property website data can be used to
improve statistics in areas with caravan parks
• What analysis of aerial imagery can tell us
about caravan parks
• Your feedback

15. Challenges for Census enumeration -
where Big Data can help
• Address intelligence is required to effectively
plan enumeration resources
• Understanding where knowledge gaps exist
• Help identify where there are access issues
or new builds

16. Property website data

17. Property websites

18. Potential benefits of property data for
Census
Improve understanding of small areas by identifying:
• High proportions of rental properties
• Unusual properties which may not be captured well in the
Address Register (house boats, caravan homes, beach
huts)
• Areas where there may be access issues or new builds
Provide some limited information on tenure of private
sector housing for Administrative Data Census

19. Work undertaken
Aim: investigate methods of machine learning
which could accurately identify, or distinguish
between, traits of interest within property data
in an automated way
Rationale: automated classification could allow
targeted field work and inform enumeration
resource allocation

20. Collecting data from these websites

21. Collecting data from these websites
• Could have ‘web scraped’ html code behind
websites to capture this data
− But many websites prohibit this
• Zoopla provides (limited) data for free via an
API (Application Programming Interface)
− Successfully collected data about 60,000
properties for sale or for rent

22. Early results
• Identified caravan homes with good accuracy
using price, property description, number of
bedrooms and property type
• Distinguished between holiday and residential
caravan homes with reasonable accuracy
(although sample size is small)
• Currently working on analysing property
description to identify gated communities

23. Identifying caravan homes (1)
• Developed machine learning methods such
as logistic regression, decision trees and
support vector machines
Is property type
“mobile/park home”?
yesno
Does property
description contain
“holiday park”?
no yes
Caravan home
Caravan homeNot a caravan home
n=180
n=55n=17,501

24. Identifying caravan homes (2)
• Support vector
machines the
most accurate
method
y
x
Linear hyperplane
and support
vectors
Non-linear
hyperplane
y
x
Predicted
Actual Not a caravan
home
Caravan
home
Not a caravan
home
7,113 18
Caravan home 0 99
Unseen testing set of 7,230:

25. Distinguishing between holiday and
residential caravan homes
• Classified 500 caravan
home descriptions
• Split descriptions into
words then correlated
each word with holiday
/ residential
classification
• Small sample size so
there is some
overfitting

26. Gated communities
• Currently exploring
use of Natural
Language Processing
on property
description
• Want “set in a private
gated development”
but not “…gated side
access to the garden”

27. Limitations
• Only provides data
about properties for
sale or rent
• Zoopla does not cover
all properties for sale or
rent
• Some properties have
no description or a very
small description
• Sample of data we have
collected is small for
unusual properties

28. Next steps
• Data shows promise but we have collected all
the free data we can (nearly 60,000 records)
• Soon to issue a tender to purchase data for
Census Test areas to test methods in 2017
• Understand how this data could improve
statistics in areas with caravan parks

29. Aerial imagery

30. Potential benefits of image data for
Census
• Similar to property data – image data could
help fill knowledge gaps by identifying:
• the number of properties in a given area
• properties which are similar / different
• properties with particular features
• Images can be more timely than field
intelligence
• Images can provide more cost effective
insight than field intelligence

31. Work undertaken
Aim: To explore the utility of aerial and satellite
imagery for official statistics through a pilot
study of caravan site images
Rationale: This could improve statistics in areas
with caravan parks, which are historically
considered 'hard to count' within the Census
Address Register

32. Collecting data
• Data are obtained from Google's API
(Application Programming Interface) for free
• There is a limit on image dimension and data
amount one can get for free (e.g. download of
images of New Forest took 2 days)
• All downloaded images have the same
dimensions and the same level of magnitude
• Google takes care of some pre-processing:
blending images together, adjusting colours

33. Pre-processing
Machine learning requires
‘training data’ where the objects
of interest are correctly labelled
- Circa 60 images were
manually labelled before
analysis
To artificially increase the size
of the dataset images were
augmented by
• rotation,
• flipping and
• translation.

34. Object recognition
Used the following machine learning techniques
(plus others):
• Logistic regression
• Random Forests
• Support Vector Machines
• But artificial neural networks worked best

35. Output
• Heat map of
probabilities
that there is a
caravan at a
given
spot/patch of
the image
• Accuracy (for
single patches)
97%

36. Limitations
Limitations of the free data:
• Quality of the images, consistency of colours (white
balance, season)
• Timeliness of the data (e.g. Google satellite imagery
is up to 3 years old)
Algorithm limitations:
• Humans can't get it 100% right, 97% seems good
• But even small error rates lead to large amount of
false positives when the classification is deployed to
large area

37. So what can we do?
• Identify deficiencies in the Address Register
used for Census
• Maybe the accuracy is not good enough for
individual caravans, but it can still help with
caravan parks
• Focus on large clusters and compare them
with Address Register

38. For example these sites
Address Register: 21 caravans
Algorithm: 188 caravans
Address Register: 3 caravans
Algorithm: 121 caravans

39. Or these sites
Address Register: 0 caravans
Algorithm: 61 caravans
Address Register: 0 caravans
Algorithm: 21 caravans?

40. Similar housing
• Find similar
buildings (e.g.
terraced
houses)
• Heat map of
similarity of
each spot/patch
to the central
one

41. Next steps
Short term:
• Use discrepancies between algorithm and
Address Register in Census Tests
• Include more data sources, e.g. LIDAR which
captures the height of mapped objects
Long term / other applications:
• Land use classification (sustainable
development, crop types)
• Population density estimation

42. Your feedback

43. Your feedback please
• Questions or comments on this work
• Can you think of other applications for this
type of data or methods?
• Is there similar work happening elsewhere?
• Can you think of other ‘big data’ sources we
haven’t considered yet for Census?
Comments / questions are welcome now or
Jane and I will be around for lunch and the
rest of the day (or
ons.big.data.project@ons.gov.uk)

44. What do we understand about public acceptability
of using administrative and other data for
research?
Vanessa Cuthill, Deputy Director, ESRC

45. Outline
• Context
• ESRC Dialogues
• Other reports
• Views from participants

46. Context
Administrative Data Research
Network

47. Dialogues – what have we done?
• ESRC
2013 Public dialogues on using admin data (ESRC,
ONS, Ipsos Mori)
2015 Big Data: Public views on the use of private sector
data for social research (ESRC, Hopkins Van Mil)
• Others:
2014 Powers and perils of data (Ipsos Mori)
2014 Public attitudes to the use and sharing of their
data (RSS and Ipsos Mori)
2015 Private Lives? (MRS Report)

48. Public dialogues on using admin data

49. Why did ESRC embark on the dialogues on
data in 2013?
• Rapidly evolving data landscape - Administrative
Data Taskforce
• We wanted to:
Better understand people’s views on the linking of admin
data
Begin the process of creating a terminology describing the
re-use of administrative data and data linking that is
understandable to the general public
Help inform the development of the governance and
operational procedures of the ADRCs and provide data
on public attitudes to inform their future strategies and
priorities

50. Background
• Throughout October - November 2013 public dialogues
held in 7 locations across UK (Ipsos MORl led)
• The aims were to:
To better understand the cultural barriers around linking
administrative data
To begin the process of creating a language that is
meaningful and accessible to the public.
To test the public perceptions of the rules that ESRC
ADRCs will be subject to and to provide the ADRCs
with data on public attitudes and appetite for
engagement
(To provide ONS with more detailed evidence on public
views of their current front-running option for Beyond
2011)

51. Support for initiative IF:
1. The data is linked for socially beneficial purposes
“As long as it’s used for good, like to develop things,
improve services, improve knowledge.” Belfast
2. It is fully de-identified – partial vs full postcodes
3. It is kept secure at all times – concerns around remote access
4. No commercial gain for business including commercial access
BUT participants needed extensive information and discussions with
experts and researchers in order to be satisfied that these conditions
would be met under the ADRN plans.
So - simply publicising these three conditions may not be enough to
ensure that the general public are reassured about or support the work
of the ADRN.

52. The ADRN Initiative

53. Impact of this dialogue
• Report shared with ADRN
• Informed decisions of the ADRN Management Committee for the
ADRN policies and procedures
▶ Lay membership in ADRN Board and Approvals Panel
• Short animated videos on ADRN website to help explain:
Data linkage https://www.youtube.com/watch?v=E3e4D2bHxa8&feature=youtu.be
Protecting Privacy https://www.youtube.com/watch?v=nnxz3_XGMAE&feature=youtu.be
• Clear policies and 5 safe’s Safe People, Safe Projects, Safe
Settings, Safe Outputs, Safe Data protocols

54. Big Data: Public views on the use of
private sector data for social research
A Findings Report for the Economic and Social
Research Council

55. Aim and objectives of the dialogue
To explore public views on access to and
the use of data from private sector
organisations for research purposes in the
context of three Data Research Centres
funded by the ESRC.
o To identify areas of public concern about
confidentiality and privacy impact
o To start creating a language around private
sector data and the use for research purposes
o To test public understanding of: data ownership,
data acquisition, data access, using/ re-using
private sector data, data storage and preservation

56. The journey

57. Public views on data collection by
the private sector
“It’s just the way we live”
Internet
GPS
trackin
g
devices
Cards
Public
Places
No way you
can opt out
of giving
data unless
you live like
a hermit in
the middle
of an island.

58. Particular Concerns
Lack of
Transparency, &
Information
Passing/Selling data
to others
Keeping data safe Linking data
Intentions for data
collection and use
DPA: Principles and
Sanctions

59. Examples of public concerns
“What people are worried about is that it’s not going to be kept just
within. It might get sold to insurance companies, employers and this
is where people want to know that it’s going to be safe.”
“The more data you add the more it’s creating this sense of identity
of each person, so it’s almost like everyone’s got this data avatar
that’s building up as we get older.”
“I try to avoid using the internet for purchases specifically because I
don’t want my data collected and then used for sales purposes
afterwards.”
(on DPA) “Things like ‘used in a way that is adequate, relevant and
not excessive’ what does that mean? Who decides what that is? Do
you get to decide that yourself when you’re doing research?

60. Acceptability and red lines:

61. Data acquisition
Only acquire
data from
trustworthy
companies
Ensure
accuracy and
relevance of
data
Work as much
as possible with
anonymised
data
…. Test the
provenance of
data sets
Little support for
payment for
data sets
Improved
information
….
Accurate
and up to
date data is
vital
It is acceptable
to share data for
the good of
society – but we
need to know
how it will be
used
I don’t think it’s
right that they
[the Data
Centres]
should buy
data, it’s public
money so they
should be
spending it on
the public

62. Data access
Trust in
processes
Approval for
access
procedures
researchers
Avoid clash of
interests
.... Secure setting
favoured over
virtual
environment
Consent for
use of personal
data
Clear
communication
about purpose
of research
....
Big data is a
broad term,
at what
point is it
not personal
anymore,
when does
it lose your
name?
The process
to secure it
and who’s
actually
getting to
use it and
stuff like that
[…] seems to
be set up
that it’s pretty
secure and
that not just
anybody can
walk in and
get this
information.

63. Data storage
More
information:
what/ / for how
long/ security
Physical storage
favoured over
virtual
environment
Ensure systems
are accessible
in future
Storage
We don’t
know
where the
information’
s stored
and who’s
in control of
it.

64. Data ownership
Low
familiarity
More
information:
type of data
owned
More
information:
data you
don’t use
Ownership
They are going to
have data coming
in, they’re going to
be processing it,
so although it
originally came
from a source, the
new information
that’s been
collected, is that
owned by them or
does [ownership]
still go back to the
original?
If it’s
about
me,
surely I
own the
data?

65. Transparency – why it matters?
“Just educate the public about the Data
Centres. If the public are aware of what’s
happening then they may not mind so much”
“A lot of the stigma that comes with data
sharing comes with people not knowing and not
being educated about the facts of how the
data’s being used.”

66. Value of private sector data for knowledge
and society
“Show the greater good of using my data – the
benefits of my data for the greater good.”
“I’d be really willing to sign up for this only if I
saw the benefits that my data provided. […] If
you see the impact that it has not only on your
life but on the life of the NHS as well and then
they are going to change their services, that’s
the greater good of it”.

67. Communication
Increased buy-in
Reassurances
and benefits
Demonstrate
impact
Data
management
processes
Turn the small
print into big
print!
Plain English
please!What is ‘good
use’ of data for
the benefit of
society?
Before we
came here, we
had absolutely
no idea what
the Data
Centre was

68. Conclusions from the dialogue
o Sharing of information about how the Data Centres operate
significantly increased levels of understanding of the benefits of
social research using private sector data.
o Improved communication and education about the processes by
which the Data Centres acquire, store, own and access private sector
data is vitally important to establish greater credibility.
o Data Centres should use case studies to demonstrate how the use of
private sector data in social research can lead to policy or service
improvements.
o The more reassurances are given about the data Centre processes
and the benefits of using private sector data the more buy-in can be
expected from members of the public.

69. Other reports (1)

70. Other reports(2)

71. 10
British
government
The
ONS
GP
surgery
The
NHS
The police
Academic
researchers
Charities
Online
retailers
The media / the
press
0%
Insurance
companies
Telecommunication
s companie
s
Local authority
Supermarkets
Internet companies
5
%
10
%
15
%
20
%
25
%
30
%
35
%
40
%
45
%
0% 5%
10%
15% 20% 25% 30% 35%
High trust in organisation generally (score
8-10)
40
%
45
%
50
%
Hightrustindata(score8-10)
“Please tell me on a score of 0-10 how much you personally trust each of the institutions below.”
“Please tell me on a score of 0-10 how much you personally trust each of the institutions below to use
your data appropriately.”
50%
© Ipsos MORI Version 1 |
RSS Ipsos Mori Report (2014) Public attitudes to the use and sharing of their
data
The “data trust
deficit”
Base: 2,019 GB adults, aged 16-
75
Source: Ipsos
MORI

72. Other reports (3)

73. Giving the last word to the participants…
Primary contact at ESRC:
Maria.Sigala@esrc.ac.uk
Thank you!
Vanessa Cuthill ESRC Deputy Director
(Evidence, Impact and Strategic Partnerships)