Consumer Data Research

Front matter

Consumer Data ` Research

Paul Longley, James Cheshire an x Singleton

L d Ale gle © | Q *UCLPRESS

Part One Provenance And Consumer Data Infrastructure

1. Consumer Registers as Spatial Data Infrastructure and their Use in Migration and Residential Mobility Research

Guy Lansley and Wen Li

Alyson Lloyd, James Cheshire and Martin Squires

3. Retail Areas and their Catchments Michalis Pavlis and Alex Singleton

4. Given and Family Names as Global Spatial Data Infrastructure Oliver O'Brien and Paul Longley fall wii

Part Two Dynamics And Consumer Data Infrastructures

5. Ethnicity and Residential Segregation Tian Lan, Jens Kandt and Paul Longley

Alexandros Alexiou, Dean Riddlesden and Alex Singleton

8. Smart Card Data and Human Mobility Nilufer Sari Aslam and Tao Cheng

9. Interpreting Smart Meter Data of UK Domestic Energy Consumers Anastasia Ushakova and Roberto Murcio

Part Three New Applications And Data Linkage

10. Geovisualisation of Consumer Data Oliver O’Brien and James Cheshire

11. Geotemporal Twitter Demographics Alistair Leak and Guy Lansley

12. Developing Indicators for Measuring Health-Related Features of Neighbourhoods Konstantinos Daras, Alec Davies, Mark A Green and Alex Singleton

13. Consumers in their Built Environment Context Alexandros Alexiou and Alex Singleton

Epilogue

Researching Consumer Data

Paul Longley, James Cheshire and Alex Singleton

CONSUMER DATA RESEARCH

Consumer Data Research -

An Overview

Paul Longley, James Cheshire and Alex Singleton

It has become a cliché to observe that new sources of Big Data are becoming available in ever greater variety, in unprecedented volumes and with ever more frequent temporal updating (velocity). This book

is about ‘consumer data’ that arise out

of every-day transactions for goods and services, carried out between individuals and organisations. Such data account

for an increasing real share of all of the characteristics and activities of active citizens today, and offer the prospect

of better understanding the nature and functioning of society.

Consumer data are not created for the edification of researchers and analysts. Instead, they are a by-product of the myriad consumer transactions that created them. This has important implications for the data’s content and coverage when they are reused for research purposes. First, the traces of (some kinds of) transactions or those people conducting them may be more evident or detailed than others, and this outcome is usually well beyond the control of the analyst. Second, different individuals have different wants, needs and spending power, and so some individuals in the population at large will be represented more prominently than others — and at the other extreme, those that consume nothing from a particular retailer / service provider will not be represented at all. A related point is that few consumer organisations have a monopoly of their markets, and many focus upon particular market niches. Taken together, this means that there

is bias in the content and coverage of consumer data sources, and that the source and operation of bias cannot be ascertained without reference to external sources.

In many ways these issues are akin to those that characterise volunteered or crowd sourced data — in that individuals need to feel motivated in order to contribute data, and the distinctive characteristics of those that feel motivated may affect the content and coverage of the resulting dataset (Haklay, 2010).

Introduction

This situation contrasts sharply with the design of conventional social surveys, where the principles of scientific sampling are used to ensure complete coverage of the relevant population of interest at the design stage. Nevertheless the quality of social surveys is diminished where acceptable response rates are not achieved, or there is bias in the relevant characteristics of those that respond to the surveys and those that do not. In this context, it is important to recognise that recent years have seen cumulative declines in response rates throughout the developed world (e.g. Sax et al 2003) and that in important respects social surveys are no longer a panacea for social science research. More generally, there is also no guarantee that we will be able to rely on the long-term availability of those traditional sources of data such as a Census of the Population, as within many countries these expensive and timeconsuming surveys have come under increasing threat in line with fiscal constraint (Singleton et al, 2017).

Many of the chapters in this book arise out of shared challenges that are faced by academics and the organisations that, to differing degrees, create consumer data. There are, of course, differences too: the timescales that characterise academic research offer horizon scanning that business organisations are less likely to have resource to facilitate; usually focused upon more operational matters, such as optimising the next set of sales figures. There may be tensions too, in that consumer data providers may safeguard their competitive position, while contributing to research that ultimately increases the competitiveness of their industrial sector as a whole. There are also differences of emphasis in method, technique and application that have evolved in different ways between the academic and business sectors. But it is also possible that there is shared interest in better understanding the form and functioning of social systems.

The research reported in this book has developed using the Consumer Data Research Centre’s (CDRC) ‘ladder of engagement’, whereby initial collaborations with consumer organisations are focused upon specific small MSc projects. A number of these have developed into co-sponsored PhD projects, or shared projects staffed by CDRC Data Scientists. Some data providers then progress to providing data for wider use by the academic community, under agreed terms set out in data licensing agreements. Finally, it is also possible to engage data providers in the co-production of data with the CDRC itself. Good examples are provided by our engagement with players in the domestic energy provision and retail sector who have participated

in the Master’s Research Dissertation Programme before going on to co-sponsor PhD research. This latter development

in turn led to providing CDRC with a nationwide dataset; which is available

to access by other researchers through

the CDRC service. The collaboration with the Local Data Company (LDC) reported in this book represents the highest rung of this ‘ladder of engagement’ and follows successful collaboration on MSc and PhD projects as well as the co-production of nationwide data with CDRC for further research and development.

Many consumer-facing organisations are highly sensitised to the risks of disclosure, although these risks are absolutely minimal where data are anonymized

prior to transfer, and appropriate resources to access them are put in place. To this end, CDRC uses a number of secure data facilities (one of which is accredited by the London Metropolitan Police), and CDRC researchers are familiar with using novel data access technologies such as secure links to sensitive data-sets held

by different organisations.

The approaches to consumer data research that are reported in this book come at an interesting time in the evolution of data landscapes in advanced economies. There

CONSUMER DATA RESEARCH is emerging consensus that data are

the world’s most valuable resource

(The Economist, 2017). To the behemoths of the Internet age — Alphabet, Amazon, Apple, Facebook, Microsoft — data are a strategic resource, largely to be acquired and siloed within corporate organisations. From the broader public good perspective, data provide infrastructure for individual and societal decision-making. For example, there is abundant evidence that Open

Data platforms and open Application Programming Interfaces (APIs) lead to wide economic and social benefits, with the data feeds from Transport for London (TfL) providing one of the most well-known exemplars. Such initiatives can lead to the creation and successive updating of new data infrastructures, although in many cases this process is impeded by difficulties in apportioning the cost of infrastructure creation and maintenance. Whilst there has been significant progress, the freer movement of data within and between jurisdictions and industrial sectors

still presents daunting challenges for government, not least because there exists no open market for many sources and forms of data.

Without a strong precedent, the work of CDRC relies heavily upon the attitudes to data licencing of a wide range of industrial partners with their own policies and procedures (over 20 data licensing agreements have been signed to date). These partners provide their data for the public good and pursue research questions that contribute to a more competitive economy and fairer society. Some of these shared objectives were integral to the 2017 Digital Economies Act, which includes provisions to require business to assist

in the compilation of national statistics. The spirit of the approach underpinning the chapters of this book is to go beyond narrow official requirements and engage in truly collaborative inter-sector research of common concern. It is our hope that these arrangements might flourish further in the future, for example through the

‘passporting’ of data originally acquired for government statistical purposes to researchers. Such arrangements would also have favourable implications for the preservation and curation of many sources of consumer data under the provisions for research exemptions of the General Data Protection Regulation (GDPR).

This vision begs a number of important strategic questions concerning the form and detail of the emerging data landscape:

1 Are Big Data to be thought of as a rival or non-rival resource? The siloed approach of large corporations suggests that data are a valuable commodity and strategic resource, the potency of which is diluted if data are shared with competitor “rivals”. Seen from this perspective, they are not to be traded or otherwise shared. Yet data sharing has been shown to leverage wide benefits, particularly if data platforms can be made open to the widest constituency of users.

2) Does GDPR present a threat to the creation and maintenance of datasets for research purposes, or an opportunity for researchers to create, maintain and preserve data-rich representations of social systems?

3) How can the Big Data ‘exhaust’ of consumer transactions and interactions be reused in representations of social systems that are genuinely inclusive? How can scientific methods be repurposed to analyse data that are created and possibly assembled without any scientific research design?

4) How can public trust and understanding of science be developed and maintained in support of research that realises more of the potential of consumer data?

CDRC’s mission includes the creation and maintenance of new measures of the ways in which ‘smart’ urban systems function, for example with respect to pedestrian

Introduction flows, household activity patterns and residential and social mobility. Any representation of a ‘smart’ system is necessarily incomplete, and it is important for analysts and public alike to understand the nature and extent of this incompleteness.

Furthermore, improved scientific understanding of the public is inextricably linked to improved public understanding of science, since only this is likely to bring informed consent for acquisition of the best data and the best research practices to take place.

There are rapid developments and changes in the digital data economy, ranging from renewed open data initiatives to the creation of new data silos within industry. Given its increasing real share of all data collected and its salience to understanding individual activities, attitudes and preferences, it seems clear that consumer data have an important role to play in developing tomorrow’s data infrastructures. The contributions to this book illustrate many of the ways in which academic engagement with customer-facing organisations can release consumer

data that will help us to better understand what is going on in contemporary society. Yet effective representation of consumer behaviour will not be achieved unless the sources and operation of bias in consumer datasets can be successfully accommodated. This argues for a research agenda that seeks to triangulate rich, salient and timely consumer data with more conventional census, administrative data and social survey sources.

Haklay, M. (2010). How good is volunteered geographical information? A comparative study

of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design, 37(4), 682-703.

Sax, L. J., Gilmartin, S. K. and Bryant, A. N. (2003). Assessing response rates and nonresponse bias in Web and paper surveys. Research in Higher Education, 44, 409-32.

Singleton, A. D., Spielman, S. and Folch, D. (2017). Urban Analytics. London: Sage.

The Economist (2017). “The world's most valuable resource is no longer oil, but data’. May 6. https:// www.economist.com/news/leaders/21721656-dataeconomy-demands-new-approach-antitrust-rulesworlds-most-valuable-resource