XClose

UCL Press

Home
Menu

Consumer Data Research

Consumer Data Research Cover
Return to results

Paul Longley, James Cheshire and Alex Singleton | April 2018

Format: 234x156mm 
Open Access PDF
ISBN: 978‑1‑78735‑388‑6
FREE
Paperback
ISBN: 978‑1‑78735‑389‑3
£17.99
Pages: 192

Free PDF download

Buy paperback now


About the book

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies.

About the author

Paul Longley is Professor of Geographic Information Science at UCL where he also directs the ESRC Consumer Data Research Centre. His research interests are focused around socioeconomic applications of GIScience, in geo-temporal demographics, retailing, genealogy and urban modelling, latterly often using Big Data analytics.

James Cheshire is a Senior Lecturer in Quantitative Human Geography at UCL and Deputy Director of the ESRC Consumer Data Research Centre. His research focuses on the analysis and visualisation of new forms of geographically referenced population data for social science.

Alex Singleton is Professor of Geographic Information Science at the University of Liverpool and Deputy Director of the ESRC Consumer Data Research Centre. His research explores how the complexities of individual behaviours manifest spatially and the ways in which they can be represented and understood though a framework of geographic data science.