A new geodemographic classification of commuting flows for England and Wales

This paper aims to contribute to the area of geodemographic research through the development of a new and novel flow-based classification of commuting for England and Wales. In doing so, it applies an approach to the analysis of commuting in which origin-destination flow-data, collected as part of the 2011 census of England and Wales, are segmented into groups based on shared similarities across multiple demographic and socioeconomic attributes. K-means clustering was applied to 49 flow-based commuter variables for 513,892 interactions that captured 18.4 million of the 26.5 million workers recorded as part of the 2011 census of England and Wales. The final classification resulted in an upper-tier of nine ‘Supergroups’ which were subsequently partitioned to derive a lower-tier of 40 ‘Groups’. A nomenclature was developed and associated pen-portraits derived to provide basic signposting to the dominant characteristics of each cluster. Analysis of a selection of patterns underlying the nine-fold Supergroup configuration revealed a highly variegated structure of commuting in England and Wales. The classification has potentially wide-ranging descriptive and analytical applications within research and policy domains and the approach would be equally transferable to other countries and contexts where origin-destination data is disaggregated based on commuter characteristics

Geo-temporal Twitter demographics

This paper seeks and uses highly disaggregate social media sources to characterize Greater London in terms of flows of people with modelled individual characteristics, as well as conventional measures of land use morphology and night-time residence. We conduct three analyses. First, we use the Shannon Entropy measure to characterize the geography of information creation across the city. Second, we create a geo-temporal demographic classification of Twitter users in London. Third, we begin to use Twitter data to characterize the links between different locations across the city. We see all three elements as data rich, highly disaggregate geo-temporal analysis of urban form and function, albeit one that pertains to no clearly defined population. Our conclusions reflect upon this severe shortcoming in analysis using social media data, and its implications for progressing our understanding of socio-spatial distributions within cities

Using workplace population statistics to understand retail store performance

We explore the value of recently released workplace geographies and accompanying census-based workplace zone statistics (WZS) and an associated classification of workplace zones (COWZ). We consider how these data could support retailers in their operational and strategic decision making, including the evaluation of retail demand and retail store performance in localities where trade is driven by non-residential demand. In collaboration with major UK grocery retailer ‘The Co-operative Group’ we explore the relationship between workplace population composition and store trading characteristics using a series of case study stores within Inner London. We use empirical store trading data to identify store and product category level temporal sales fluctuations attributable to workplace populations. We also use census-derived flow data to identify the spatial origins of workplace population inflow. We identify that store performance exhibits characteristics attributable to demand driven by these populations. We conclude that workplace population geographies, WZS and the COWZ afford considerable potential for understanding drivers of store performance, observed store trading patterns and evaluation of retail store performance. We suggest that the next step is to build these populations and their micro geography spatial and temporal characteristics into predictive models and evaluate their potential for store performance evaluation and location-based store and network decision making within this sector

The applications of loyalty card data for social science

Large-scale consumer datasets have become increasingly abundant in recent years and many have turned their attention to harnessing these for insights within the social sciences. Whilst commercial organisations have been quick to recognise the benefits of these data as a source of competitive advantage, their emergence has been met with contention in research due to the epistemological, methodological and ethical challenges they present. These issues have seldom been addressed, primarily due to these data being hard to obtain outside of the commercial settings in which they are often generated. This thesis presents an exploration of a unique loyalty card dataset obtained from one of the most prominent UK high street retailers, and thus an opportunity to study the dynamics, potentialities and limitations when applying such data in a research context. The predominant aims of this work were to firstly, address issues of uncertainty surrounding novel consumer datasets by quantifying their inherent representation and data quality issues and secondly, to explore the extent to which we may enrich our current knowledge of spatiotemporal population processes through the analysis of consumer activity patterns. Our current understanding of such dynamics has been limited by the data-scarce era, yet loyalty card data provide individual level, georeferenced population data that are high in velocity. This provided a framework for understanding more detailed interactions between people and places, and what these might indicate for both consumption behaviours and wider societal phenomena. This work endeavoured to provide a substantive contribution to the integration of consumer datasets in social science research, by outlining pragmatic steps to ensure novel data sources can be fit for purpose, and to population geography research, by exploring the extent to which we may utilise spatiotemporal consumption activities to make broad inferences about the general population

Interaction Data Sets In The UK: An Audit

Interaction or flow data involves counts of flows between origin and destination areas and can be extracted from a range of sources. The Centre for Interaction Data Estimation and Research (CIDER) maintains a web-based system (WICID) that allows academic researchers to access and extract migration and commuting flow data (the so-called Origin-Destination Statistics) from the last three censuses. However, there are many other sources of interaction data other than the decadal census, including national administrative or registration procedures and large scale social surveys. This paper contains an audit of interaction data sets in the UK, providing detailed description and exemplification in each case and outlining the advantages and shortcomings of the different types of data where appropriate. The Census Origin-Destination Statistics have been described elsewhere in detail and only a short synopsis is provided here together with review of the interaction data that can be derived from other census products. The primary aims of the audit are to identify those interaction data sets that exist that might complement the census origin-destination statistics currently contained in WICID and to assess their suitability and availability as potential data sets to be held in an expanded version of WICID. Tables or flow data sets are included for exemplification. The paper concludes with a series of recommendations as to which of these data sets should be incorporated into a new information system for interaction flows that complement the census data and also provide opportunities for new research projects

Exploring small area demand for grocery retailers in tourist areas

This paper uses data from a major loyalty card scheme to draw insights about the characteristics of grocery expenditure by tourists. The authors explore the volume, value and composition of storebased visitor expenditure using consumer data from the loyalty card scheme. They focus on grocery spending at selected stores in Cornwall, a popular tourist destination in South West England. The loyalty card data provide a valuable source rarely available for academic investigations. The authors are able to analyse visitor spend by socio-economic and geodemographic characteristics, drawing a range of comparisons with residential demand from within the store catchment areas. They demonstrate that visitor grocery expenditure is complex and varies by store, destination and type of customer. The paper presents evidence to suggest that the current approaches used to estimate sales uplift and local-level economic impact from visitor demand are unable to account for the complexities of this form of expenditure. Based on these insights, the authors recommend that sophisticated modelling is employed to estimate the impact of visitor expenditure

Consumer Data Research

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