Lansley, G; Li, W; (2018) Consumer Registers as Spatial Data Infrastructure and their Use in Migration and Residential Mobility Research. In: Longley, PA and Cheshire, J and Singleton, A, (eds.) Consumer Data Research. (pp. 15-27). UCL Press: London, UK.

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Abstract

This chapter outlines efforts to devise modelled estimates of population change at a small-area level using annual registers that blend consumer and voter registration data. Names and addresses of individuals are routinely collected by governments and commercial organisations. However, there have been few attempts by academics to pool the data in order to track population changes despite the registers representing the majority of the adult population. Therefore, the possibility of linking databases for chronological pairs of years could provide a unique insight into population dynamics on an annual basis. Aligned with consumer data analytics, this information could reveal important statistics about the United Kingdom’s changing social structure and how it varies geographically – with far more frequent refresh than available from comprehensive government sources such as the Census of Population. Comprehensive models of Consumer Registers as Spatial Data Infrastructure and their Use in Migration and Residential Mobility Research Guy Lansley and Wen Li migration at a household level would give us the opportunity to develop an understanding of social mobility and asset accumulation through linkage to other geographic datasets. In this chapter, we present work on the 2013 and 2014 Consumer Registers produced by CACI Ltd (London, UK). The registers comprise the public version of the Electoral Register (sometimes termed the ‘edited register’) and are supplemented by a range of unattributed consumer data sources. Together, these population databases provide near complete coverage of the adult population at the individual level and are consolidated on an annual basis. However, the data only contain information on adult individuals’ names and postal addresses and lack any demographic variables. In addition, due to the nature of their data collection and amalgamation, the consumer data are of unknown provenance. We have therefore developed novel data-linkage techniques in order to assess the completeness of the population recorded prior to modelling apparent trends from these pooled data. Set in the context of harnessing information on population dynamics from data linkage between two registers, this study has three broad aims. First, to devise an appropriate technique to match addresses. Second, to estimate household dynamics by linking names at matched addresses. And finally, to estimate migration by modelling the movements of those that have left and joined addresses – specifically between 2013 and 2014. We will explore the feasibility of this model as a means of representing migration and social mobility.

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