The role of digital trace data in supporting the collection of population statistics – the case for smart metered electricity consumption data
The role of digital trace data in supporting the collection of population statistics – the case for smart metered electricity consumption data
Debates over the future of the UK's traditional decadal census have led to the exploration of supplementary data sources, which could support the provision of timely and enhanced statistics on population and housing in small areas. This paper reviews the potential value of a number of commercial datasets before focusing on high temporal resolution household electricity load data collected via smart metering. We suggest that such data could provide indicators of household characteristics that could then be aggregated at the census output area level to generate more frequent official small area statistics. These could directly supplement existing census indicators or even enable development of novel small area indicators. The paper explores this potential through preliminary analysis of a ‘smart meter-like’ dataset, and when set alongside the limited literature to date, the results suggest that aggregated household load profiles may reveal key household and householder characteristics of interest to census users and national statistical organisations. The paper concludes that complete coverage, quasi-real time reporting, and household level detail of electricity consumption data in particular could support the delivery of population statistics and area-based social indicators, and we outline a research programme to address these opportunities.
census, energy monitoring, small area statistics, digital trace data, big data, census2022
1-13
Newing, A.
e1c52fa1-4438-48f9-b809-e113a2c5691b
Anderson, B.
01e98bbd-b402-48b0-b83e-142341a39b2d
Bahaj, A.S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
James, P.A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
20 July 2015
Newing, A.
e1c52fa1-4438-48f9-b809-e113a2c5691b
Anderson, B.
01e98bbd-b402-48b0-b83e-142341a39b2d
Bahaj, A.S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
James, P.A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
Newing, A., Anderson, B., Bahaj, A.S. and James, P.A.B.
(2015)
The role of digital trace data in supporting the collection of population statistics – the case for smart metered electricity consumption data.
Population, Space and Place, .
(doi:10.1002/psp.1972).
Abstract
Debates over the future of the UK's traditional decadal census have led to the exploration of supplementary data sources, which could support the provision of timely and enhanced statistics on population and housing in small areas. This paper reviews the potential value of a number of commercial datasets before focusing on high temporal resolution household electricity load data collected via smart metering. We suggest that such data could provide indicators of household characteristics that could then be aggregated at the census output area level to generate more frequent official small area statistics. These could directly supplement existing census indicators or even enable development of novel small area indicators. The paper explores this potential through preliminary analysis of a ‘smart meter-like’ dataset, and when set alongside the limited literature to date, the results suggest that aggregated household load profiles may reveal key household and householder characteristics of interest to census users and national statistical organisations. The paper concludes that complete coverage, quasi-real time reporting, and household level detail of electricity consumption data in particular could support the delivery of population statistics and area-based social indicators, and we outline a research programme to address these opportunities.
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Accepted/In Press date: 22 May 2015
Published date: 20 July 2015
Keywords:
census, energy monitoring, small area statistics, digital trace data, big data, census2022
Organisations:
Energy & Climate Change Group
Identifiers
Local EPrints ID: 379449
URI: http://eprints.soton.ac.uk/id/eprint/379449
ISSN: 1544-8444
PURE UUID: d6121a5d-3782-4e5e-9c6f-13ec27c51ad8
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Date deposited: 28 Jul 2015 12:31
Last modified: 15 Mar 2024 02:46
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Author:
A. Newing
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