Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Census 2022: An overview

804 views

Published on

An overview of the new ESRC Transformative research project given by Andy Newing to MRS Census and Geodemographic Group (CGG) hosted by GFK NOP, 19th November 2013 at Ludgate House, London

  • If you are looking for customer-oriented academic and research paper writing service try ⇒⇒⇒ WRITE-MY-PAPER.net ⇐⇐⇐ liked them A LOTTT Really nice solutions for the last-day papers
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • My friend sent me a link to to tis site. This awesome company. They wrote my entire research paper for me, and it turned out brilliantly. I highly recommend this service to anyone in my shoes. ⇒ www.HelpWriting.net ⇐.
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Hello! I can recommend a site that has helped me. It's called ⇒ www.WritePaper.info ⇐ They helped me for writing my quality research paper.
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

Census 2022: An overview

  1. 1. Census 2022: Transforming Small Area Socio-Economic Indicators through ‘Big Data’ An ESRC Transformative Research Project Andy Newing and Ben Anderson a.newing@soton.ac.uk Sustainable Energy Research Group (SERG) University of Southampton
  2. 2. Outline 2
  3. 3. Who are we? 3
  4. 4. Transforming Small Area Socio- Economic Indicators through ‘Big Data’ 4
  5. 5. Transforming Small Area Socio- Economic Indicators through ‘Big Data’ 5  Potential demise of decennial census – Challenge: robust alternative methods for creating small area socio-demographic and socio-economic indicators – Opportunity: transform the nature of these indicators using new analytic methods and datasets  Annual or sub-annual production?
  6. 6. Commercial transactional data  Geo-coded household data from utility service providers - collected as part of routine service provision  Can we derive traditional and novel small area population estimates and social indicators  Census-like and Census-plus 6
  7. 7. Owen. 2006. The rise of the machines—a review of energy using products in the home from the 1970s to today., Energy Saving Trust, London. 7
  8. 8. What can utilities data reveal about household characteristics?  Established links between household and householder characteristics and energy consumption (e.g. see AECOM 2011, Druckman and Jackson, 2008)  Based on understanding relationship between end use and end users  Consumption profiles reflect ownership and use of appliances, household size and characteristics and routines 8
  9. 9. What can utilities data reveal about household characteristics?  Elexon. 2013. Load profiles and their use in Electricity Settement, Elexon, London. 9
  10. 10. What can utilities data reveal about household characteristics? 10 Druckman, A. and Jackson, T. 2008. Household energy consumption in the UK: A highly geographically and socio- economically disaggregated model. Energy Policy, 36(8), pp.3177-3192
  11. 11.  Onzo. 2012. Onzo Application Detection Technology Onzo Ltd., London. 11
  12. 12. One-Minute Resolution Domestic Electricity Use Data (2008-2009)  Household study  Sample of 22 households  1 minute resolution mean power import  Linked to (limited) survey data on household characteristics  This dataset alone produces over 17m recorded observations 12
  13. 13. 13Original dataset: Richardson, I. and Thomson, M., One-Minute Resolution Domestic Electricity Use Data, 2008- 2009 [computer file]. Colchester, Essex: UK Data Archive [distributor], October 2010. SN: 6583, http://dx.doi.org/10.5255/UKDA-SN-6583-1.
  14. 14. 14Original dataset: Richardson, I. and Thomson, M., One-Minute Resolution Domestic Electricity Use Data, 2008-2009 [computer file]. Colchester, Essex: UK Data Archive [distributor], October 2010. SN: 6583, http://dx.doi.org/10.5255/UKDA-SN-6583-1.
  15. 15. 15Original dataset: Richardson, I. and Thomson, M., One-Minute Resolution Domestic Electricity Use Data, 2008-2009 [computer file]. Colchester, Essex: UK Data Archive [distributor], October 2010. SN: 6583, http://dx.doi.org/10.5255/UKDA-SN-6583-1.
  16. 16. 16 Original dataset: Richardson, I. and Thomson, M., One-Minute Resolution Domestic Electricity Use Data, 2008-2009 [computer file]. Colchester, Essex: UK Data Archive [distributor], October 2010. SN: 6583, http://dx.doi.org/10.5255/UKDA-SN-6583-1.
  17. 17. UoS ‘Smart Meter’ Data  Household study in two areas (case and control)  Up to 180 households over extended period  Detailed survey of household characteristics, behaviors and attitudes  Energy use (and other attributes) at one second resolution 17
  18. 18. 18
  19. 19. 19
  20. 20. ‘Big Data’  Huge datasets – UoS one second data produces over 500m records per month! – Aggregate and summarise but need to understand missing data – Nationally at 30 minute resolution – 94bn readings per month  Need to sample – Spatially representative and/or temporal – New ways of thinking about census data 20
  21. 21. Scope  Not seeking to produce robust nationally representative small area indicators ……  Identify available datasets  Focused exploratory work to demonstrate the potential of these datasets  Develop methodologies and algorithms  Build up an interested expert stakeholder group 21
  22. 22. Approaches  Regression – using median household consumption  Cluster analysis – driven by data 22
  23. 23. Approaches  Time series and periodicity – repeating patterns 23  All can be explored using standard settlement periods (30min) and alternative temporal resolution – and disaggregation by day, time of year etc.
  24. 24. Indicators  Traditional ‘census-type’ – household occupancy, – household age structure – household economic activity – OAC classification? ….. based on energy consumption  Novel census-plus indicators – energy inequality - energy consumption gini coefficient? 24
  25. 25. Commercial Value 25  Almost all households consume electricity  New source of geo-coded address point data with household attributes  Commercial transaction-driven ‘big data’ from utilities providers  Cannot mask consumption – reveals actual habits and routines!
  26. 26. Impacts  Commercial data owners  Commercial data aggregators  Local and national authorities and similar organisations  Future collaborations 26
  27. 27. 27
  28. 28. Questions and discussion  To what extent is this type of thinking already part of your business or organisation?  What datasets of this nature do you have access to or might be able to share?  What is your specific interest in work of this nature?  Would you be interested in contributing further? 28
  29. 29. Census 2022: Transforming Small Area Socio-Economic Indicators through ‘Big Data’ www.energy.soton.ac.uk /category/research/energy-behaviour/census- / Andy Newing a.newing@soton.ac.uk Sustainable Energy Research Group (SERG)

×