Geodemographics in an Academic Context

Talk given for the CSDR launch - 11/12th June 2015

Transcript

1. www.alex-singleton.com @alexsingleton Geodemographics in an Academic Context Alex Singleton Professor of Geographic Information Science Department of Geography and Planning Consumer Data Research Centre An ESRC Data Investment
2. http://www.google.co.uk/intl/en_uk/earth/ how?
3. https://www.flickr.com/photos/ bluesquarething/5512923662/
4. http://www.alex-singleton.com/r/2014/02/05/2011-census-open-atlas-project-version-two/
6. “What is needed is a solution which will pick out pattern from the detail, without loosing too much of the original information, and which will admit more detailed examination of parts of the pattern which become relevant to a particular issue or local area as and when required” Webber (1978, 275).
8. http://www.google.co.uk/intl/en_uk/earth/ 52: POORER FAMILIES, MANY CHILDREN, TERRACED HOUSING 51: YOUNG PEOPLE IN SMALL, LOW COST TERRACES 59: DEPRIVED AREAS AND HIGH- RISE FLATS 11: SETTLED SUBURBIA, OLDER PEOPLE Urban Adversity Affluent Achievers
9. 4 Challenges Transparency Feedback Responsiveness Universality
10. How can we make a classification more open?
11. http://public.cdrc.ac.uk/
12. http://www.opengeodemographics.com/
13. https://plus.google.com/u/0/communities/111157299976084744069
14. One classification to rule them all?
15. OAC 2001 OAC 2011
16. A1: Struggling suburbs A2: Suburban localities B1: Disadvantaged diaspora B2: Bangladeshi enclaves B3: Students and minority mix C1: Asian owner occupiers C2: Transport service workers C3: East End Asians C4: Elderly Asians D1: Educational advantage D2: City central E1: City and student fringe E2: Graduation occupation F1: City enclaves F2: Affluent suburbs G1: Affordable transitions G2: Public sector and service employ H1: Detached retirement H2: Not quite Home−Counties LOAC 2011
17. What if a classification is wrong?
19. http://esociety.publicprofiler.org/ 250k views - afternoon released
20. Postcode Search Propensity by e-Society Types 0" 20" 40" 60" 80" 100" 120" 140" 160" 180" 200" 220" 240" 260" 280" 300" Index"(Base"100)" Group"A":"E;unengaged" Group"B":"E;marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E;independents" Group"F":"Instrumental"E;users" Group"G":"E;business"users" Group"H":"E;"experts"
21. Feedback Origin 0" 20" 40" 60" 80" 100" 120" 140" 160" 180" 200" 220" 240" Index"(Base"100)" Group"A":"E:unengaged" Group"B":"E:marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E:independents" Group"F":"Instrumental"E:users" Group"G":"E:business"users" Group"H":"E:"experts"
22. Feedback Destination 0" 50" 100" 150" 200" 250" 300" 350" 400" 450" 500" Index"(Base"100)" Group"A":"E:unengaged" Group"B":"E:marginalised" Group"C":"Becoming"engaged" Group"D":"E"for"entertainment"&" shopping" Group"E":"E:independents" Group"F":"Instrumental"E:users" Group"G":"E:business"users" Group"H":"E:"experts"
23. How do we make classifications more responsive?
26. A: Hispanic and Kids B: Wealthy Nuclear Families C: Middle income, single family homes D: Native American E: Wealthy Urbanites F: Low Income and Diverse G: Old, Wealthy White H: Low Income Minority Mix I: African−American Adversity J: Residential Institutions, Young People Burgess (1925)
27. Where next?
28. Dynamic Social Topology • Places are more than a composite of attributes • Linked through interactions • Extension from purely domicile geography • New data sources • LEHD Origin-Destination Employment Statistics (LODES)
29. Many thanks…