Implications for ethnic analysis of research surveys and administrative datasets

1. Using Intelligent Systems to infer ethnicity from names Implications for ethnic analysis of research surveys and administrative datasets Richard Webber Visiting Professor, Department of Geography, UCL / OriginsInfo Oxford, 18 July 2006

2. Name and address Daniele Ceccomori 16a Broadlands Road London N6 4AN

3. Intelligent Inferences Italy Central Italy Young Daniele Ceccomori 16a Broadlands Road London N6 4AN Male 1880 - 1939 Subdivided house Global Connections

4. Monica / Stage Age Daniele Ceccomori 16a Broadlands Road London N6 4AN

5. Mosaic / Acorn Daniele Ceccomori 16a Broadlands Road London N6 4AN Global Connections

6. Nam Pehchan / Sangra / Origins Italy Central Italy Daniele Ceccomori 16a Broadlands Road London N6 4AN

7. ‘Ethnic’ coding systems • Based typically on surname element of name • Typically targetting a single or regional group • South Asians (UK) • Hispanics (US) • East Asians (US) • Calibrated against a reference database • Optimised for a specific geographical area • May be delivered using bespoke software application

8. ‘Onomastic’ coding systems • Categories based on common naming practices • Based on interconnection between personal and family names • May or may not correspond to ethnic, linguistic, religious or cultural categorisations • No external validation • Global coverage and territorial independence

9. Example of Onomastic codingBrent North Electoral Register

10. Example of Onomastic analysis :Computer Studies graduates, City University, 2006

11. Optimising ‘onomastic’ classifications Origins - Australia

12. ‘Origins’ • Onomastic coding system • Base files • UK / Ireland / Australia • France • Spain • Italy • Netherlands • Romania • Norway • Number of unique names • Family names 620,000 • Personal names 200,000 • Number of categories • 195 onomastic types • 13 onomastic groups

13. Key Origins Groups European English Celtic Western European Hispanic Nordic Eastern European Jewish and Armenian Rest of the World African Muslim South Asian Sikh East Asian Japanese

14. General Strategy • Access universal file

15. General Strategy • Access universal file • Personal name • Family name • Geodemographic cluster • Regional division

16. General Strategy • Access universal file • Personal name • Family name • Geodemographic cluster • Regional division • Rank names by frequency • Names of known origin (eg Peter, John, Patel, Rees) • Names of unknown or unclear origin

17. General Strategy • Access universal file • Personal name • Family name • Geodemographic cluster • Regional division • Rank names by frequency • Names of known origin (eg Peter, John, Smith, Rees) • Names of unknown or unclear origins • Unknown or unclear names • Triage • Text mining • Geodemographic profiling • Regional analysis

18. Naming practices as indicators of cultural integration

19. Triage example : Lorcan

20. Triage example : Ourania • UK total : 88 • 11.4% have British family name • 27.3% have Greek Orthodox family name • 61.4% have family names that have not been classified • Greek Orthodox total : 24 • 62.5% have Greek family names • 37.5% have Greek Cypriot family names

21. Text mining to find Nigerians

22. Italy : names occurring only in South Tyrol

23. Netherlands : names exclusive to multi-cultural geodemographic clusters

24. General Strategy • Access universal file • Personal name • Family name • Geodemographic cluster • Regional division • Rank names by frequency • Names of known origin (eg Peter, John, Smith, Rees) • Names of unknown or unclear origins • Unknown or unclear names • Triage • Text mining • Geodemographic profiling • Regional analysis • Extend to other countries • Compare frequencies between countries

25. Personal names : Spain, Italy, UK

26. Section of current family name reference file • Current family name total 562,558

27. Arbitration and the use of confidence scores

28. Validation : Comparing census with name based classifications

29. Validation : Gender

30. Research Questions • The size of different minority groups • Their regional dispersion • Their degree of residential integration • Their success in improving their social position • The growth or decline in their numbers

31. ‘Bird’ names

32. River endings‘-bourne’‘-burn’

33. ‘-son’ Patronymic endings ‘-s’

34. Electors with ‘Welsh’ surnames

35. Destinations of Irish migrants

36. Destinations of Cornish migrants

37. Destinations of ‘Cornish’ migrants in Australia

38. Destinations of Cornish migrants in the US

39. US : Destinations of migrants from Cornwall (above) and Devon (below)

40. Turkish migrants in Greater London

41. Ethiopian migrants in Greater London

42. West London postcodes where Hindus and Sikhs are the majority community

43. A contemporary ethnic map of London

44. Evaluating economic and social integration

45. Residential segregation : selected Local Authorities

46. Neighbourhood segregation in Blackburn

47. Neighbourhood segregation in Leicester

48. Residential segregation : selected communities

49. Using Intelligent Systems to infer ethnicity from names Implications for ethnic analysis of research surveys and administrative datasets Richard Webber Visiting Professor, Department of Geography, UCL / OriginsInfo Oxford, 18 July 2006