Understanding of the nature and detailed composition of ethnic groups remains key to a vast swathe of social science and human natural science. Yet ethnic origin is not easy to define, much less measure, and ascribing ethnic origins is one of the most contested and unstable research concepts of the last decade - not only in the social sciences, but also in human biology and medicine. As a result, much research remains hamstrung by the quality and availability of ethnicity classifications, constraining the meaningful subdivision of populations. This PhD thesis develops an alternative ontology of ethnicity, using personal names to ascribe population ethnicity, at very fine geographical levels, and using a very detailed typology of ethnic groups optimised for the UK population. The outcome is an improved methodology for classifying population registers, as well as small areas, into cultural, ethnic and linguistic groups (CEL). This in turn makes possible the creation of much more detailed, frequently updatable representations of the ethnic kaleidoscope of UK cities, and can be further applied to other countries. The thesis includes a review of the literature on ethnicity measurement and name analysis, and their applications in ethnic inequalities and geographical research. It presents the development of the new name to ethnicity classification methodology using both a heuristic and an automated and integrated approach. It is based on the UK Electoral Register as well as several health registers in London. Furthermore, a validation of the proposed name-based classification using different datasets is offered, as well as examples of applications in profiling neighbourhoods by ethnicity, in particular the measurement of residential segregation in London. The main study area is London, UK.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:498871 |
| Date | January 2007 |
| Creators | Mateos, Pablo |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/16145/ |
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