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Title: Understanding internal migration in Britain at the start of the 21st century
Author: Dennett, Adam Raymond
ISNI:       0000 0004 2708 7716
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2010
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Along with changes in fertility, mortality and international migration, internal migration acts to affect population change in almost all areas of Britain. As well as changing the numbers of people in localities it will alter the structure and composition of populations, impacting upon the planning and allocation of resources to local communities. In order to plan effectively, knowledge of the flows of people within the country is essential, but with almost ten percent of the population of Britain changing their permanent place of residence every year, a complex system of flows between a multitude of origins and destinations is presented. There is a long history of studying internal migration in Britain; a history which owes much to the system of flows continually evolving. Monitoring this system can be problematic as unlike births and deaths, there is no compulsory mechanism to record the movements of people within the country. Data are accessed from different sources, each with their own idiosyncrasies which pose challenges for those wishing to build a complete understanding of the flows taking place. This thesis tackles the problem of building an understanding of internal migration in Britain where data are limited and patterns and processes complex. New methods for estimating incomplete data are presented, along with new techniques for analysing available datasets. Central to the understanding of internal migration patterns is the association of types of migrant with origin and destination areas; therefore one of the central contributions of this piece of work is the development of a new internal migration-based geodemographic area classification framework, designed to both assist in the analysis of internal migration data from the census used to build it and to offer a parsimonious system for the analysis of temporally rich but attribute poor non-census datasets.
Supervisor: Stillwell, J. ; Duke-Williams, O. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available