1. T OWARDS AN AREA CLASSIFICATION FOR MIGRATION ANALYSIS AND INTER - CENSAL MONITORING Presentation to the BSPS Annual Conference, Manchester 2008 Adam Dennett Centre for Interaction Data Estimation and Research School of Geography University of Leeds

2. P RESENTATION O UTLINE Background: Area classifications and their use in migration research An interaction data/migration classification: Justification and rationale Considerations for an interaction data classification A Trial classification: methodology and some preliminary results Moving forward: refining the classification and future research avenues

3. B ACKGROUND Classification of areas according to the individuals residing within has a long history: – Charles Booth 19 th Century London – Burgess and the Chicago School in 1920s – CACI (ACORN), Experian (MOSAIC), Eurodirect (CAMEO) – UK government (ONS) since the 1960s. Recent classifications for Local Authorities, Wards, SOAs, OAs & Health Authorities. Area Classifications

4. B ACKGROUND Dennett and Stillwell (2008) used a Local Authority classification designed by Vickers et al. (2003) to examine age/sex patterns of internal migration in Britain from the 2001 Census. Stillwell and Hussain (2008) used the same classification to examine patterns of ethnic group migration. Area classifications and migration research

5. At a relatively aggregate (Family) level, it can be argued that the process of counterurbanisation is continuing. At Group and Class levels, however, this is less so. There is evidence for a two tier ‘rural’ when Commuter belt is taken into consideration (even outside of city regions). Net migrants and net migration rates by district classification – all ages, 2000-01

6. A N INTERACTION DATA CLASSIFICATION Interaction data are inherently more complex than standard count data – origin and destination. One of the principal purposes of any classification is to simplify complex data to aid understanding. District level classifications such as those developed by ONS and Vickers et al. have been created without the use of migration/commuting variables. Underlying populations in areas may or may not be representative of migrant populations; thus classifying areas separately by their migrants may reveal things not shown in other general purpose classifications. Rationale – Classification to aid understanding

7. A N INTERACTION DATA CLASSIFICATION An new area classification will be useful as a framework for monitoring migration measured from other data sources post-2001 Further research need not be limited to using the classification directly: – The development of any classification necessitates the evaluation and selection of variables. Any variables selected are likely to be more helpful in explaining the migration system and thus useful for future migration research – e.g. the development of projection models. Rationale – Classification as part of the research process

8. C ONSIDERATIONS FOR THE NEW CLASSIFICATION Interaction data available at variety of scales in UK (OA to Region), although more variables available at coarser scales. Census provides more variables at a wider range of scales than any other data source in the UK. Finer grained data less attribute rich and more prone to small count perturbation. A classification of coarser areal units may encourage more false ecological inferences. Data at some levels not available for same units of measurement in UK (districts – Britain, Parliamentary constituencies – NI). Scale

9. C ONSIDERATIONS FOR THE NEW CLASSIFICATION Internal migration data available from a variety of sources disaggregated by numerous variables (especially when census data are used). Largest influence on population change in the UK. International migration data available from fewer sources, although census data are directly comparable with internal migration census data. Evidence for linkage with internal migration (Stillwell and Duke- Williams, 2005). Commuting data are similarly available from census and non-census sources. Phenomenon closely related to migration – especially where longer distance, longer stay commutes are concerned. Internal migration, International migration, Commuting?

10. C ONSIDERATIONS FOR THE NEW CLASSIFICATION It could be argued that the defining element of interaction data is the linkage between origins and destinations and the flow between those points. Classification of flows has been carried out before – ‘functional regionalisation’ used by Coombes et al. (1986, 2002) to create Travel To Work Areas creates sets of contiguous areas based upon commuting flow data. The same technique used with migration data to create Housing Market Areas (Coombes et al., 2004). Classifying flows tends to create sets of contiguous areas, although contiguity may be an unnecessary constraint in an interaction data classification. Classifying flows or individuals?

11. A N INTERACTION DATA CLASSIFICATION Scale: District level data used initially due to increased attribute availability, but only for Britain. Scope for trialling a ward level classification (including NI) at a later stage. Data: Internal and international migration data used initially. Experimentation with commuting data will come later as complex relationship with migration may confound initial clusters. Classification type: This classification will concentrate on classifying individuals rather than flows at this stage. Flow classification presents a separate set of problems and is likely to require a suit of classifications by variable. Initial decisions

12. A TRIAL CLASSIFICATION A standard clustering methodology similar to the one suggested by Milligan (1996) was used to create the classification: 1.408 Local authority districts in Britain were chosen as objects to cluster 2.56 variables were selected from an original list of 5559 taken from 7 2001 Census SMS level 1 tables. Domains included: – In/out/within/international/no usual address migration rates for age, ethnicity, economic activity and long term illness – Migration efficiency rates (due to no suitable denominator) for Socio-economic status, family status and housing tenure Selection through a combination of correlation analysis, principal components analysis and analysis of standard deviation stats. Methodology

13. A TRIAL CLASSIFICATION 3. All variables were standardised by z-scores to avoid any bias in the classification clusters 4. Euclidean distance was chosen as measure of proximity used to compute clusters 5. Ward’s hierarchical clustering algorithm was used in the initial partition as it does not require definition of the number of clusters prior to the clustering procedure – a suitable number of clusters chosen from Ward’s algorithm output… Methodology

14. A TRIAL CLASSIFICATION Methodology

15. The initial partition of 8 clusters has produced an area classification classifying all but two areas in Britain (Isles of Scilly and Merthyr Tydfil):

16. A TRIAL CLASSIFICATION In-migration and out-migration of young migrants (1, 2, 5 and 6) This higher turning over of the younger population is joined by the out-migration of older and economically inactive migrants (7, 8 and 25). International immigration of younger individuals (13, 14, 15) is also of heightened importance, as too are movements of those of higher socio-economic status who are not moving in household groups (34, 36, 38) Within district moves and moves of those in lower socio-economic categories are of much less importance in this cluster (9-12 and 39, 40, 41 and 43). Cluster 2 Z-scores defining cluster 2

17. A TRIAL CLASSIFICATION Defined more by the variables that are below average than the variables that are above… However, migrants moving into or from owner occupied accommodation are of above average importance (48 and 49) As too are migrants in the middle to lower socio-economic groups (38, 39, 40 and 43) Migrants who are part of families (couple and single parent) are also of increased importance (54 and 55) In, out and within area migration along with international migration are all of below average importance – low migration Cluster 3 Z-scores defining cluster 3

18. A TRIAL CLASSIFICATION Defined principally by two main types of migrant: Firstly there are the young (often student) in-migrants and within district migrants (1, 9, 44 and 45). Then there are the slightly older, non-white and economically inactive within-district migrants (10-14, 22 and 26). Migration of individuals in other moving groups and non-family households is prevalent (51 and 53), as is migration into and out of communal establishments (56). International immigration of all ages is above average in this cluster (17-20 and 30) as is the migration of economically inactive migrants in general. Cluster 6 Z-scores defining cluster 6

19. A TRIAL CLASSIFICATION Initial classification has produced a set of areas with distinctive characteristics in line with what might be expected (e.g. London, London periphery, university towns, ex- industrial economically more depressed areas, rural periphery all forming distinct clusters). This suggests there is a sound base for further refining of this individual based classification to produce an optimum classification of this type. Preliminary results

20. M OVING FORWARD Experimentation with the addition, omission and substitution of variables. Which variables ARE most important. What impact does the addition/subtraction/substitution of variables (such as commuting or distance) have on the cluster output? Does 8 clusters remain the optimum solution? Experimentation with different measures of proximity and methods of standardisation if necessary. Optimisation of the classification through the use of an optimising clustering algorithm such as k-means. A different classification based on migrant flows rather than migrants? Wards rather than districts? Refining the classification

21. M OVING FORWARD What does a final interaction classification reveal? – Pen portraits and cluster names which will assist in the comprehension migration profiles for different districts – Thorough comparison with existing district classifications… A finalised interaction data/migration classification

22. M OVING FORWARD How might this classification add value to new migration research? – Analysis of 2001 and post-2001 Patient re-registration migration data in the context of this 2001 based classification: What changes are there? Attribute poor patient data enhanced by attribute rich classification. Projecting migration beyond 2008 – Final suite of variables used in the classification will be those most important in presenting a comprehensive picture of migration. There would therefore be scope in the future for incorporating these variables into models that could predict future migration patterns in Britain. A finalised interaction data/migration classification

23. T HANK YOU Adam Dennett – School of Geography, University of Leeds http://www.geog.leeds.ac.uk/people/a.dennett/ a.r.dennett@leeds.ac.uk