Creating consistency in British Census Space a Nigel Walford and Kelly Hayles Centre for Earth and Environmental Science Research, School of Earth Sciences and Geography GISRUK06, 5-7 April 2006

Presentation Outline  Research Aim  Issues and previous research  Data Sources  Methods  Areal Interpolation  Dasymetric Mapping  Discussion  Conclusion a

Research Aim  To develop and implement a methodology for producing consistent small geographical units with associated estimates of key census counts spanning at least the British Population Censuses. Objectives  Assess alternative GIS-based methodologies for deriving this information from existing data sources  Examine alternative ways of visualising demographic and socio-economic changes between censuses. a

Issues  Previous work  Change files  Census tracts (England and Wales)   Reformatting SAS (Scotland)  LCT – Linking Censuses Through time  large spatial units  a

Issues cont’d  CATT – Consistent Areas Through Time (Daniel Exeter)  LUTs  Scotland only a

Issues cont’d  Census comparability  Definitions change  Questions change  Population base changes  Geographies  No digital boundaries existed of Tracts or 1981 EDs prior to Walford (2005) research  Changes occurring each census year for enumeration purposes a

Data Sources a Census Counts CDU, MIMAS  142 counts for 1981 (~130,000 EDs)  304 counts for 1991 (~148,000 ED/OAs)  454 counts for 2001 (~218,000 OAs)  Extracted counts and joined to DBFs due to Casweb download limits DBFs Edina ® UKBORDERS  Downloaded as whole countries for England, Wales and Scotland  Either as EDs or OAs  Split into GOR and joined to count tables 2001 National Outline Edina ® UKBORDERS  England, Wales and Scotland DBFs extracted and intersected with Strategi®  depict populated  non-populated areas

Data Sources cont’d a 1981 ED DBFs Walford (2005)  England, Wales and Scotland EDs were joined to counts from CDU Census Tract DBFs Walford (2005)  Available for England and Wales, identical over 2 years or amalgamations of two or more EDs from one or both years Census Tract Definitions Data Archive  Definitions comprised 490 counts linking for England and Wales only Strategi® Edina ® Digimap ®  Vector data derived from 1:250,000 topographic base.  Urban, water, forest features extracted using feature codes  ‘Other’ by polygon overlay

Data Sources cont’d Census Tracts DBFs DBFs a Census Tract Counts

Data Sources cont’d Strategi® England National Outline Polygonised Strategi® a

Methods  Areal Interpolation  Given data on one source zone, determines values based on another target system  Splits one boundary based on another and proportions the counts based on area  Allows aggregation back to another geographical level.  Disadvantages:  Assumes uniform distribution over ED/OA  Does not account for areas of zero population. a

Areal Interpolation cont’d a 1981 EDs East Anglia 2001 OAs

Areal Interpolation cont’d 7126 (0.39%) a 1981 EDs which now have been proportioned to the 2001 OA boundaries ED ED_AI LOSS 861 (0.047%)

Dasymetric Mapping  Using a-priori based weights to proportion counts by land use  Water and forest = 0  Urban = 0.9  Other = 0.1  Weight populated EDs where both ‘urban’ and ‘other’ exist  Weight all forest and water land use (x 0)  Aggregate based on ‘FEATLAB’ – land use and ED (value not sum)  Uses areal interpolation once weighting has been undertaken – disaggregate and aggregate on new 2001 lables  More accurate analysis as accounts for areas with no population. a

Dasymetric Mapping cont’d a Intersection of Land use with 1981 ED DBFs

Dasymetric Mapping cont’d a 3. Weight selected query for all fields by the ‘rate_luse’ field 4. Select non populated land uses and weight (x 0) 2. Join the frequencies to the MapInfo Table and perform a SQL Query to select EDs where both ‘urban’ and ‘other’ are > 0 1. Create frequencies in SPSS of all land use types

Discussion cont’d Total: 100 Other (100) Urban (100) Total: 100 Other (10) Urban (90) Total: 100 Other (10) Urban (90) Water (0) Forest (0) a Urban (100) Water (0) Total: 100

Dasymetric Mapping cont’d  Aggregate data (sum) where 2001 OAs contain the 1981 EDs. a ED ED_DM Loss 380 (0.02%) 380 (0.02%) 5273 (0.29%)  Loss in total population of 380 using DM, whilst 861 missing in AI.  Less people in external boundary in DM than AI

 Loss of population in outer boundary  Discrepancies in internal boundaries Discussion a 1981 and and 2001 (clipped boundaries)

Discussion cont’d ED ED_AI Loss 861 (0.047%) aED ED_DM Loss 380 (0.021%) Areal Interpolation Dasymetric Mapping

Discussion cont’d  Complexity of processing  Computationally intensive  Large number of files  Aggregation, then disaggregation and aggregation again a

Conclusion  Issues with both techniques and boundaries not aligning  Issues with assigning weights for land uses – what is best weight to use  Land uses – assumption that they are the same for both years, not always the case  Dasymetric mapping complex process, areal interpolation more simplistic to process a

Conclusion  Is Dasymetric Mapping a valid technique?  Is Dasymetric Mapping using land use weighting more superior than Areal Interpolation? a

References  Walford, N (2005) Creating historical and contemporary small-area geography in Britain: The creation of digital boundary data for 1971 and 1981 census units, International Journal of GIS, Vol.19, No.7 August 2005, a

Copyright Statements   Source: 2001 Census Output Area Boundaries. Crown copyright Crown copyright material is reproduced with the permission of the Controller of HMSO.   This work is based on data provided with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown and the ED-LINE Consortium.   © Crown Copyright/database right 20(yy). An Ordnance Survey/EDINA supplied service. a