1. Zone design methods for epidemiological studies
Samantha Cockings, David Martin
Department of Geography
University of Southampton, UK
Thanks to:
Arne Poulstrup, Henrik Hansen
Medical Office of Health, Province of Vejle, Denmark

2. Why use areas? No choice - data only available for areas
Confidentiality
Cost
Through choice
Believe some phenomena are area-level
Rates/ratios
Visualisation/mapping
Decision-making/planning

3. Problems with using areas
Modifiable areal unit problem (MAUP)
Scale
Aggregation
For a given set of data, different
aggregations/zoning systems will often
show apparently different spatial
patterns in the data (Openshaw, 1984)
Ecological fallacy
Relationships between variables which are observed
at one level of aggregation may not hold at the individual,
or any other, level of aggregation (Blalock, 1964)
Small numbers/instability of rates
Non-nesting units

4. Recent developments in (UK) automated zone design methods/tools
2001 UK Census of Population
Automated design of Output Areas (OAs)
Martin et al (2001)1; Martin (2002)2
Based on Automated Zoning Procedure (AZP)
Openshaw (1977)3; Openshaw & Rao (1995)4
Automated Zone Matching software (AZM)
Martin (2002)5
1 Environment & Planning A, 33, Transactions of the IBG, NS, 2,
2 Population Trends 108, Environment & Planning A, 27,
5 IJGIS, 17,

5. Methods Building blocks Aggregated zones
Automated zone design … iterative recombination
Building blocks
Aggregated
zones
Initial random aggregation
Iterative recombination
Maximise objective function
Martin, D (2002), Population Trends, 108, p.11

6. How can automated zone design help in environment and health studies?
Explore sensitivity of results to MAUP
Design sets of ‘optimal’ purpose-specific zones
Stability of estimates
Zones of homogeneous population size?
Exploring spatial patterning of disease
Zones of homogeneous rates?
Analysing relationships between variables
Zones of homogeneous risk/confounding factors?
Barriers/boundaries
Zones constrained by geog. features or admin. boundaries

7. Empirical study 1: Pre-aggregated data Morbidity and deprivation in SW England
County of Avon (1991 Census)
1970 enumeration districts
177 wards
Premature (0-64 years) limiting long term illness (LLTI)
Townsend deprivation score
Standardisation to England & Wales

8. © Crown copyright/ED-LINE Consortium, ESRC/JISC supported

9. © Crown copyright/ED-LINE Consortium, ESRC/JISC supported

10. © Crown copyright/ED-LINE Consortium, ESRC/JISC supported

11. © Crown copyright/ED-LINE Consortium, ESRC/JISC supported

12. Aims
Explore sensitivity of association at different scales (population size)
Explore sensitivity of association for different aggregations at a given scale
Explore ‘robustness’ of ED and ward level zoning systems for this type of spatial analysis

13. AZM software
©David Martin

14. target 3250; mean 0-64 pop. 3713
© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

15. © Crown copyright/ED-LINE Consortium, ESRC/JISC supported

16. Correlation (Townsend score and LLTI SMR) against mean pop
Correlation (Townsend score and LLTI SMR) against mean pop. size … the scale effect
Wards
EDs

17. Standard deviation (pop. 0-64) against mean pop
Standard deviation (pop. 0-64) against mean pop. size … the scale effect
Wards
EDs

18. Correlation (LLTI-Townsend) vs
Correlation (LLTI-Townsend) vs. mean population size at given scale … the aggregation effect

19. Results
Observed association affected by choice of zoning system – MAUP/ecological fallacy
Automated zoning systems demonstrating greater stability of population size, higher correlations
Generally increasing Townsend-LLTI correlation with increasing zone size (pop.) and iterations
ED and ward correlations at low end of variation at given scale
Neighbourhood scale of ~3000 for UK?

20. Empirical study 2: Individual level data Dioxins and cancer, Kolding, Denmark
Background
c.50,000 residents
Airborne carcinogenic dioxin
Data
Geo-referenced addresses of residents
Roads, rivers, lakes
Buildings/urban areas

21. Possible zone design criteria
Population size: threshold/target
Physical boundaries
Roads, rivers, lakes
Shape
Homogeneity
Built environment - dwelling type, tenure
Socio-economic - education, income, occupation

23. Methods: Thiessen polygons around addresses
Creation of space-filling polygons such that each polygon encloses the space which is closer to its own point than to any other.

24. Methods: Using constraining features – roads and rivers

25. Methods: Clipped thiessen polygons

26. Illustrative zoning system from AZM: target 300, threshold 250

27. Next steps Other design constraints
Physical boundaries in zone design process
Homogeneity
Built environment
Social environment
Use zones to calculate rates of cancer
Sensitivity analysis

28. Conclusions
All zoning systems are imposed and should not be considered neutral or stable
Zone design methods offer:
The ability to explore the sensitivity and robustness of existing and alternative zoning systems
The ability to design purpose-specific zoning systems according to pre-defined criteria

29. Environment and health studies: What are we trying to model?
Points?
Health
Outcome
People
Points/areas?
Risk factors
Individual level
Area level
Confounding
factors
Points?
Areas?
Predisposing: age, sex,
ethnicity, genetics, birthweight
Lifestyle: smoking, diet, exercise,
alcohol
Socio-economic: occupation,
income, education
Pollution: air, water, noise
‘Neighbourhood’: services, housing type/quality, ethnic groupings/population
mixing, deprivation, crime, support networks
‘People’/‘Composition’
‘Place’/’Context’

30. Standard deviation (0-64) vs
Standard deviation (0-64) vs. mean population size for different aggregations at a given scale