Applied Spatial Analysis and Policy

, Volume 4, Issue 4, pp 281–300 | Cite as

Internal and External Validation of Spatial Microsimulation Models: Small Area Estimates of Adult Obesity

  • Kimberley L. Edwards
  • Graham P. Clarke
  • James Thomas
  • David Forman


Spatial microsimulation models can be used to estimate previously unknown data at the micro-level, although validation of these models can be challenging. This paper seeks to describe an approach to validation of these models. Obesity data in adults were estimated at the small area level using a static, deterministic, spatial microsimulation model called SimObesity. This model utilised both Census 2001 data and the Health Survey for England for 2004–2006. Regression analysis was used to identify the covariates that were the strongest predictors of obesity and these were used as the model input variables. The model was calibrated using regression and equal variance t-tests. Two methods of external validation were undertaken; aggregating obesity data to a coarser geographical level at which obesity data was available, and secondly using small area level cancer data for tumour sites known to be correlated to obesity. The output obesity data were mapped and statistically significant hot (cold) spots of high (low) prevalence of obesity identified. Both internal and external validation showed low errors, suggesting this was a satisfactory simulation. Statistically significant hot and cold spots of (simulated) obesity prevalence existed, even after adjusting for age. This paper emphasises three steps to validation of spatial microsimulation models: 1. Accurate simulations require strong correlations between the input and output variables; 2. It is essential to internally validate the models; 3. Use all means possible to externally validate the model.


Obesity Small area estimation Spatial microsimulation modelling 



KLE would like to thank NATSEM, University of Canberra, for the invite to spend a sabbatical with them working on spatial microsimulation modelling techniques. The 2001 Census statistics and boundary data used in this paper are Crown Copyright produced by the Office for National Statistics. Licensed for academic use by the Economic and Social Research Council and the Joint Information Systems Committee Census Programme. The Census data service provider is the Census Dissemination Unit through the Manchester Information and Associated Services (MIMAS) of Manchester Computing, University of Manchester, through an interface called CASWEB. The boundary data service provider is the Census Geography Data Unit (UK Boundary Outline and Reference Database for Education and Research Study: UKBORDERS) via Edinburgh University Data Library. The 2001 Census Super Output Area and Ward Boundaries are Crown copyright 2003 where Crown copyright material is reproduced with the permission of the Controller of HMS. The national surveys are available from UK Data Archives managed by the University of Essex (


  1. Allman-Farinelli, M. A., Chey, T., Bauman, A. E., Gill, T., & James, W. P. (2008). Age, period and birth cohort effects on prevalence of overweight and obesity in Australian adults from 1990 to 2000. European Journal of Clinical Nutrition, 62(7), 898–907.CrossRefGoogle Scholar
  2. Anderson B (2007). Creating Small Area Income Estimates for Wales: spatial microsimulation modelling. <>. Chimera Working Paper 2007-11, Ipswich: University of Essex.
  3. Anderson B (2009). Welsh Small Area Estimates of Income Deprivation <> Centre for Research in Economic Sociology and Innovation (CRESI) Working Paper 2009-04, Colchester: University of Essex
  4. Asthana, S., Curtis, S., Duncan, C., & Gould, M. (2002). Themes in British health geography at the end of the century: a review of published research 1998–2000. Social Science & Medicine, 55, 167–173.CrossRefGoogle Scholar
  5. Ballas, D. (2004). Simulating trends in poverty and income inequality on the basis of 1991 and 2001 census data: a tale of two cities. Area, 36(2), 146–163.CrossRefGoogle Scholar
  6. Ballas, D., & Clarke, G. (2001). Modelling the local impacts of national social policies: a spatial microsimulation approach. Environment and Planning C: Government and Policy, 19, 587–606.CrossRefGoogle Scholar
  7. Ballas, D., Rossiter, D., Thomas, B., Clarke, G., & Dorling, D. (2005). Geography matters: simulating the local impacts of national social policies. York: Joseph Rowntree Foundation.Google Scholar
  8. Ballas, D., Clarke, G., Dorling, D., Rigby, J., & Wheeler, B. (2006). Using geographical information systems and spatial microsimulation for the analysis of health inequalities. Health Informatics Jounal, 12(1), 65–79.CrossRefGoogle Scholar
  9. Baum, C. L., & Ruhm, C. J. (2009). Age, socioeconomic status and obesity growth. Journal of Health Economics, 28(3), 635–48.CrossRefGoogle Scholar
  10. Bergstrom, A., Pisani, P., Tenet, V., Wolk, A., & Adami, H. (2001). Overweight as an avoidable cause of cancer in Europe. International Journal of Cancer, 91(3), 421–430.CrossRefGoogle Scholar
  11. Berrington de Gonzalez, A., Sweetland, S., & Spencer, E. (2003). A meta-analysis of obesity and the risk of pancreatic cancer. British Journal of Cancer, 89(3), 519–523.CrossRefGoogle Scholar
  12. Bianchini, F., Kaaks, R., & Vainio, H. (2002). Overweight, obesity, and cancer risk. The Lancet Oncology, 3(9), 565–574.CrossRefGoogle Scholar
  13. Birkin, M., & Clarke, G. P. (2010). The enhancement of spatial microsimulation models using geodemographics, working paper, School of Geography, University of LeedsGoogle Scholar
  14. Clarke, G. (1996). Microsimulation: an introduction. In G. Clarke (Ed.), Microsimulation for urban and regional policy analysis (pp. 1–9). London: Pion.Google Scholar
  15. Clarke, G., Eyre, H., & Guy, C. (2002). Deriving Indicators of Access to Food Retail Provision in British Cities: Studies of Cardiff, Leeds and Bradford. Urban Studies, 39(11), 2041–2060.CrossRefGoogle Scholar
  16. Coen, S. E., & Ross, N. A. (2006). Exploring the material basis for health: Characteristics of parks in Montreal neighbourhoods with contrasting health outcomes. Health & Place, 12, 361–71.CrossRefGoogle Scholar
  17. Communities and Local Government web site has details on The Index of Multiple Deprivation 2004 (accessed July 2010)
  18. Communities and Local Government web site has details on The Index of Multiple Deprivation 2007 (accessed January 2009)
  19. Cummins, S., Curtis, S., Diez-Roux, A. V., & MacIntyre, S. (2007). Understanding and representing ‘place’ in health research: A relational approach. Social Science & Medicine, 65, 1825–38.CrossRefGoogle Scholar
  20. Curtis, S., Cave, B., & Coutts, A. (2002). Is urban regeneration good for health? Perceptions and theories of the health impacts of urban change. Environment and Planning C—Government and Policy, 20(4), 517–34.CrossRefGoogle Scholar
  21. Danaei, G., Vander Hoorn, S., Lopez, A. D., Murray, C. J. L., Ezzati, M., & the Comparative Risk Assessment collaborating group (cancers). (2005). Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors. The Lancet, 366, 1784.CrossRefGoogle Scholar
  22. Dummer, T. J., Gibbon, M. A., Hackett, A. F., Stratton, G., & Taylor, S. R. (2005). Is overweight and obesity in 9-10-year-old children in Liverpool related to deprivation and/or electoral ward when based on school attended? Public Health Nutrition, 8(6), 636–41.CrossRefGoogle Scholar
  23. Edwards K. L. Defining & mapping obesogenic environments in children. In: Lake AA, Townshend T, Alvanides S (eds). Obesogenic Environments: complexities, perceptions and objective measures. Blackwell Publishing Ltd, London, in print [accepted March 09]Google Scholar
  24. Edwards, K. L., & Clarke, G. (2009). The design and validation of a spatial microsimulation model of obesogenic environments in Leeds: SimObesity. Social Science and Medicine (under review).Google Scholar
  25. Frezza, E. E., Wachtel, M. S., & Chiriva-Internati, M. (2006). Influence of obesity on the risk of developing colon cancer. Gut, 55(2), 285–291.CrossRefGoogle Scholar
  26. Gatrell, A. C. (2002). Geographies of health: an introduction. Oxford: Blackwell.Google Scholar
  27. Hardy, R., Wadsworth, M., & Kuh, D. (2000). The influence of childhood weight and socioeconomic status on change in adult body mass index in a British national birth cohort. International Journal of Obesity, 24(6), 725–34.CrossRefGoogle Scholar
  28. Huang, Z., & Williamson, P. (2001). A comparison of synthetic reconstruction and combinatorial optimisation approaches to the creation of small-area microdata. Working paper 2001/02, Department of Geography, University of Liverpool [online] (accessed Feb 2009)
  29. IOTF (International Obesity Task Force)., accessed December 2009
  30. Josefson, D. (2001). Obesity and inactivity fuel global cancer epidemic. BMJ, 322, 945.CrossRefGoogle Scholar
  31. Kaaks, R., Lukanova, A., & Kurzer, M. S. (2002). Obesity, endogenous hormones, and endometrical cancer risk:a synthetic review. Cancer Epidemiology, Biomarkers & Prevention, 11(12), 1531–1543.Google Scholar
  32. Key, T., Allen, N., Spencer, E., & Travis, R. (2002). The effect of diet on risk of cancer. Lancet, 360(9336), 861–868.CrossRefGoogle Scholar
  33. Kulldorf, M. (2006). SatScan user guide for version 7.
  34. Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496.CrossRefGoogle Scholar
  35. Langford, I. H. (1994). Using Empirical Bayes estimates in the geographical analysis of disease risk. Area, 26(2), 142–149.Google Scholar
  36. Larsson, S. C., & Wolk, A. (2007). Obesity and colon and rectal cancer risk: a meta-analysis of prospective studies. The American Journal of Clinical Nutrition, 86(3), 556–565.Google Scholar
  37. Leyland, A. H., & Davies, C. A. (2005). Empirical Bayes methods for disease mapping. Statistical Methods in Medical Research, 14(1), 17–34. Emperical Bayes software (add-in for ArcView 3x) is available from: = 13900.CrossRefGoogle Scholar
  38. Lovejoy, J. C., Sainsbury, A., & Stock Conference 2008 Working Group. (2009). Sex differences in obesity and the regulation of energy homeostasis. Obesity Reviews, 10(2), 154–67.CrossRefGoogle Scholar
  39. Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: how can we conceptualise, operationalise and measure them? Social Science & Medicine, 55, 125–39.CrossRefGoogle Scholar
  40. Mohan, J., Twigg, L., Barnard, S., & Jones, K. (2005). Social capital, geography and health: a small-area analysis for England. Social Science & Medicine, 60, 1267–1283.CrossRefGoogle Scholar
  41. Moller, H., Mellemgaard, A., Lindvig, K., & Olsen, J. H. (1994). Obesity and cancer risk: a Danish record-linkage study. European Journal of Cancer, 30A(3), 344–350.CrossRefGoogle Scholar
  42. Monden, C. W. S., van Lenthe, F. J., & Mackenbach, J. P. (2006). A simultaneous analysis of neighbourhood and childhood socio-economic environment with self-assessed health and health-related behaviours. Health & Place, 12(4), 394–403.CrossRefGoogle Scholar
  43. Morrissey, K., Clarke, G. P., Ballas, D., Hynes, S., & O’Donoghue, C. (2008). Examining access to GP services in rural Ireland using microsimulation analysis. Area, 40(3), 354–364.CrossRefGoogle Scholar
  44. National Centre for Social Research and University College London. Department of Epidemiology and Public Health (2006), Health Survey for England, 2004 [computer file]. Colchester, Essex: UK Data Archive [distributor], SN: 5439Google Scholar
  45. National Centre for Social Research and University College London. Department of Epidemiology and Public Health (2007), Health Survey for England, 2005 [computer file]. Colchester, Essex: UK Data Archive [distributor], SN: 5675Google Scholar
  46. National Centre for Social Research and University College London. Department of Epidemiology and Public Health (2008), Health Survey for England, 2006 [computer file]. 2nd Edition. Colchester, Essex: UK Data Archive [distributor], SN: 5809Google Scholar
  47. Nelson, M. C., Gordon-Larsen, P., Song, Y., & Popkin, B. M. (2006). Built and Social Environments: Associations with Adolescent Overweight and Activity. American Journal of Preventive Medicine, 31(2), 109–17.CrossRefGoogle Scholar
  48. Okasha, M., McCarron, P., McEwen, J., Durnin, J., & Davey Smith, G. (2003). Childhood social class and adulthood obesity: findings from the Glasgow Alumni Cohort. Journal of Epidemiology and Community Health, 57, 508–9.CrossRefGoogle Scholar
  49. Openshaw, S. (1984). The Modifiable Areal Unit Problem, Concepts and Techniques in Modern Geography, 38, Norwich: Geo Books [copy freely available via RGS-IBG QMRG website]Google Scholar
  50. Parsons, T. J., Power, C., Logan, S., & Summerbell, C. D. (1999). Childhood predictors of adult obesity: a systematic review. International Journal of Obesity, 23(Suppl. 8), S1–107.Google Scholar
  51. Peto, J. (2001). Cancer epidemiology in the last century and the next decade. Nature, 411, 390.CrossRefGoogle Scholar
  52. Power, C., & Parsons, T. (2000). Nutritional and other influences in childhood as predictors of adult obesity. The Proceedings of the Nutrition Society, 59(2), 267–72.CrossRefGoogle Scholar
  53. Procter, K. L. (2007). Measuring the obesogenic environment of childhood obesity. PhD thesis. Available from the University of Leeds libraryGoogle Scholar
  54. Procter, K. L., Clarke, G. P., Ransley, J. K., & Cade, J. (2008). Micro-level analysis of childhood obesity, diet, physical activity, residential socio-economic and social capital variables: where are the obesogenic environments in Leeds? Area, 40(3), 323–340.CrossRefGoogle Scholar
  55. Rees, P., Martin, D. M., & Williamson, P. (2002). Census data resourses in the United Kingdom. In P. Rees, D. M. Martin, & P. Williamson (Eds.), The Census Data System, (chapter 1). Chichester: Wiley.Google Scholar
  56. Reidpath, D. D., Burns, C., Garrard, J., Mahoney, M., & Townsend, M. (2002). An ecological study of the relationship between social and environmental determinants of obesity. Health & Place, 8, 141–145.CrossRefGoogle Scholar
  57. Ross, C. E. (2000). Walking, exercising, and smoking: does neighbourhood matter? Social Science & Medicine, 51, 265–74.CrossRefGoogle Scholar
  58. Scarborough, P., Allender, S., Rayner, M., & Goldacre, M. (2009). Validation of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for wards in England. Health & Place, 15(2), 596–605.CrossRefGoogle Scholar
  59. Smith, D. M., Clarke, G. P., & Harland, K. (2009). Improving the synthetic data generation process in spatial microsimulation models. Environment and Planning A, 41, 1251–1268.CrossRefGoogle Scholar
  60. Stewart, B. W., & Kleihues, P. (Eds.). (2003). WHO: World Cancer Report. Lyon: IARC Press.Google Scholar
  61. Swinburn, B., Egger, G., & Raza, F. (1999). Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 29, 563–570.CrossRefGoogle Scholar
  62. Timperio, A., Salmon, J., Telford, A., & Crawford, D. (2005). Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. International Journal of Obesity, 29, 170–175.CrossRefGoogle Scholar
  63. Tomintz, M. N., Clarke, G. P., & Rigby, J. E. (2008). The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking services. Area, 40(3), 341–353.CrossRefGoogle Scholar
  64. Voas, D., & Williamson, P. (2001). Evaluating goodness-of-fit measures for synthetic microdata. Geographical and Environmental Modelling, 5(2), 77–200.CrossRefGoogle Scholar
  65. Whelan, A., Wrigley, N., Warm, D., & Cannings, E. (2002). Life in a ‘food desert’. Urban Studies, 39(11), 2083–100.CrossRefGoogle Scholar
  66. World Health Organisation (2003). Report of a joint WHO/FAO Expert Consultation on diet, nutrition and the prevention of chronic disease. WHO technical report series; 916. GenevaGoogle Scholar
  67. Wrigley, N. (2002). ‘Food Deserts’ in British Cities: Policy Context and Research Priorities. Urban Studies, 39(11), 2029–2040.CrossRefGoogle Scholar
  68. Zaninotto, P., Head, J., Stamatakis, E., Wardle, H., & Mindell, J. (2009). Trends in obesity among adults in England from 1993 to 2004 by age and social class and projections of prevalence to 2012. Journal of Epidemiology and Community Health, 63(2), 40–146.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Kimberley L. Edwards
    • 1
  • Graham P. Clarke
    • 2
  • James Thomas
    • 1
  • David Forman
    • 1
  1. 1.Centre of Epidemiology and BiostatisticsUniversity of LeedsLeedsUK
  2. 2.School of GeographyUniversity of LeedsLeedsUK

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