Applied Spatial Analysis and Policy

, Volume 12, Issue 2, pp 321–348 | Cite as

Measuring Spatial Accessibility to Services within Indices of Multiple Deprivation: Implications of Applying an Enhanced two-Step Floating Catchment Area (E2SFCA) Approach

  • Nicholas PageEmail author
  • Mitchel Langford
  • Gary Higgs


Approaches to calculating spatial accessibility within existing indices of multiple deprivation (IMD) methodologies are based on ‘traditional’ accessibility metrics and tend not to adopt more recent methodological enhancements. In particular, the last decade has seen a relatively large body of studies that have applied floating catchment area (FCA) methods that account for both service supply and potential demand interactions, mediated by the impact of distance, in a wide range of application areas. In this paper, we investigate potential implications of incorporating an FCA-based approach to measuring spatial accessibility within an existing IMD framework. Using the Welsh Index of Multiple Deprivation (WIMD) as a case study, FCA-derived accessibility scores were substituted for the existing approach used to calculate accessibility and a revised index was computed. The published methodologies used to construct the other ‘domains’ within the WIMD were followed and the implications for the overall deprivation measure were assessed. Statistical and visualisation tools revealed implications for both the access and overall IMD rankings, with sparsely populated (predominantly rural) areas tending to receive higher accessibility scores from FCA-based approaches than more densely populated (predominantly urban) areas. These areas in turn showed the greatest decline in ranking on the WIMD calculations following the application of FCA approaches. Potential reasons for such trends are posited before we conclude by drawing attention to the implications of adopting FCA-based approaches to calculate IMDs particularly for those policies designed to distribute funds or allocate resources to areas of need.


Accessibility Indices of multiple deprivation Two-step floating catchment area Reproducible research 



This paper is based on research supported by the Wales Institute of Social and Economic Research, Data and Methods (WISERD). Funded by the Economic and Social Research Council (ESRC), WISERD is a collaborative venture between the Universities of Aberystwyth, Bangor, Cardiff, South Wales and Swansea (Grant number: ES/L009099/1).

Compliance with Ethical Standards

Conflict of Interest

None declared.

Supplementary material

12061_2017_9246_Fig6_ESM.png (87 kb)
Fig. S1 Change in LSOA ranking for the ‘access to services’ domain of WIMD-2014 based on ES2FCAderived accessibility scores (5 min catchment zones) (86.9 kb)
12061_2017_9246_Fig7_ESM.png (85 kb)
Fig. S3 Change in LSOA ranking for overall WIMD-2014 based on ES2FCA-derived accessibility scores (5 min catchment zones) (85.4 kb)


  1. Beynon, C. (2016) Risk factors associated with childhood obesity in Wales: a secondary analysis of cross-sectional data from the Welsh Health Survey. The Lancet. 388:S24.Google Scholar
  2. Brunsdon, C., & Singleton, A. (2015). Reproducible research: concepts, techniques and issues. In C. Brunsdon & A. Singleton (Eds.), Geocomputation: A practical primer (pp. 254–264). London: Sage.CrossRefGoogle Scholar
  3. Caranci, N., Biggeri, A., Grisotto, L., Pacelli, B., Spadea, T., & Costa, G. (2010). The Italian deprivation index at census block level: definition, description and association with general mortality. Epidemiologia e Prevenzione, 34, 167–176.Google Scholar
  4. Carstairs, V., & Morris, R. (1989). Deprivation: explaining differences in mortality between Scotland and England and Wales. BMJ, 299, 886.CrossRefGoogle Scholar
  5. Corder, G. W., & Foreman, D. I. (2014). Nonparametric statistics: A step-by-step approach (2nd ed.). Oxford, UK: Wiley.Google Scholar
  6. Cummins, S., McKay, L., and Macintyre, S. (2005). McDonald's restuarants and neighbourhood deprivation in Scotland and England. American Journal of Preventative Medicine. 29(4):308–310.Google Scholar
  7. Deas, I., Robson, B., Wong, C., & Bradford, M. (2003). Measuring neighbourhood deprivation: a critique of the index of multiple deprivation. Environment and Planning. C, Government & Policy, 21(6), 883–903.CrossRefGoogle Scholar
  8. Department for Communities and Local Government [DCLG]. (2015a). The English Indices of Deprivation 2015. Accessed 14 May 2017.
  9. Department for Communities and Local Government [DCLG]. (2015b). English Indices of Deprivation 2015 Technical Report. Accessed 14 May 2017.
  10. Department for Environment Food and Rural Affairs. (2016). Guide to applying the Rural Urban Classification to data. Office for National Statistics. Accessed 27 May 2017.
  11. Department of the Environment, Transport and the Regions [DTER]. (1998). 1998 Index of Local Deprivation - A Summary of Results. Accessed 3 May 2017.
  12. Department of the Environment, Transport and the Regions [DTER]. (2000). Indices of deprivation 2000. Accessed 3 May 2017.
  13. Environmental Systems Research Institute [ESRI]. (2015). ArcGIS desktop: Release 104. Redlands, CA: Environmental Systems Research Institute.Google Scholar
  14. Exeter, D., Zhao, J., Crengle, S., Lee, A., & Browne, M. (2017). The New Zealand indices of multiple deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New Zealand. PLoS One, 12(8), e0181260.CrossRefGoogle Scholar
  15. Fairburn, J., Maier, W., & Braubach, M. (2016). Incorporating environmental justice into second generation indices of multiple deprivation: lessons from the UK and progress internationally. International Journal of Environmental Research and Public Health, 13, 750–763.CrossRefGoogle Scholar
  16. Ferguson, N., & Michaelson, M. (2013). The legacy of conflict - regional deprivation and school performance in Northern Ireland. Ruhr Economic Paper No.419.
  17. Fransen, K., Neutens, T., De Maeyer, P., & Deruyter, G. (2015). A computer-based two-step floating catchment area method for measuring spatial accessibility of day care centres. Health & Place, 32(1), 65–73.CrossRefGoogle Scholar
  18. Higgs, G. (2004). A literature review of the use of GIS-based measures of access to health care services. Health Services & Outcomes Research Methodology, 5(2), 119–139.CrossRefGoogle Scholar
  19. Higgs, G., Langford, M., & Norman, P. (2015). Accessibility to sport facilities in Wales: A GIS-based analysis of socio-economic variations in provision. Geoforum, 62, 105–120.CrossRefGoogle Scholar
  20. Hofmeister, C., Maier, W., Mielck, A., Stahl, L., Breckenkamp, J., & Razum, O. (2016). Regional deprivation in Germany: Nation-wide analysis of its association with mortality using the German index of multiple deprivation (GIMD). Gesundheitswesen, 78(1), 42–48.Google Scholar
  21. Jarman, B. (1983). Identification of underprivileged areas. British Medical Journal (Clinical Research Edition), 286(6379), 1705–1709.CrossRefGoogle Scholar
  22. Jones, A., Hillsdon, M., & Coombes, E. (2009). Greenspace access, use, and physical activity: Understanding the effects of area deprivation. Preventative Medicine, 49(6), 500–505.CrossRefGoogle Scholar
  23. Langford, M., Fry, R., & Higgs, R. (2012). Measuring transit system accessibility using a modified two-step floating catchment area technique. International Journal of Geographical Information Science, 26(2), 193–214.CrossRefGoogle Scholar
  24. Langford, M., Higgs G., & Fry, R. (2015). “USW-FCA2: An ArcGIS add-In tool to compute Enhanced Two-Step Floating Catchment Area accessibility scores” Software Package–ArcMap add-in Available at:
  25. Langford, M., Higgs, G., and Fry, R. (2016). Multi-modal two-step floating catchment area analysis of primary health care accessibility. Health & Place. 38:70–81.Google Scholar
  26. Luo, W. (2004). Using a GIS-based floating catchment area method to assess areas with shortage of physicians. Health & Place, 10, 1–11.CrossRefGoogle Scholar
  27. Luo, W., & Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15, 1100–1107.CrossRefGoogle Scholar
  28. Luo, W., & Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environment and Planning B, 30(6), 865–884.CrossRefGoogle Scholar
  29. MacDonald, L., Cummins, S., and Macintyre, S. (2007). Neighbourhood fast food environment and area deprivation - substitution or concentration? Appetite. 49(1):251–254.Google Scholar
  30. Macintyre, S., Macdonald, L., & Ellaway, A. (2008). Do poorer people have access to local resources and facilities? The distribution of local resources by area deprivation in Glasgow, Scotland. Social Science and Medicine, 67, 900–914.CrossRefGoogle Scholar
  31. Maier, W., Fairburn, J., & Mielck, A. (2012). Regional deprivation and mortality in Bavaria. Development of a community-based index of multiple deprivation. Gesundheitswesen, 74(7), 416–425.CrossRefGoogle Scholar
  32. Mao, L., & Nekorchuk, D. (2013). Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health & Place, 24, 115–122.CrossRefGoogle Scholar
  33. McGrail, M. R., & Humphreys, J. S. (2009). Measuring spatial accessibility to primary care in rural areas: Improving the effectiveness of the two-step floating catchment area method. Applied Geography, 29, 533–541.CrossRefGoogle Scholar
  34. Neutens, T. (2015). Accessibility, equity and health care: Review and research directions for transport geographers. Journal of Transport Geography, 43(1), 14–27.CrossRefGoogle Scholar
  35. Ngui, A. N., & Apparicio, P. (2011). Optimizing the two-step floating catchment area method for measuring spatial accessibility to medical clinics in Montreal. BMC Health Services Research, 11(1), 166–177.CrossRefGoogle Scholar
  36. NHS England. (2016) Technical Guide to Allocation Formulae and Pace of Change - For 2016/17 to 2020/21 revenue allocations to Clinical Commissioning Groups and commissioning areas. Accessed 16 June 2017.
  37. Noble, M., Smith, G.A.N., Wright, G., Dibben, C., Lloyd, M., Penhale, B. (2000). Index of Multiple Deprivation for Wales – Final Report. Accessed 3 May 2017.
  38. Noble, M., Wright, G., Smith, G., & Dibben, C. (2006). Measuring multiple deprivation at the small-area level. Environment and Planning A, 38, 169–185.CrossRefGoogle Scholar
  39. Noble, M., Barnes, H., Wright, G., & Roberts, B. (2010). Small area indices of multiple deprivation in South Africa. Social Indicators Research, 95, 281–297.CrossRefGoogle Scholar
  40. Northern Ireland Statistics and Research Agency [NISRA]. (2010). Northern Ireland Multiple Deprivation Measure 2010. Accessed 12 May 2017.
  41. Northern Ireland Statistics and Research Agency [NISRA]. (2016). Consultation Document – Proposals for the updated NI Multiple Deprivation Measure (NIMDM 2017). Accessed 12 May 2017.
  42. O’Flaherty, M., Bishop, J., Redpath, A., McLaughlin, T., Murphy, D., Chalmers, J., et al. (2009). Coronary heart disease mortality among young adults in Scotland in relation to social inequalities: Time trend study. BMJ, b2613, 339.Google Scholar
  43. Ordnance Survey. (2015). OS MasterMap Integrated Transport Network Layer. Accessed 21 May 2015.
  44. Pickrell, W., Lacey, A., Bodger, O., Demmler, J., Thomas, R., Lyons, R., Smith, P., Rees, M., and Kerr, M. (2015) Epilepsy and deprivation, a data linkage study. Epilepsia. 56:585–591.Google Scholar
  45. R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing Scholar
  46. Sanchez-Cantalejo, C., Ocana-Riola, R., & Fernández-Ajuria, A. (2008). Deprivation index for small areas in Spain. Social Indicators Research, 89(2), 259–273.CrossRefGoogle Scholar
  47. Scott-Pillai, R., Spence, D., Cardwell, C., Hunter, A., and Holmes, V. (2013). The impact of body mass index on maternal and neonatal outcomes: a retrospective study in a UK obstetric population, 2004-2011. BJOG. 120(8):932–939.Google Scholar
  48. Scottish Government. (2016a). Introducing The Scottish Index of Multiple Deprivation 2016. Accessed 15 May 2017.
  49. Scottish Government. (2016b). The Scottish Index of Multiple Deprivation: SIMD16 Technical Notes. Accessed 15 May 2017.
  50. Stephens, M., Evans, M., Ilham, M., Marsden, A., and Asderakis, A. (2010) The influence of socioeconomic deprivation on outcomes following renal transplantation in the United Kingdom. American Journal of Transplantation. 10(7):1605–1612.Google Scholar
  51. Townsend, P. (1987). Deprivation. Journal of Social Policy, 16(2), 125–146.CrossRefGoogle Scholar
  52. Tseliou, F., Maguire, A., and Donnelly, M. (2016). The influence of mobility on mental health status in young people: the role of area-level deprivation. Health & Place. 42:96–103.Google Scholar
  53. Wang, F. (2012). Measurement, optimization and impact of healthcare accessibility: A methodological review. Annals of the Association of American Geographers, 102(5), 1104–1112.CrossRefGoogle Scholar
  54. Welsh Government. (2005). Welsh Index of Multiple Deprivation 2005 – Technical Report. Accessed 12 May 2017.
  55. Welsh Government. (2013a). Communities first - Programme Guidance 2013. Accessed 5 June 2017.
  56. Welsh Government. (2013b). Consultation Document – Proposed indicators for the Welsh Index of Multiple Deprivation 2014. Accessed 1 June 2017.
  57. Welsh Government. (2014a). Welsh Index of Multiple Deprivation (WIMD) 2014 Revised. Accessed 2 May 2017.
  58. Welsh Government. (2014b). Welsh Index of Multiple Deprivation 2014 (WIMD 2014) Technical Report. Accessed 2 May 2017.
  59. Welsh Government (2015). Welsh Index of Multiple Deprivation 2014: A guide to analysing deprivation in rural areas – Revised. Accessed 1 June 2017.
  60. Whynes, D. K., Frew, E. J., Manghan, C. M., Scholefield, J. H., & Hardcastle, J. D. (2003). Colorectal cancer, screening and survival: the influence of socio-economic deprivation. Public Health, 117(6), 389–395.CrossRefGoogle Scholar
  61. Whyte, L. A., Kotecha, S., Watkins, W. J., & Jenkins, H. R. (2014). Coeliac disease is more common in children with high socio-economic status. Acta Paediatrica, 103(3), 289–294.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Wales Institute of Social and Economic Research, Data and Methods (WISERD) and GIS Research Centre, Faculty of Computing, Engineering and ScienceUniversity of South WalesPontypriddUK

Personalised recommendations