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Using GIS to determine if the built environment’s walkability helps determine health

Using GIS to determine if the built environment’s walkability helps determine health. Sarah Rodgers, Ph.D. 14 th October, 2008: Park Place, Cardiff s.e.rodgers@swansea.ac.uk. Outline. Background: the Obesity Problem Method: walking and built environment HIRU and OSMM data (GIS)

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Using GIS to determine if the built environment’s walkability helps determine health

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  1. Using GIS to determine if the built environment’s walkability helps determine health Sarah Rodgers, Ph.D.14th October, 2008: Park Place, Cardiffs.e.rodgers@swansea.ac.uk

  2. Outline • Background: the Obesity Problem • Method: walking and built environment • HIRU and OSMM data (GIS) • RALFs (more GIS)

  3. Defining the Problem: Obesity and Mobility

  4. Obesity Statistics • UK Children – 28% overweight • Giles-Corti, BMJ, 6th October 2007 • Overweight and obesity increasing • The percentage of obese adults has roughly doubled since the mid-1980's.

  5. Welsh Index of Multiple Deprivation

  6. Percentage of overweight childrenPrimary school entry children Child Health Database (SAIL) Swansea, Neath and Port Talbot

  7. by Deprivation 5ths, LSOA, WIMD2005, n=13,416 Percentage overweight children Swansea, Neath and Port Talbot

  8. National Statistics Office • In the last twelve months (2002) the 5 most popular sports, games or physical activities among adults were: • walking (46%); • swimming (35%) • keep fit/yoga – including aerobics and dance exercise (22%) • cycling (19%); and • cue sports - billiards, snooker and pool (17%)

  9. Built Environment and Walking Health experts are broadening the definition of physical activity from leisure-time activity to active living: “a lifestyle or way of life that integrates physical activity into daily routines with the goal of accumulating at least 30 minutes of activity each day.” Orleans C, Kraft M, Marx J, McGinnis J. Why are some neighborhoods active and others not? Ann Behav Med 2003;25:77–9.

  10. Can the physical environment determine physical activity levels? • Self-selection • active people choose to live in walkable environments. • Environmental determinism • walkable environments encourage individuals to be active. Ewing, R. 2005. Can the physical environment determine physical activity levels? Exercise and Sport Sciences Reviews 33: 69-75.

  11. Built Environment and Walking • For walking: • Intermittent traffic • Not too steep • Grid streets for direct route • Numerous interesting destinations within walking distance (e.g. 1 km) • Safe and pleasant walkways • Against walking: • Lots of traffic • Steep gradient • Circuitous route • Nowhere to interesting to walk • High crime area • Large car parks

  12. GIS Walkability Literature • Several authors who have used GIS for “walkability” calculations: • Moudon and Lee (Texas) • Frank (British Columbia) • Leslie (South Australia) • Giles-Corti (Western Australia) Leslie, E., N. Coffee, L. Frank, N. Owen, A. Bauman, and G. Hugo. 2007. Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attributes. Health & Place 13: 111-122. Adapting US/Australian methods for UK’s OS Master Map data

  13. Density & Connectivity Dwelling Density • Count dwellings per hectare Connectivity • Count junction density using road nodes deciles:1-10 Low level of connectivity deciles:1-10

  14. OSMM Data on Enterprise GIS Only now is it possible to analyse UK areas for the purpose of “walkability” using GIS with detailed Ordnance Survey data Integrated Transportation Network Topography Address Layer 2 (AL2)

  15. Dwelling Density • Count ‘dwellings’ per LSOA (Address layer, AL2)

  16. Dwelling Density • Count ‘dwellings’ per LSOA (Address layer, AL2) • Develop residential land extraction method for buildings and surrounding land (Area and AL2): Area.DescGroup = 'Building' AND Area.OS_CLASS = 'DWELLING' OR (Area.DescGroup = 'General Surface' AND Area.make <> 'Natural’) AND AL2.OS_CLASS = ‘dwelling’ • Dwellings/residential land • Dwelling density per hectare

  17. Urban Swansea Neath and Port Talbot>200 people per sq. km Residences per Square km Dwelling density deciles: 1-10

  18. Frequent road junctions encourage walking: Junctions per square km Connectivity deciles: 1-10

  19. Hypothesis: • Some neighbourhoods are more ‘walkable’: A more walkable local environment will reduce prevalence of obesity-related chronic diseases.

  20. HIRU database: GP data • Coronary Heart Disease • Chronic Kidney Disease • COPD • Diabetes (Type 2) • Heart Failure • Stroke • Atrial Fibrillation Data coding and extraction by Caroline Brooks

  21. Statistical Test: Walkability deciles summed: 2-20 • Choose 25% highly walkable and 25% less-walkable areas for comparison • Compare numbers of people with ≥1 obesity-related chronic diseases in each group

  22. Results: • No significant difference between affluent walkable and less walkable areas • Highly walkable, m = 14,490 • Less walkable, m = 14,347 • p = 0.470 • Significant decrease in adult obesity-related diseases (CHD, diabetes) in deprivedareas where the environment supports walking • Highly walkable, m = 17,928 • Less walkable, m = 22,135 • p = 0.011

  23. Identifiable Margaret Williams age 72 8 Main St, Swansea Chronic Heart Disease Hip replacement Meals on wheels David Williams age 70 8 Main St, Swansea COPD Diabetes Meals on wheels Anonymous Same RALF:cohabiting Same environmental metric ALF RALF Medical 1 Medical 2 Social 1 Environment1 11223387 5448893 CHD Hip replacement Meals on wheels 5.852 11238889 5448893 COPD Diabetes Meals on wheels 5.852 Residential Anonymous Linking Fields (RALFs)

  24. RESIDENTIAL ANONYMOUS LINKING FIELDS (RALFs): A NOVEL INFORMATION INFRASTRUCTURE TO STUDY THE INTERACTION BETWEEN THE ENVIRONMENT AND INDIVIDUALS’ HEALTH Submitted Sarah E Rodgers, Ronan A Lyons, Rohan Dsilva, Kerina H Jones, Caroline J Brooks, David V Ford, Gareth John, Phil Verplancke Keywords: Confidentiality, Geographic Information Systems, Environment, Longitudinal Studies, Medical Record Linkage

  25. Processing Problems – Data Overlap . . . . dwelling dwelling LSOA = 700 dwellings

  26. Processing: Supercomputer merge into DB2INST3.DWELL_RALF_2 tgt using (select distinct TOID ,THEME ,CALCULATEDAREAVALUE ,DESCRIPTIVEGROUP ,DESCRIPTIVETERM ,MAKE from DB2INST3.TOPOGRAPHICAREA_RALF ) as src on tgt.OS_RT_TOID= src.TOID when matched then update set tgt.TOPO_THEME = src.THEME , tgt.TOPO_CAL_AREAVALUE = src.CALCULATEDAREAVALUE , Data coding and extraction by Rohan Dsilva

  27. Acknowledgements Ronan Lyons – Professor of Public Health Caroline Brooks & Steven Macey – Health Analysts David Ford & HIRU team (Rohan Dsilva)

  28. Geographic Information System (GIS) used to create derived environment metrics for each house in region at HIRU HIRU provide AL2 address key and GIS metrics to HSW HIRU HSW • a. Create environment • metrics OS Data b. KEY and addresses with environment metrics HIRU GIS NHSAR c. Match incoming address data and attach RALFs Filter d. RALFs and environment metrics encrypt encrypt e. Combination of RALFs with ALFs SAIL Return anonymous environment data and RALFs to HIRU Anonymous data now ready for analyses

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