Transcript Slide 1

Using GIS to determine if the built
environment’s walkability helps
determine health
Sarah Rodgers, Ph.D.
14th October, 2008: Park Place, Cardiff
[email protected]
Outline
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Background: the Obesity Problem
Method: walking and built environment
HIRU and OSMM data (GIS)
RALFs (more GIS)
Defining the Problem:
Obesity and Mobility
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.
Welsh Index of Multiple Deprivation
Percentage of overweight children
Primary school entry children
Child Health Database (SAIL)
< 10%
10.01% - 20%
20.01% - 30%
> 30.01%
Swansea, Neath
and Port Talbot
0
2.5
5
10
Kilometers
Percentage overweight children
by Deprivation 5ths, LSOA, WIMD2005, n=13,416
Swansea, Neath
and Port Talbot
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%)
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.
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.
Built Environment and Walking
• Against walking:
– Lots of traffic
– Steep gradient
– Circuitous route
– Nowhere to
interesting to walk
– High crime area
– Large car parks
• 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
GIS Walkability Literature
• Several authors who have used GIS for “walkability”
calculations:
Adapting
– Moudon and Lee (Texas)
US/Australian
– Frank (British Columbia)
methods for UK’s
OS Master Map data
– 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.
Density & Connectivity
Dwelling Density
• Count dwellings per hectare
deciles:1-10
Low level of
connectivity
Connectivity
• Count junction density
using road nodes
deciles:1-10
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
Topography
Address Layer 2 (AL2)
Integrated
Transportation
Network
Dwelling Density
•
Count ‘dwellings’ per LSOA
(Address layer, AL2)
S
A
SS5494
Area SS5595
0
100
200
400
Meters
Dwelling Density
•
Count ‘dwellings’ per LSOA
(Address layer, AL2)
Develop residential land
extraction method for
buildings and surrounding
Reside
land (Area and AL2):
•
Area S
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
Residential SS5595
Area SS5595
0
100
200
400
Meters
Urban Swansea Neath and Port Talbot
>200 people per sq. km
Residences per
Square km
Dwelling density deciles: 1-10
Frequent road junctions encourage walking:
Junctions per
square km
Connectivity deciles: 1-10
Hypothesis:
• Some neighbourhoods are more
‘walkable’: A more walkable local
environment will reduce prevalence of
obesity-related chronic diseases.
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
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
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 deprived areas
where the environment supports walking
• Highly walkable, m = 17,928
• Less walkable, m = 22,135
• p = 0.011
Residential Anonymous Linking Fields
(RALFs)
Margaret Williams
age 72
8 Main St, Swansea
Identifiable
David Williams
age 70
8 Main St, Swansea
Chronic Heart Disease
Hip replacement
Meals on wheels
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):
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
Processing Problems – Data Overlap
.
dwelling
LSOA
= 700 dwellings
.
.
.
dwelling
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
Acknowledgements
Ronan Lyons – Professor of Public Health
Caroline Brooks & Steven Macey – Health
Analysts
David Ford & HIRU team (Rohan Dsilva)
Geographic Information System (GIS) used
to create derived environment metrics for
each house in region at HIRU
HIRU
HIRU provide AL2 address key and
GIS metrics to HSW
HSW
a. Create
environment
metrics
b. KEY and addresses with environment metrics
OS Data
HIRU GIS
c. Match incoming
address data
and attach RALFs
encrypt
SAIL
d. RALFs and environment
metrics
NHSAR
Filte
r
encrypt
e. Combination of
RALFs with ALFs
Anonymous data now ready for analyses
Return anonymous environment
data and RALFs to HIRU