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Developments with ONS’s Small Area Population Estimates Project
Andy Bates, Office for National Statistics
Outline of Talk
• Purpose of the Project
• Geography considerations
• Methodology
• Data sources
• Project developments
• Publication Plans
Purpose of the Project
“To investigate the feasibility of producing an authoritative set of small area population estimates that would be available on a nationally consistent basis”
– what geography?– which method and data sources?– what age/sex breakdown?– how accurate?
– constrained to ONS LA mid-year estimates
What Geography?
What is meant by a ‘small area’?– Census Output Areas – Wards (CAS/statistical/electoral) – Parishes/Communities
ONS development of Super Output Areas– Lower Layers (LSOAs) – Middle Layers (MSOAs)
175,4348,800
11,265 +
– Upper Layers
34,3787,193
39 CAS Wards - average population 25,400
CAS Wards
131 MSOAs - average population 7,600
MSOAs
Mean Crude Birth Rate Birmingham 2002-2004Live births per 1,000 resident population
15.1 and over 12.1 to 15.0 9.9 to 12.0 7.9 to 9.8 7.8 and under
Data ranges are based on quintiles of national LSOA mean crude birth rates
641 LSOAs – average population 1,500
LSOAs
© Crown Copyright (ONS GD272183 2007)
Methodology• Early stages of project identified potential methods
• Shortlist of 3 methods– Apportionment– Cohort Component– Ratio Change
• Initially produced ward test estimates for mid-2002– Ratio Change identified as preferred method
• Subsequent evaluation in 2006 using MSOA test estimates for mid-2002 to mid-2004 (3 years)
– Ratio Change method remained the preferred method
• Further full evaluation all 3 methods intended when 2011 Census data become available
Ratio Change method data requirements
• Previous population base (estimate)
• Age & sex information– Child Benefit– Older Persons Dataset– Patient Registers
• Special Population– Prisoners– UK Armed Forces– Foreign Armed Forces & dependants
• Data needs to be consistent over time!
Ratio Change method (1)
Coverage Dataset 0-14 15-64 65+ Child Benefit Older Persons Dataset Patient Registers
Eg to derive MSOA ratios for 0-4 year olds by sex, we create dataset specific ratios:
CB 0-4 male ratio = Year 2 dataset count (117) = ratio 1.0833
Year 1 dataset count (108)
0-4 male ratio = Child Benefit ratio + Patient Register ratio
2
Base population
Year 1SP
x age & sex
ratiosSP
Year 2
Population
2nd period
Ratio Change method (2)
Constrain to LA MYEs less SP
Checking of derived change Ratios
Key Data Sources
Child Benefit– provided by HM Revenue & Customs– covers children 0-16 for whom benefit is claimed– data received at LSOA level, quinary age & sex
Older Persons Dataset– provided by Department for Work and Pensions (DWP)– covers persons 65+ from number of benefit databases eg State Pension, Widows Benefit & Winter Fuel Allowance– data received at LSOA level, quinary age & sex
Patient Register– provided by NHS– covers all persons registered with GP– data received at postcode level, single year of age & sex
Dataset coverage 2005
MYE less dataset % difference
5.2
-1.3-1.5
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Child Benefit (0-14)
Older PersonsDataset (65+)
Patient Registers (All Ages)
% D
iffe
ren
ce
2005 Child Benefit & Patient Register counts
r = 0.9836
MSOAs 0-14 year-olds
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000
Child Benefit
Pat
ient
Reg
iste
r
r = 0.9836
2005 OPD & Patient Register counts
r = 0.9836
MSOAs 65+ year-olds
r = 0.9960
0
1,000
2,000
3,000
4,000
5,000
0 1,000 2,000 3,000 4,000 5,000
Older Persons Dataset
Pat
ient
Reg
iste
r
r = 0.9960
2005 Patient Registers v MYEs - Males
0.0
0.5
1.0
1.5
2.0
2.5
0-4
5-9
10-1
4
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65-6
9
70-7
4
75-7
9
80-8
485
+
Cou
nt (
milli
ons)
Patient Registers MYEs
2005 Patient Registers v MYEs – Females
0.0
0.5
1.0
1.5
2.0
2.5
0-4
5-9
10-1
4
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65-6
9
70-7
4
75-7
9
80-8
485
+
Cou
nt (
milli
ons)
Patient Registers MYEs
Patient Registers – 2005 List Inflation
-3
0
3
6
9
12
15
18
0-4
5-9
10-1
4
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65-6
9
70-7
4
75-7
9
80-8
485
+
Per
cent
age
diffe
renc
e (P
R-M
YE
)
Males Females
PR List Inflation
Males
Females
Persons
7.2%
3.3%
5.2%
Limitations with the Estimates (1)
• Change in counts from administrative data sources may not reflect actual population change
• Some areas have difficult to estimate populations
– Students & migrants
• Cumulative errors over time may occur
• During an intercensal period difficult to assess the accuracy of the estimates
– no gold standard to compare against
• Greater uncertainty of accuracy at finer levels – eg at LSOA level by quinary age & sex
Limitations with the Estimates (2)
However
– most LSOAs/MSOAs have a stable population over time
– average annual LSOA population change just +7 persons
– no identified shortcomings with Ratio Change method
– data sources are of good quality
– good feedback from users over the quality of the estimates
– belief therefore that for the majority of areas at least, the estimates should be of good quality
– feedback is very welcome!
Project Developments
• Investigating data visualisation tools– Scalable Vector Graphics (SVG)
Project Developments
• Investigating data visualisation tools– scaleable vector graphics (svg)
• Extend geographical coverage of population estimates
– Postcode Best Fit methodology (PBF)
Postcode Best Fit Development
• Consultation with users indicated demand for Ward population estimates on ‘current’ boundaries
• Demand for estimates for other geographies which cannot always be derived from LSOA estimates
– Parliamentary Constituencies
– National Parks
• Limited geographical availability of some data sources used in Ratio Change methodology
– Child Benefit & Older Persons Dataset
Lower Layer SOA Estimates
Ward Estimates
LSOA estimates less the special population apportioned to Patient Register postcode counts
Patient Register postcode counts aggregated to Wards
‘current’ Wards overlaid onto Patient Register postcodes
Add back inspecial population
Removespecial population
Postcode Best Fit methodology
Postcode Best Fit – River Catchment Areas
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© Crown Copyright (ONS GD272183 2007)
Project Developments
• Investigating data visualisation tools– scaleable vector graphics (svg)
• Extend geographical coverage of population estimates
– Postcode Best Fit methodology (PBF)
• Small area population estimates to become a standard and established ONS output
Project Developments
• Investigating data visualisation tools– scaleable vector graphics (svg)
• Extend geographical coverage of population estimates
– Postcode Best Fit methodology (PBF)
• Small area population estimates to become a standard and established ONSCD output
• Consideration for the Experimental Statistics to become National Statistics in 2008
Planned Publications
October 2007• mid-2005 LSOA & MSOA estimates• revisions to mid-2001 to mid-2004 LSOA & MSOA estimates
November 2007• mid-2003 to mid-2005 CAS Ward estimates?• revisions to mid-2001 to mid-2002 CAS Ward estimates?• mid-2001 to mid-2005 Statistical/current ward estimates• 2004/05 MSOA Population Turnover Rates• revisions to MSOA Population Turnover Rates 2001/02 to 2003/04
December 2007• mid-2001 to mid-2005 Parliamentary Constituency estimates• Mid-2001 to mid-2005 National Park estimates
Contact Information
Email the project team at:
Find estimates at:
www.statistics.gov.uk/popest
Updated information on the NS website at:
www.statistics.gov.uk/SAPE
Any Questions?
