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Using Interaction Data in the (Public Sector) GLAJohn Hollis
‘Exploring the Research Potential of the 2011 Census’University of Manchester 7th July 2011
Overview
Experience of using 2001 Census interaction data at GLA
Migration
Journey to Work
Ethnicity of Mother, Father and Infant
2011?
Migration in Population Modelling
GLA projection models for:– Boroughs– Ethnic groups in boroughs– Wards
(0) 1-90+ SYA Gender
10 Ethnic groups – based on 1991 Census – No ‘Mixed’ groups – add into ‘Other Asian’, etc– No split of ‘White’ group
Borough Model - 1
32 Boroughs plus City of London– Inter-borough matrix– 3 Borough Groups - Central (3 + City) , Rest of Inner (10), Outer
(19)
4 External origins and destinations– South East region– East region– Rest of UK– Overseas
‘No Usual Address a year ago’ - 60k out of 271k
Borough Model - 2
To: Central Rest Inner Outer East South East Rest UK OverseasFrom:Central Probability Probability Probability Probability Probability Probability Not Census
Rest Inner Probability Probability Probability Probability Probability Probability Not Census
Outer Probability Probability Probability Probability Probability Probability Not Census
East Probability Probability Probability NA NA NA NA
South East Probability Probability Probability NA NA NA NA
Rest UK Probability Probability Probability NA NA NA NA
Overseas Structure Structure Structure NA NA NA NA
Borough Model - 3
Population at Risk of Migrating
Migration is continuous over year In models all migration ‘takes place’ at end of projection year – after
survival– Census also relies on survival to end of year
Enumerated population contains inflow over last year and not the outflow
Population at risk – add total outflow and subtract total inflow– Which inflow and which outflow?
P’(a, g, A) = P(a, g, A) + M(a, g, A, D) – M(a, g, O, A)– a – age, g – gender, A –area of interest, D – destination area, O – origin
area– Sum origins and destinations over all in UK
Borough Model - 4
Probability of Migrating (From Area A to Destination D)
Ages 1-90+
Pr(a, g, A, D) = M(a, g, A, D)/{P(a, g, A) + M(a, g, A, D) – M(a, g, O, A)}
Smooth schedule using Tom Wilson methodWilson, T. (2010). “Model migration schedules incorporating student migration peaks”, Demographic Research, 23, 8: 191-222.
Age 0– ‘Back project’ ages 5 to 1 to estimate age 0 – then divide by 2
Borough Model - 5
0.000
0.005
0.010
0.015
0.020
0.025
0 10 20 30 40 50 60 70 80 90
Scled migration probability
Age
Scaled migration probability
Student peak model schedule
Borough Model - 6
Base out-migration probabilities: Central to RoI
0
0.05
0.1
0.15
0.2
0 20 40 60 80
Age
Pro
ba
bili
ty
Male
Female
Borough Model - 7
Migration probabilities: GOER to Camden
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0 10 20 30 40 50 60 70 80 90
Age
Mig
ratio
n p
rob
ab
ility
Ethnic Model
Origins/Destinations– All UK – outside specific Borough– Overseas - inflows only
Data 5-year age groups and gender– 5YA spread over SYA
Outflows – probabilities applied after ageing-on Inflows – structures Flows constrained to output from Borough model for total population
(SYA)
Problems:– SCAMing– Was 2000-01 a typical year?– Small flows for some ethnic groups – especially to Outer boroughs
• Use Borough Group data
Ward Model
Origins/Destination– Inflow – rest of UK (outside ward) and Overseas– Outflow – rest of UK
Data mostly 5-year age groups and gender – some smaller age groups for children
Outflows – SYA probabilities applied after ageing-on Inflows – SYA structures – used to ‘top up’ population:
– linked to capacity of all available homes Population results constrained to Borough totals
Problems:– SCAMing– Is a single year OK for long-term use when development levels
change?
Journey to Work
A great disappointment of the 2001 Census …
… but SASPAC could handle it
Journey to Work
For small areas – ‘sets of random numbers’
SCAMing at sub-borough level– Add in factors ie gender, occupation, mode => little consistency between tables– Needed to have larger areas to get broad consistency
OK for basic inter-borough commuting patterns …– … once scaled to 2001 MYE populations
TfL used data for London Transport Studies Model – 1000 zones
Large GLA study on journey to work and qualifications – seriously reduced in scope
Would have liked to use for small area profiling – but …
Ethnicity of Mother, Father & Infant
For use in Borough Ethnic group projection model
Commissioned Table C0200– Infants in private households with mother present– Ethnicity of infant– Age/ethnicity of mother– Ethnicity of father– Greater/Inner/Outer London
Covered 91% of infants in PH– Mother not present/identified on form– Infant on Continuation Form
Data on 76% of Fathers
Chart 9: Proportion of all births where Father not present/ethnicity unknown
56.1 55.7
44.1
23.821.3
19.6 18.5 17.913.9 13.4
11.6
0
10
20
30
40
50
60
Black Other BlackCaribbean
BlackAfrican
Total Bangladeshi White Pakistani Other Chinese Other Asian Indian
Ethnicity of Child
%
Father Present
Father Present by Mother's Age and Ethnicity
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
<20 20-24 25-29 30-34 35-39 40+
Age of Mother
%
White Indian Pakistani Bangladeshi Other Asian Black Carib
Black African Black Other Chinese Other
13% of Infants have different Ethnicity to Mother – using 10 groups (21% with 16 groups)
Mother and Child with different ethnicity
45.2
40.1
34.4
31.3 30.3
17.2 16.6 16.4
11.6
8.0
0
5
10
15
20
25
30
35
40
45
50
Black Other Other Chinese Other Asian BlackCaribbean
Pakistani Indian Black African Bangladeshi White
Ethnicity of Mother
%
Different Ethnicity by Mother’s Age
0
5
10
15
20
25
30
35
40
45
<20 20-24 25-29 30-34 35-39 40+
Age Group
White Black African Indian Black Carib Bangladeshi All
Ethnicity of Mother and Infant
Ethnicity of Child:
Ethnicity of Mother: Other Black Black Other
Births Same White Indian Pakistani Ban'deshi Asian Caribbean African Black Chinese Other
White 57,417 92.0 - 0.1 0.1 0.1 1.6 0.1 0.4 4.0 0.0 1.5
Indian 4,610 83.4 2.9 - 2.3 0.5 8.4 0.4 0.2 0.3 0.2 1.5
Pakistani 2,287 82.8 3.6 2.8 - 0.9 7.2 0.3 0.3 0.8 0.3 1.2
Bangladeshi 2,630 88.4 3.5 1.5 1.1 - 3.0 0.0 0.2 0.3 0.1 1.8
Other Asian 2,545 68.7 14.9 2.6 1.8 1.3 - 0.5 0.9 1.7 0.0 7.6
Black Caribbean 4,481 69.7 3.2 0.5 0.1 0.1 0.7 - 5.6 18.1 0.1 1.9
Black African 7,872 83.6 2.7 0.2 0.2 0.3 0.7 1.9 - 9.8 0.0 0.6
Other Black 2,056 54.8 13.9 0.4 0.1 0.3 1.6 8.8 8.1 - 0.0 12.0
Chinese 941 65.6 6.0 0.0 0.3 0.3 6.3 0.6 0.0 0.6 - 20.3
Other 2,567 59.9 19.2 0.8 0.6 0.1 11.9 1.2 1.3 3.2 1.8 -
Black Caribbean Mothers 30-34:Known and Potential Fathers
0
10
20
30
40
50
60
70
80
White Indian Pakistani Bangladeshi Other Asian BlackCaribbean
BlackAfrican
Black Other Chinese Other
Ethnic Group of Father
%
Known Potential
Black Caribbean Mothers 30-34:Potential Fathers – ‘Relative Risk’
0.27 0.080.46 0.74
12.65
1.642.37
0.360
1
2
3
4
5
6
7
8
9
10
11
12
13
14
White Indian Pakistani Bangladeshi Other Asian BlackCaribbean
BlackAfrican
Black Other Chinese Other
Ethnic Group of Potential Fathers
Black Caribbean Mothers 30-34:White Fathers – Potential EG of Child
0.088
0.022
0.794
0.096
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
White
India
n
Pakis
tani
Bang
ladeshi
Othe
r Asia
n
Black C
aribb
ean
Black A
frican
Black O
ther
Chine
seOt
her
Ethnic Group of Child
What about 2011?
No SCAMing!
No Usual Address => Address Where Staying
More ethnic groups– Will extend GLA ethnic model to up to 18 groups
Home Address Second Address
Second Address (for work) Workplace
Short-term Migrant Commuting
6 persons to a form
