spatial microsimulation approaches to population forecasting dimitris ballas department of...

Spatial microsimulation approaches to population

forecasting

Dimitris BallasDepartment of Geography, University of Sheffield

http://www.sheffield.ac.uk/sasi

e-mail: [email protected]

ESRC Research Methods FestivalOxford, 1-3 July 2008

RES-163-27-1013

http://www.sheffield.ac.uk/sasi

Outline

• What is microsimulation?• What is spatial microsimulation?• Dynamic spatial microsimulation• Projecting small area statistics into the

future• Projecting small area microdata into the

future• Available software• Concluding comments

What is microsimulation?

• A technique aiming at building large scale data sets

• Modelling at the microscale• A means of modelling real life

events by simulating the characteristics and actions of the individual units that make up the system where the events occur

What is microsimulation?

PERSON AHID PID AAGE12 SEX AJBSTAT … AHLLT AQFVOC ATENURE AJLSEG …

1 1000209 10002251 91 2 4 … 1 1 6 9 …

2 1000381 10004491 28 1 3 … 2 0 7 -8 …

3 1000381 10004521 26 1 3 … 2 0 7 -8 …

4 1000667 10007857 58 2 2 … 2 1 7 -8 …

5 1001221 10014578 54 2 1 … 2 0 2 -8 …

6 1001221 10014608 57 1 2 … 2 1 2 -8 …

7 1001418 10016813 36 1 1 … 2 1 3 -8 …

8 1001418 10016848 32 2 -7 … 2 -7 3 -7 …

9 1001418 10016872 10 1 -8 … -8 -8 3 -8 …

10 1001507 10017933 49 2 1 … 2 0 2 -8 …

11 1001507 10017968 46 1 2 … 2 0 2 -8 …

12 1001507 10017992 12 2 -8 … -8 -8 2 -8 …

Some examples of microsimulation applications in

Economics

• PENSIM. This was a microsimulation model for the simulation of pensioners’ incomes up to the year 2030. Hancock et al. (1992)

• Sutherland and Piachaud (The Economic Journal, 2001) developed and used a microsimulation methodology for the assessment of British government policies for the reduction of child poverty in the period 1997-2001. Results suggest that the number of children in poverty will be reduced by approximately one-third in the short term and that there is a trend towards further reductions

Microsimulation in Geography and Regional Science

• First study by Hägerstrand (1967) – spatial diffusion of innovation

• Foundations for spatial microsimulation of populations laid by Wilson and Pownall (1976): building small area microdata

• Clarke et al. (1979 onwards) extended the theoretical framework of Wilson and Pownall

Spatial microsimulation applications

• Static ‘What-if’ simulations– impacts of alternative policy scenarios on the

population can be estimated• if a factory closed what are the impacts on the local

economy• if we close a school where will the pupils be re-

distributed

• “Static updating”– update a basic micro-dataset and future-oriented

what-if simulations• if local taxes are raised today what would the

redistributive effects have been between different socio-economic groups and between areas of the city by 2007?

Examples of spatial microsimulation (1)

• Birkin and Clarke (1988 & 1989) SYNTHESIS model• Williamson (1992) OLDCARE model• Williamson (1996) and Williamson et al. (1998) first

ever application of combinatorial optimisation for static microsimulation

• Holm et al. (1996), Vencatasawmy et al. (1999) – SVERIGE model (Spatial Modelling Centre – Sweden – the first comprehensive spatial microsimulation model in the world! (http://www.smc.kiruna.se/)

Examples of spatial microsimulation (2)

• Caldwell et al. (1996) CORSIM model• Wegener and Spiekermann (1996) Urban models:

land-use and travel• Veldhuisen et al. (2000) RAMBLAS – daily

activity patterns• Ballas (2001), Ballas and Clarke (2000, 2001a &

2001b) SimLeeds model• Ballas, Clarke and Commins (2001) SMILE –

model of the Irish rural economy• Ballas et al. (2005) SimBritain model• Birkin and colleagues, MoSeS model

Spatial microsimulation procedures

• The construction of a micro-dataset from samples and surveys

• Static What-if simulations, in which the impacts of alternative policy scenarios on the population are estimated: for instance if there is a taxation policy change today, what would be the “morning after” effect? Which areas would be most affected?

• Dynamic modelling, to update a basic micro-dataset and future-oriented what-if simulations: for instance if the current government had raised income taxes this year what would the redistributive effects have been between different socio-economic groups and between central cities and their suburbs by 2011?

Reweighting approaches (1)

PERSON AHID PID AAGE12 SEX AJBSTAT … AHLLT AQFVOC ATENURE AJLSEG …

1 1000209 10002251 91 2 4 … 1 1 6 9 …

2 1000381 10004491 28 1 3 … 2 0 7 -8 …

3 1000381 10004521 26 1 3 … 2 0 7 -8 …

4 1000667 10007857 58 2 2 … 2 1 7 -8 …

5 1001221 10014578 54 2 1 … 2 0 2 -8 …

6 1001221 10014608 57 1 2 … 2 1 2 -8 …

7 1001418 10016813 36 1 1 … 2 1 3 -8 …

8 1001418 10016848 32 2 -7 … 2 -7 3 -7 …

9 1001418 10016872 10 1 -8 … -8 -8 3 -8 …

10 1001507 10017933 49 2 1 … 2 0 2 -8 …

11 1001507 10017968 46 1 2 … 2 0 2 -8 …

12 1001507 10017992 12 2 -8 … -8 -8 2 -8 …

Reweighting approaches (2)

Small area table 1 (household type)

Small area table 2 (economic activity of household head)

Small area table 3 (tenure status)

Area 1 Area 1 Area 1

60 "married couple households"

80 employed/self-employed

60 owner occupier

20 "Single-person households"

10 unemployed 20 Local Authority or Housing association

20 "Other" 10 other 20 Rented privately

Area 2 Area 2 Area 2

40 "married couple households"

60 employed/self-employed

60 owner occupier

20 "Single-person households"

20 unemployed 20 Local Authority or Housing association

40 "Other" 20 other 20 Rented privately

Tenure and car ownership example

Household car ownership characteristics

Household tenure characteristics

1 car

2+ cars

No car

Owner-occupier

LA/HA rented

Other

Simulation 27 24 49 39 17 44

Census 50 20 30 60 10 30

Absolute error

23 4 19 21 7 14

age/sex male femaleunder-50 1 1over-50 2 1

Deterministic Reweighting the BHPS - a simple example (1)

A hypothetical sample of individuals (list format)Individual sex age-group weight1st male over-50 12nd male over-50 13d male under-50 14th female over-50 15th female under-50 1

In tabular format:


Hypothetical Census data fora small area:


Reweighting the BHPS - a simple example (2)

Calculating a new weight, so that the sample will fit into the Census table

In tabular format:


Hypothetical Census data fora small area:

Individual sex age-group weight New weight 1st male over-50 1 1 x 3/2 = 1.5 2nd male over-50 1 1 x 3/2 = 1.5 3d male under-50 1 1 x 3/1 = 3 4th female over-50 1 1 x 1/1 = 1 5th female under-50 1 1x 5/1 = 5

Probabilistic synthetic reconstruction

After Birkin, M., Clarke, M. (1988), SYNTHESIS – a synthetic spatial informationsystem for urban and regional analysis: methods and examples, Environment and Planning A, 20, 1645-1671

Dynamic spatial microsimulation

• Probabilistic dynamic models, which use event probabilities to project each individual in the simulated database into the future (e.g. using event conditional probabilities).

• Implicitly dynamic models, which use independent small area projections and then apply the static simulation methodologies to create small area microdata statically

Steps 1st 2nd … Last Age, sex and marital status and location (DED level) (given)

Age: 25 Sex: Male Marital Status: Single GeoCode: Leitrim Co., DED 001 Ballinamore

Age: 76 Sex: Female Marital Status: married GeoCode: Leitrim Co., DED 002 Cloverhill

… Age: 30 Sex: Male Marital Status: married GeoCode: Leitrim Co., DED 078 Rowan

Probability (conditional upon age, sex, location) of hh to migrate

0.30 0.05 … 0.26

Random number 0.2 0.4 … 0.4 Migration status assigned on the basis of random sampling

Migrant Non-migrant … Non-migrant

Probability (conditional upon age, sex, location) of hh to survive

0.9 0.5 0.8

Random number 0.5 0.4 … 0.4 Survival status Survived Deceased … Survived

Probabilistic dynamic models

after Ballas D , Clarke, G P, Wiemers, E, (2005) Building a dynamic spatial microsimulationmodel for Ireland , Population, Space and Place, 11, 157–172 (http://dx.doi.org/10.1002/psp.359)

http://dx.doi.org/10.1002/psp.359

Event modelling· Demographic transitions

1. Age all individuals 2. Change marital status (marriage &

divorce rates: trends & assumptions)3. Birth (fertility rate: trends &

assumptions)4. Death5. (use 5-year survival rates deaths/pop

at risk)6. Migration

· Socio-economic transitionsEducation (Enter school, university, etc.) Labour market (become employed/unemployed etc.)

Simulating migration, education and social mobility

“It is well known that mobility rates are substantially higher among renters than among homeowners. Similarly, the age structure of migrants to and from neighborhoods is likely to be quite different in a neighborhood comprised primarily of homeowners in comparison with a renter-dominated neighborhood.” (Rogerson and Plane, 1998: 1468)

“During their lifetimes, the simulated individuals have to change their educational and employment status. They will enter school with different probabilities when they are between 14 and 20 years old, they will be employed in different jobs, lose their jobs, earn an income which depends on their type of job, and eventually retire with different probabilities depending on their ages.”

(Gilbert and Troitzch, 1998)

Determining inter-dependencies

…while a woman’s labour force status can depend on the number of children she has and on her marital status, it cannot also influence the probability of the woman having a child in any year. The ordering of the modules necessarily involves making assumptions about the direction of causality in relationships between variables.

(Falkingham and Lessof, 1992: 9)

The SimBritain model

• Funded by:– Joseph Rowntree Foundation– BT– Welsh Assembly Government

• Aimed at creating small area microdata for the years 1991, 2001, 2011 and 2021 (at electoral ward and parliamentary consistency level) for the whole of Britain by combining the Census small area statistics and the British Household Panel Survey

• Extrapolate constraint values and re-populate each area anew at the-yearly intervals using the original samples

• Simulate this population for the years 2001, 2011, 2021 (“groundhog day” scenario)

• What-if policy analysis

SimBritain: combining Census data with the BHPS

Census of UK population:

• 100% coverage• fine geographical detail• Small area data

available only in tabular format with limited variables to preserve confidentiality

• cross-sectional

British Household Panel Survey:

• sample size: more than 5,000 households

• Annual surveys (waves) since 1991

• Coarse geography• Household attrition

Ballas, D. , Clarke, G.P., Dorling, D., Eyre, H. and Rossiter, D., Thomas, B (2005) SimBritain: a spatial microsimulation approach to population dynamics, Population, Space and Place 11, 13–34 (http://dx.doi.org/10.1002/psp.351)

http://dx.doi.org/10.1002/psp.351

How do we make SimBritain dynamic?

• Original strategy: model the ageing death and creation of households (from the panel nature of the BHPS) and the geographic movement of households (using migration data from the Census and other sources). This was abandoned when migration data proved to be of insufficient quality.

• Intermediate strategy: extrapolate constraint values and re-populate each area anew at the-yearly intervals using the original samples

• Future strategy: create synthetic household histories from the panel data. Methods are also being developed to allow for inflation of values over time (e.g. income, pc ownership etc) and for changing geographical composition (via projected constraint values)

Projecting small area statistics into the future (1)

where u, v and w are the smoothed proportions in 1971, 81 and 91 respectively, W is the observed ward proportion in 1991 and A is the projected ward proportion in 2001.

))/(lnln*)(ln*exp(ln 32 vuwWA


where Lt and bt are respectively (exponentially smoothed) estimates of the level and linear trend of the series at time t, whilst Ft+m is the linear forecast from t onwards

mbLF

bLLb

bLYL

ttmt

tttt

tttt

11

11

)1()(

))(1(


whereW = ward proportionw = smoothed ward proportiont = census year

)])/(ln(ln)(lnexp[ln 31020

210 ttttt wwwWW

SimBritain: spatial distribution of “poor” households, 1991

SimBritain: spatial distribution of “poor” households, 2001

Spatial distribution of “poor” households, 2011

Spatial distribution of “poor” households, 2021

SimBritain: spatial distribution of “retired” households, 1991

Census data

Year 1951 1961 1971 1981 1991 Predicted proportion for

1991

Difference between

projection and actual

data

Class I & II 19% 21% 24% 28% 34% 34% 0%

Class III 51% 50% 49% 47% 43% 44% 1%

ClassIV & V 30% 29% 27% 25% 24% 22% -2%

How do we know it makes sense?Comparing Census data to projected data for 1991 (projection based on data from the Censuses of 1961, 1971 and 1981)

projected v actual 2 or more cars 2001

05

1015202530354045

Local Authority

Per

cent

age

projected 2+ cars2001

actual 2+ cars2001

“Projecting” small area microdata into the future1. Establish a set of constraints2. Choose a spatially defined source population3. Repeatedly sample from source4. Adjust weightings to match first constraint5. Adjust weightings to match second constraint6. …7. Adjust weightings to match final constraint8. Go back to step 4 and repeat loop until

results converge9. Save weightings which define membership of

SimBritain

CONSTRAINT TABLES

TABLE CATEGORY

Car Ownership no cars 1 car 2+ cars

Social Class affluent middle income less affluent

Demography 1 child 2+ children no children

Employment active retired inactive

Households married couple lone parent other

Tenure owner occupied council tenant other

How do we know it makes sense?

Average age se = 1.0 r squared = .760 beta = 1.22RR

cage

30

32

34

36

38

40

42

44

46

48

50

sage 30 32 34 36 38 40 42 44 46 48 50

How do we know it makes sense?

Long-term illness se = 1.7 r squared = .767 beta = 1.19

cill

0.00

0.05

0.10

0.15

0.20

0.25

0.30

sill 0.00 0.05 0.10 0.15 0.20 0.25 0.30

The potential of dynamic spatial microsimulation for policy analysis

Classifying households• Very poor: all households with income below 50% of the

median York income• Poor: all households with income more than 50% of the

median but lower than 75% of the median• Below-average: all households living on incomes higher

than 75% of the median but less than or equal to the median

• Above-average: all households living on incomes higher than the median and lower than 125% of the median

• Affluent: all households living on incomes above 125% of the median

Ballas, D., Clarke, G P, Dorling D, Rossiter, D. (2007), Using SimBritain to Modelthe Geographical Impact of National Government Policies,Geographical Analysis 39, pp.44-77 (doi:10.1111/j.1538-4632.2006.00695.x)

http://dx.doi.org/10.1111/j.1538-4632.2006.00695.x

SimBritain results in York

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

1991 2001 2011 2021

Year

(%)

of

ho

use

ho

lds

in Y

ork

Very poor

Poor

Under-average

Over-average

Affluent

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

1991 2001 2011 2021

Year

(%)

of

chil

dre

n i

n Y

ork

Very poor and poor

Under-average

Over-average

Affluent

SimBritain results, York: children in households

Very poor households 1991 2001 2011 2021

Households (% of all households in York) 17.2% 17.3% 17.8% 21.3%

Individuals (% of all individuals in York) 14.7% 13.3% 13.7% 20.5%

Children (% of all children in York) 21.8% 17.7% 18.6% 38.5%

LLTI (as a % of all individuals in group) 9.0% 7.3% 5.4% 7.9%

Elderly (over 64 years as a % of all individuals in group) 30.1% 32.0% 33.3% 44.2%

Individuals in group with father's occupation: unskilled (%) 10.5% 6.8% 3.3% 15.1%

Reporting anxiety and depression (% of all individuals in group) 10.6% 10.3% 7.4% 3.1%

Reporting health problems with alcohol or drugs (% of all individuals in group) 0.9% 1.1% 0.3% 0.0%

Individuals who reported that they have no one to talk to 19.9% 23.8% 31.1% 31.5%

Living standards of very poor households

Causes of povertyVery poor households 1991 2001 2011 2021

Unemployed (as a % of economically active in group)

45.4% 25.7% 16.7% 9.6%

Economically active (%)

18.3% 17.1% 16.8% 17.7%

Vocational qualifications (% of all adult individuals in group)

20.9% 20.7% 18.9% 12.2%

Full-time job (% of economically active in group)

43.1% 65.9% 80.7% 90.1%

Adults with no qualifications (% of all adult individuals in group)

58.4% 65.2% 72.3% 78.9%

Very poor households: sources of income

An analysis of persons in the city who are below the “primary”poverty line shows that more than one half of these are members of families whose wage-earner is in work but in receipt of insufficient wages.

Rowntree (2000: 114)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1991 2001 2011 2021

Average householdearned income

Average householdincome from othersources

Average householdincome from Investment

Average householdbenefit income

Average householdpension income

Future challenges: modelling income and substitution effects

A substitution effect making leisure more attractive than workAn income effect, encouraging people to work more to make up the loss of income

“Different taxes have different effects, and affect people at different levels of income or in different household circumstances in different ways.”

(Hill and Bramley, 1986: 85)

(%) happy more than usual

8.8 - 10.110.1 - 10.910.9 - 11.511.5 - 11.911.9 - 14.5

Simulating geographies of happiness (Ballas, 2008)

Estimated geography of happiness in Wales (%) happy more than usual, 1991

Welsh Unitary Authorities (%)8.918.91 - 9.479.47 - 10.0110.01 - 10.3710.37 - 10.65

N

EW

S


parliamentary constituencies9.396 - 9.6969.696 - 10.10910.109 - 10.37210.372 - 10.72310.723 - 11.547

N

EW

S


Welsh Unitary Authorities (%)10.61 - 11.0511.05 - 11.6811.68 - 12.2812.28 - 13.7513.75 - 14.54

N

EW

S



N

EW

S

Spatial Microsimulation software: Micro-MaPPAS

• Micro-simulation• Modelling • and• Predictive• Policy• Analysis• System• Turning academic research in to a usable tool for the

real world

Ballas, D., Kingston, R., Stillwell, J., Jin, J. (2007) A microsimulation-basedspatial decision support system, Environment and Planning A 39(10), 2482 – 2499

Income < £10k, 3 children, LA home

Conclusions

• Tackle calibration problems• Create a socio-economic atlas of the future

for Britain• Policy spatial micro-modelling - income and

substitution effect• Include more regional subsystems (labour

demand, schools, hospitals, etc.)• Small area multiplier analysis• What-if, what-will-happen-if and What-would-

have-happened-if analysis

More information on microsimulation:http://www.microsimulation.org/

… and free book downloads: http://www.jrf.org.uk/bookshop/details.asp?pubID=659

http://www.microsimulation.org/

http://www.jrf.org.uk/bookshop/details.asp?pubID=659

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