school of something faculty of other school of geography faculty of earth and environment moses: a...

School of somethingFACULTY OF OTHER

School of GeographyFACULTY OF EARTH AND ENVIRONMENT

MOSES: A Synthetic Spatial Model of UK Cities and Regions

Mark BirkinUniversity of Leeds


MoSeS: November 2007

OVERVIEW

• MoSeS

• Modelling and Simulation for e-Social Science

• Project funded under the ESRC’s e-Social Science initiative

• One of eight major projects in the National Centre for e-Social Science (NCeSS) (£12 million programme)

• Others include Geographic Visualisation of Urban Environments (GeoVUE)

• And (arguably) a bunch of Computer Science



OVERVIEW• e-Science

• Major research council initiative in the UK over the last 6/7 years

• Matched by the US Cyberinfrastructure programme

• Aims to address the Grand Challenges of scientific research

• Suggestion is that new solutions are brought into view through a combination of:

• Data availability

• Simulation and visualisation

• Virtual collaboration

• All supported through a new generation of computational infrastructure (Grid?)



Powering the Virtual Universehttp://www.astrogrid.ac.uk(Edinburgh, Belfast, Cambridge, Leicester, London, Manchester, RAL)

Multi-wavelength showing the jet in M87: from top to bottom – Chandra X-ray, HST optical, Gemini mid-IR, VLA radio. AstroGrid will provide advanced, Grid based, federation and data mining tools to facilitate better and faster scientific output.

Picture credits: “NASA / Chandra X-ray Observatory / Herman Marshall (MIT)”, “NASA/HST/Eric Perlman (UMBC), “Gemini Observatory/OSCIR”, “VLA/NSF/Eric Perlman (UMBC)/Fang Zhou, Biretta (STScI)/F Owen (NRA)”

p4 Printed: 20/04/23



myGrid Project

Motivation: In silico experiments necessitate the virtual organization of people, data, tools and machines. The scientific process also necessitates an awareness of the experience base, both of personal data as well as the wider context of work. The management of all these data and the co-ordination of resources to manage such virtual organizations and the data surrounding them needs significant computational infra-structure support.



OVERVIEWMoSeSThe Modelling and Simulation of e-Social Science.

MoSeS Objectives: To develop a complete representation of the UK population at a fine spatial scale To produce rich, detailed and robust forecasts of the future population of the UK To investigate scenarios which relate demographics to service provision - emphasis on policy applications within the health and transport policy sectors



MoSeS: An Example

• Leeds Social Services

• Requirement to understand the future needs of the population (morbidity/ mortality)

• Allocation of resources

• Service delivery

• Statutory targets e.g. reduction of (spatial) inequalities in life expectancy

• Preparation of strategy demands a relatively long view: 2027?



0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

2004 2009 2014 2019 2024 2029

65-74 75-84 85+

Population Projections

30,000

32,000

34,000

36,000

38,000

40,000

42,000

44,000

46,000

48,000

2004 2009 2014 2019 2024 2029

0-4 5-9 10-14

Source: Office for National Statistics

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

2004 2009 2014 2019 2024 2029

65-74 75-84 85+

32,000

34,000

36,000

38,000

40,000

42,000

44,000

46,000

48,000

50,000

2004 2009 2014 2019 2024 2029

0-4 5-9 10-14

Source: Moses



2006 2011 2016 2021 2026 2031UK 671334 675994 687030 697905 702397 702929New Commonwealth - Africa and Caribbean 13464 14321 15815 17786 19489 20802New Commonwealth - Asia 32515 34987 39457 44709 49551 54118Others 21245 24616 29162 34236 39900 46556

0

800000

1

2006

2031

UK0

100000

1

2006

2031

0

100000

1

2006

2031

0

100000

1

2006

2031

Caribbean

Asia Europe etc

Ethnic ProjectionsSource: Moses



Growth in Elderly Population (85+)2006-2031

leeds_wards by oldpeople

1,150 to 1,200 (2)1,000 to 1,150 (5)

850 to 1,000 (10)700 to 850 (12)

0 to 700 (3086)



Model of disability (1) of Disability (1)

12.2%

87.8%

Disabled in Leeds Disabled in UK

Source: BHPSSource: Moses

Estimate of the disabled in Leeds: 51,599



9.1%

90.9%

Disabled in Leeds, 2006 Disabled in Leeds, 2031

Source: MosesSource: Moses

Estimate of the disabled in Leeds 2031: 93,698

Increase of 82%!

14.1%

85.9%



leeds_wards by disab

0.1 to 0.12 (4)0.09 to 0.1 (10)0.08 to 0.09 (16)0.07 to 0.08 (3)0 to 0.07 (3082)

leeds_wards by disab

0.1 to 0.11 (4)0.09 to 0.1 (10)0.08 to 0.09 (16)0 to 0.08 (3085)



Model of Disability (3):Scenario 5Plus1

9.1%

90.9%

14.1%

85.9%

Disabled in Leeds, 2006Disabled in Leeds, 2031

Source: MosesSource: Moses

Revised estimate of the disabled in Leeds 2031: 70,359

Increase of ‘only’ 36%!

Baseline Scenario

Assume that a 65 year old in 2031 enjoys the

health of a 60 year old today



Other Estimates of Need

Count Rate Index Count Rate Index Count Rate IndexHas poor health 48630 8% 100 86064 13% 156 71360 11% 129Health limits daily activities 84460 14% 100 151489 23% 158 125405 19% 131Health hinders getting dressed 11622 2% 100 25984 4% 197 17877 3% 135Need help getting out of bed 2030 0.3% 100 3074 0.5% 133 1992 0.3% 86Need help with bath/shower 7193 1% 100 12337 2% 151 8376 1% 102Visits to GP 1921994 3.3 100 2552113 3.9 117 2384012 3.6 109Provide care in household 30677 5% 100 45625 7% 131 40029 6% 115Provide care outside household 61716 11% 100 75792 11% 108 70626 11% 101Hours of care provided 871453 1.5 100 1224235 1.8 124 1116696 1.7 113Average hours of care provided 9.4 100 10.1 107 10.1 107No-one who will listen 22553 4% 100 34501 5% 135 35173 5% 137

Baseline: 2006 Projection: 2031 Scenario: 2031



Moses: Methodology

• What are the functional components of an applied urban simulation?

• Recreation of a baseline population

• A dynamic/ forecasting capability

• A suite of service utilisation and activity models

• A container (spatial decision support system?)



Moses: Methodology

• We create a synthetic representation of the UK population

• Using data from the 2001 Census Small Area Statistics and the Sample of Anonymised Records

• 24 million households and 60 million residents are individually represented

• The synthetic population looks just like the actual population but no ‘real’ citizens are included

• The reconstructed population includes a wide range of social and demographic attributes – age, ethnicity, housing, economic activity etc



Leeds Output Areas (OA)

Census AreaCounts

Populationby

Age

Source: UK Census Small Area Statistics (SAS)

England & Wales

Household &Individual Profiles

AgeEthnicitySocio-econ gpHealth status

Demographics

Housing

Source: UK Census - Sample of Anonymised Records (SAR)


Census AreaCounts

Populationby

Ethnicity



Census AreaCounts

Householdsby

Socio-economic status



Census AreaCounts

Populationby

Health Status


LeedsOAs



Demographics

Housing

Source: Moses

Moses: Population Reconstruction

Model



Health Status (Optimised)

Actual Model



Car ownership (Co-varying)



England & Wales



HealthLifestylesBehaviour

Attitudes

Source: British Household Panel Survey (BHPS)

LeedsOAs



Demographics

Housing

Source: Moses

LeedsOAs



HealthLifestylesBehaviour

Attitudes

Source: Moses

Moses: Activity Model



Smoking

leeds_wards by SMOKING

0.27 to 0.299 (4)0.25 to 0.27 (11)0.23 to 0.25 (4)0.21 to 0.23 (10)0 to 0.21 (3086)



Carers

leeds_wards by carers

1,600 to 1,900 (5)1,400 to 1,600 (10)1,300 to 1,400 (8)1,200 to 1,300 (4)

0 to 1,200 (3088)



Diabetes

wards3 by diabetes

52 to 59 (11)51 to 52 (3)50 to 51 (2)47 to 50 (9)25 to 47 (8)



• MoSeS: Dynamic Model



Population at Year Start

Ageing Process

Probability of death: remove from database

Probability of birth: generate new individual

Movement within Leeds: review location flag

Migration from Leeds: remove from database

Migration to Leeds: generate new individual

Population at Year End

Probability of marriage: update marital status

MosesDynamic

Model



Migration Model

• We combine two approaches:

• A person-specific “general” model, using probabilities of migration derived from the BHPS applied to “cloned” individuals in households derived from the 2001 Census SAR

• Location specific information about migration intensities in small areas (2001 Census SMS), which are used to modify the results of the person-specific model

• The model has a two stage procedure:

• Migrant generation protocol

• Migrant distribution protocol



Migrant generation protocol

• Assess migration probabilities from an analysis of BHPS data, 2000-2004 for

• a) households• b) groups• c) individuals

• Major drivers of migration identified using a stepwise chi-squared estimation procedure

• Households: age of head, household size, housing type• Individuals: age, household size, marital status• Groups: merged with individuals (small numbers)

• National rates are locally adjusted by age using the Census Migration Statistics (SMS)


MoSeS: November 2007Migrant generation: households

Chi-square 1036 Chi square 184 Chi square 80

Chi-square 28 Chi-square 28 Chi-square 25

Chi-square 22 Chi-square 6 Chi-square 5

Age

00.5

11.5

22.5

33.5

4

16-24 25-34 35-44 45-54 55-64 65-74 75+

Dwelling Type

0

0.5

1

1.5

2

Detached Terraced Flats etc

Household Size

0

0.2

0.4

0.6

0.8

1

1.2

1.4

One Two Three Four +

Tenure

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Owned Rented Council

Occupation

00.20.40.60.8

11.21.4

Agri

Mini

ng

Man

ufElec

Constr

Retail

Hotels

Trans

port

Finan

ceOth

er

Marital Status

00.20.40.60.8

11.21.4

Mar

ried

Couple

Wid

owed

Divorc

ed

Separ

ated

Never

m'd

Health

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Very good Good Moderate Poor Very poor

Ethnicity

0

0.2

0.4

0.6

0.8

1

1.2

1.4

White Non-white

Sex

0.880.9

0.920.940.960.98

11.021.041.06

Male Female



Migrant generation: individualsAge

00.5

11.5

22.5

33.5

4

16-24 25-34 35-44 45-54 55-64 65-74 75+

Household size

00.20.40.60.8

11.21.41.6

Two Three Four +

Marital status

00.5

11.5

22.5

33.5

4

Mar

ried

Couple

Wid

owed

Divorc

ed

Separ

ated

Never

m'd

Health

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Very good Good Moderate Poor Very poor

Dwelling Type

00.20.40.60.8

11.21.41.6

Detached Terraced Flats etc

Industry

00.20.40.60.8

11.21.4

Agri

Mini

ng

Man

ufElec

Constr

Retail

Hotels

Trans

port

Finan

ceOth

er

Tenure

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

Owned Rented Council

Headship

0.920.940.960.98

11.021.041.061.08

Head Not head

Sex

0.98

0.985

0.99

0.995

1

1.005

1.01

Male Female

Ethnicity

0

0.2

0.4

0.6

0.8

1

1.2

White Non-white Other



Migrant distribution protocol

• The problem can be described as follows:

• Estimate migration rates by location, age, household size and housing type: this process creates a stock of vacant housing

• For each migrant, by location and household type (age, size) find a destination location by location and house type

• Calibrate this process using data on known moves (by distance – from the census SMS) and known assignments of household type to house type (BHPS)



SimulationDatabase

AggregateTo Migrant Population

AggregateTo VacantDwellings

Migrantgeneration

model

Spatial Interaction ModelCompute dwelling

preferencefor eachmigrant

Update Location and

Dwelling Characteristics

1

4

3

2

5

2

Migration distribution protocol

( See Birkin and Clarke 1987; Nakaya et al. 2006)



Migrant distribution model distribution model

Beta calibration

0

12

34

5

67

8

0 0.2 0.4 0.6 0.8 1 1.2

Beta value

Pre

dic

ted

dis

tan

ce

LambdaCalibration



Model 1 versus Observed

y = 1.1821x - 2.6462R2 = 0.6468

-50

0

50

100

150

200

250

300

350

400

450

500

0 100 200 300 400



leeds_wards by aire1

25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)


25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)

Model Results: Aireborough

Observed Predicted




25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)


25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)

Model Results: Seacroft

Observed Predicted




25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)


25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)

Model Results: Headingley

Observed Predicted



Agent-based simulation of student migrantsAgent-based simulation of student migrants

•We recognise the following groups:•First year undergraduates•Other undergraduates•Master students•Doctoral students

•We apply the following rules:•Each group is allowed set years to stay in an area•Students stay close to their university of study•They don’t “do” marriage and fertility



• Moses Methodology: Architecture



Moses Selection Portlet



Moses Architecture



Moses Mapping Portlet 1: Google Maps


MoSeS: November 2007Moses Mapping Portlet 2: SeeGeo



Moses: Discussion

1. Moses is not the only work in this area in either an academic or a policy environment

• But has some interesting and unique features!



• Moses: Discussion

2. This work has both an intellectual and a practical value

• Even though it is not ‘critical’

• Sometimes it is necessary to be ‘constructive’ as well



Moses: Discussion

3. This work is hard

• Maybe too hard?

• Scale back ambition?

• Extend capability/ resourcing?



Moses: Conclusions and Next Steps

• There is still much work to be done to establish a convincing set of demonstrator applications for urban simulation

• Enhanced visual representation of simulation outputs is one key ingredient

• Collaboration with GeoVUE has important strategic value

• Embedding this research more clearly within a paradigm of (generative) social simulation is a potential means to re-enter the mainstream

• Genesis project?

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