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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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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?)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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.
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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?
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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%
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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?)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
Leeds Output Areas (OA)
Census AreaCounts
Populationby
Ethnicity
Source: UK Census Small Area Statistics (SAS)
Leeds Output Areas (OA)
Census AreaCounts
Householdsby
Socio-economic status
Source: UK Census Small Area Statistics (SAS)
Leeds Output Areas (OA)
Census AreaCounts
Populationby
Health Status
Source: UK Census Small Area Statistics (SAS)
LeedsOAs
Household &Individual Profiles
AgeEthnicitySocio-econ gpHealth status
Demographics
Housing
Source: Moses
Moses: Population Reconstruction
Model
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Health Status (Optimised)
Actual Model
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Car ownership (Co-varying)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
England & Wales
Household &Individual Profiles
AgeEthnicitySocio-econ gpHealth status
HealthLifestylesBehaviour
Attitudes
Source: British Household Panel Survey (BHPS)
LeedsOAs
Household &Individual Profiles
AgeEthnicitySocio-econ gpHealth status
Demographics
Housing
Source: Moses
LeedsOAs
Household &Individual Profiles
AgeEthnicitySocio-econ gpHealth status
HealthLifestylesBehaviour
Attitudes
Source: Moses
Moses: Activity Model
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Diabetes
wards3 by diabetes
52 to 59 (11)51 to 52 (3)50 to 51 (2)47 to 50 (9)25 to 47 (8)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
• MoSeS: Dynamic Model
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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)
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
leeds_wards by aire1
25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)
leeds_wards by aire1
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
leeds_wards by aire1
25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)
leeds_wards by aire1
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
leeds_wards by aire1
25 to 221 (3)10 to 25 (2)5 to 10 (7)3 to 5 (14)0 to 3 (3089)
leeds_wards by aire1
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
• Moses Methodology: Architecture
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Moses Selection Portlet
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Moses Architecture
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Moses Mapping Portlet 1: Google Maps
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007Moses Mapping Portlet 2: SeeGeo
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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!
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
• 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
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
Moses: Discussion
3. This work is hard
• Maybe too hard?
• Scale back ambition?
• Extend capability/ resourcing?
School of GeographyFACULTY OF EARTH AND ENVIRONMENT
MoSeS: November 2007
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?