School of GeographyFACULTY OF ENVIRONMENT

Spatial Microsimulation for City Modelling, Social Forecasting and

Urban Policy Analysis

Mark Birkin 6649386

Example: Urban Simulation

MoSeS Project

• Can we project the population of a city forwards in time over a 25 year period?

• technically & intellectually demanding

• policy relevant

• housing, transport, health care, education, …

• Three components• Population reconstruction

• Dynamic simulation

• Activity and behaviour modelling

Health and social care...

2001

20312016

2006

Health and Social Care…

2001Co-dependency

2031

LLTI20312001

Health and Social Care…2001

Ethnicity

2031

MultipleDeprivation

20312001

Moses Dynamic Model

Ageing/mortality

Fertility

Inmigration

Emigration

Household formation

Marital status

Health status

Local migration

Transition rates for fertility, mortality and migration are spatially disaggregated

E.g. fertility: rates by age, marital status and locationEvent is simulated as a Monte Carlo process

Example: married woman, aged 28, living in AireboroughProbability of maternity is 0.127Pull a probability from a distribution of random numbers; if <= 0.127 then the event occursAll events in discrete intervals of one year

MoSeS Data Sources

Census Small Area Statistics

Household and Individual SARS

ONS Vital Statistics

Special Migration Statistics

International Passenger Statistics

BHPS

Health Survey for England

National Travel Survey

General Household Survey

Hospital Episode Statistics

EASEL Housing Needs Study

Google Maps

Moses Dynamic Model

Simulation Year 1

Gender FAge 45

Location AMarital status W

Household status LHealth G

Gender FAge 18

Location AMarital status S

Household status LHealth G

Moses Dynamic Model

Simulation Year 1 2

Gender F FAge 45 46

Location A AMarital status W W

Household status L SHealth G G

Gender F FAge 18 19

Location A BMarital status S S

Household status L SHealth G G

Moses Dynamic Model

Simulation Year 1 2 3

Gender F F FAge 45 46 47

Location A A AMarital status W W W

Household status L S SHealth G G G

Gender F F FAge 18 19 20

Location A B BMarital status S S S

Household status L S SHealth G G G

Moses Dynamic Model

Simulation Year 1 2 3 4 5

Gender F F F F FAge 45 46 47 48 49

Location A A A A AMarital status W W W W W

Household status L S S S SHealth G G G G G

Gender F F F F FAge 18 19 20 21 22

Location A B B B CMarital status S S S S C

Household status L S S S CHealth G G G G G

Gender MAge 24

Location CMarital status C

Household status CHealth G

Moses Dynamic Model

Simulation Year 1 2 3 4 5 6 7

Gender F F F F F F FAge 45 46 47 48 49 50 51

Location A A A A A A AMarital status W W W W W W W

Household status L S S S S S SHealth G G G G G G G

Gender F F F F F F FAge 18 19 20 21 22 23 24

Location A B B B C C CMarital status S S S S C C C

Household status L S S S C F FHealth G G G G G G G

Gender M M MAge 24 25 26

Location C C CMarital status C C C

Household status C C FHealth G G G

Gender MAge 0

Location CMarital status S

Household status FHealth G

Moses Dynamic Model

Simulation Year 1 2 3 4 5 6 7 8 9 10

Gender F F F F F F F F F FAge 45 46 47 48 49 50 51 52 53 54

Location A A A A A A A A A AMarital status W W W W W W W W W W

Household status L S S S S S S S S SHealth G G G G G G G G G G

Gender F F F F F F F F F FAge 18 19 20 21 22 23 24 25 26 27

Location A B B B C C C C C CMarital status S S S S C C C C M M

Household status L S S S C F F F F FHealth G G G G G G G G G G

Gender M M M M M MAge 24 25 26 27 28 29

Location C C C C C CMarital status C C C C M M

Household status C C F F F FHealth G G G G G G

Gender M M M MAge 0 1 2 3

Location C C C CMarital status S S S S

Household status F F F FHealth G G G G

Gender FAge 0

Location CMarital status S

Household status FHealth G

Moses Dynamic Model

Simulation Year 1 2 3 4 5 6 7 8 9 10 11 12 13

Gender F F F F F F F F F F F F FAge 45 46 47 48 49 50 51 52 53 54 55 56 57

Location A A A A A A A A A A A A AMarital status W W W W W W W W W W W W W

Household status L S S S S S S S S S S S SHealth G G G G G G G G G G G G G

Gender F F F F F F F F F F F F FAge 18 19 20 21 22 23 24 25 26 27 28 29 30

Location A B B B C C C C C C C C DMarital status S S S S C C C C M M M M M

Household status L S S S C F F F F F F F FHealth G G G G G G G G G G G G G

Gender M M M M M M M M MAge 24 25 26 27 28 29 30 31 32

Location C C C C C C C C DMarital status C C C C M M M M M

Household status C C F F F F F F FHealth G G G G G G G G G

Gender M M M M M M MAge 0 1 2 3 4 5 6

Location C C C C C C DMarital status S S S S S S S

Household status F F F F F F FHealth G G G G G G G

Gender F F F FAge 0 1 2 3

Location C C C DMarital status S S S S

Household status F F F FHealth G G G G

Moses Dynamic Model

Simulation Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Gender F F F F F F F F F F F F F F F FAge 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Location A A A A A A A A A A A A A A A AMarital status W W W W W W W W W W W W W W W W

Household status L S S S S S S S S S S S S S S SHealth G G G G G G G G G G G G G G G G

Gender F F F F F F F F F F F F F F F FAge 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Location A B B B C C C C C C C C D D D DMarital status S S S S C C C C M M M M M M M M

Household status L S S S C F F F F F F F F F F FHealth G G G G G G G G G G G G G G G G

Gender M M M M M M M M M M M MAge 24 25 26 27 28 29 30 31 32 33 34 35

Location C C C C C C C C D D D DMarital status C C C C M M M M M M M M

Household status C C F F F F F F F F F FHealth G G G G G G G G G G G P

Gender M M M M M M M M M MAge 0 1 2 3 4 5 6 7 8 9

Location C C C C C C D D D DMarital status S S S S S S S S S S

Household status F F F F F F F F F FHealth G G G G G G G G G G

Gender F F F F F F FAge 0 1 2 3 4 5 6

Location C C C D D D DMarital status S S S S S S S

Household status F F F F F F FHealth G G G G G G G

MoSeS Dynamic Model

Simulation Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Gender F F F F F F F F F F F F F F F F F F F F F F F F F F F F FAge 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73

Location A A A A A A A A A A A A A A A A A A A A A A A A A A A A AMarital status W W W W W W W W W W W W W W W W W W W W W W W W W W W W W

Household status L S S S S S S S S S S S S S S S S S S S S S S S S S S S SHealth G G G G G G G G G G G G G G G G G G G G G G G G G G G G G

Gender F F F F F F F F F F F F F F F F F F F F F F F F F F F F FAge 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Location A B B B C C C C C C C C D D D D D D D D D D D D D D D D DMarital status S S S S C C C C M M M M M M M M M M M M M M M M M M M M M

Household status L S S S C F F F F F F F F F F F F F F F F F F F F F F F FHealth G G G G G G G G G G G G G G G G G G G G G G G G G G G G G

Gender M M M M M M M M M M M M M M M M M M M M M M M M MAge 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Location C C C C C C C C D D D D D D D D D D D D D D D D DMarital status C C C C M M M M M M M M M M M M M M M M M M M M M

Household status C C F F F F F F F F F F F F F F F F F F F F F F FHealth G G G G G G G G G G G P P M M G G G G G G G G G G

Gender M M M M M M M M M M M M M M M M M M M M M M MAge 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Location C C C C C C D D D D D D D D D D D D D E E E EMarital status S S S S S S S S S S S S S S S S S S S S S S S

Household status F F F F F F F F F F F F F F F F F F F F S S SHealth G G G G G G G G G G G G G G G G G G G G G G G

Gender F F F F F F F F F F F F F F F F F F F FAge 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Location C C C D D D D D D D D D D D D D D D D DMarital status S S S S S S S S S S S S S S S S S S S S

Household status F F F F F F F F F F F F F F F F F F F FHealth G G G G G G G G G G G G G G G G G G G G

0

200,000

400,000

600,000

800,000

1,000,000

2001 2005 2010 2015 2020 2025 2031

Pop

ulat

ion （

pers

on）

0

10

20

30

40

50

60

Ave

rage

spe

ed （

km/h

）

Population Average speed

Population and average speed changes in Leeds from 2001 to 2031

Transport…

2001 2031

2015

* Traffic Intensity=Traffic load/Road capacity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Traffic Intensity *

Transport…

Scenario-based forecasting

Public Policy

Source: MAPS2030

Simulation of Epidemics

Ferguson et al, Nature, 2006

The El Farol Bar Problem

Everyone wants to go the bar

- unless it’s too crowded!

Must relax neoclassical economic assumptions (homogeneity of preferences, simultaneous decision-making)

Individual actors/ agent-based decision-making

- generic template for real markets

heterogeneous

out of equilibrium

(Arthur, 1994)

NeISS Architecture

NeISS Portal

NeISS Portal

Census Data Population

ReconstructionModel

Crowdsourced data

PersistentRepository

Policy

InfrastructureData

SpatialData

SurveyData

DynamicModel

Visualisation

Social Simulation

Models

Data FusionTool

Research Training Public

Planners

MapTube

NGSSyntheticData

Key IndicatorsReports

Provenance

What ifassumptions

EnhancedData

SyntheticData

SyntheticData

Synthetic Data Sets Provenance

Synthetic Data Sets Provenance

Data Issues and Questions

• Complexity

• Visualisation

• Integration

• Proliferation

• Generation

Complexity of data

Complexity, scale and volume of data inputs

Legend

Silverburn

Observed / Predicted0.00 - 0.50

0.51 - 1.00

1.01 - 1.50

1.51 - 2.00

2.01 - 3.40

µ

0 30 6015

Kilometers

Centre 1961 1971a 1971b Capture

Liverpool 54 77 69 10.4%Manchester 51 70 63 10.0%Haydock 0 0 47Warrington 6 11 7 36.4%Wigan 7 11 6 45.5%

Data visualisation

Data integration

Modelling and simulation as data integration

• “Data diarrhoea, information constipation”• → data compression

• → missing data

Legend

Silverburn

Observed / Predicted

0.00 - 0.50

0.51 - 1.00

1.01 - 1.50

1.51 - 2.00

2.01 - 3.40

µ

0 30 6015

Kilometers

Proliferation of data domains

• “customer science”• public/ private/ commercial

• Crowd-sourced data

Data Generation

Example 1. (Silverburn)

• 400 post sectors

• 100 destinations

• 6 ages

• 4 ethnic groups

• 4 social/ income groups

• 2 car ownership• 516 inputs; 8 million model

flows (sparse matrix!)

Example 2. (MoSeS)

• 25 years of simulation

• 60 million individuals

• 200? characteristics

• 20? scenarios

Example 3. (Epstein, 2009)

• 8 billion agents!

• Dynamic resolution at 10 minute intervals?!!

Example 1. (Silverburn)

• 400 post sectors

• 100 destinations

• 6 ages

• 4 ethnic groups

• 4 social/ income groups

• 2 car ownership• 516 inputs; 8 million model

flows (sparse matrix!)

Example 2. (MoSeS)

• 25 years of simulation

• 60 million individuals

• 200? characteristics

• 20? scenarios

Example 3. (Epstein, 2009)

• 8 billion agents!

• Dynamic resolution at 10 minute intervals?!!

Conclusion

Social simulation involves quite a lot of data intensive research!!

Note that quite a lot of social scientists have so far failed to appreciate this important fact!!!