Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis. Presentation

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 behaviourmodelling

Health and social care...
2006
2001
2031
2016

Health and Social Care…
2001
2031
Co-dependency
LLTI
2031
2001

Health and Social Care…
2001
2031
Ethnicity
2031
2001
Multiple
Deprivation

Moses Dynamic Model
Transition rates for fertility, mortality and migration are spatially disaggregated
E.g. fertility: rates by age, marital status and location
Event is simulated as a Monte Carlo process
Example: married woman, aged 28, living in Aireborough
Probability of maternity is 0.127
Pull a probability from a distribution of random numbers; if <= 0.127 then the event occurs
All events in discrete intervals of one year

MoSeS Data Sources
Census Small Area Statistics
Special Migration Statistics
Health Survey for England
Household and Individual SARS
International Passenger Statistics
National Travel Survey
ONS Vital Statistics
BHPS
General Household Survey
Hospital Episode Statistics
EASEL Housing Needs Study
Google Maps

Moses Dynamic Model

MoSeS Dynamic Model

Transport…
Population and average speed changes in Leeds from 2001 to 2031

2031
2001
Transport…
2015
Traffic Intensity *
* Traffic Intensity=Traffic load/Road capacity

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

Data Issues and Questions
Complexity
Visualisation
Integration
Proliferation
Generation

Complexity of data
Complexity, scale and volume of data inputs

Data visualisation

Data integration
Modelling and simulation as data integration
“Data diarrhoea, information constipation”
-> data compression
-> missing data

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 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 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!!!

Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis. Presentation

by NeISSProject on Feb 04, 2011

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Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis. Presentation — Presentation Transcript