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School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis Mark Birkin 6649386.

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Presentation on theme: "School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis Mark Birkin 6649386."— Presentation transcript:

1 School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis Mark Birkin

2 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

3 Health and social care

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

5 Health and Social Care… 2001 Ethnicity 2031 Multiple Deprivation

6 Moses Dynamic Model Ageing/mortalityFertilityInmigrationEmigrationHousehold formationMarital statusHealth statusLocal migration 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 Pull a probability from a distribution of random numbers; if <= then the event occurs All events in discrete intervals of one year

7 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

8 Moses Dynamic Model

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16 MoSeS Dynamic Model

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

18 * Traffic Intensity=Traffic load/Road capacity Traffic Intensity * Transport…

19 Scenario-based forecasting

20 Public Policy Source: MAPS2030

21 Simulation of Epidemics Ferguson et al, Nature, 2006

22 The El Farol Bar Problem Everyone wants to go the bar -unless its 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)

23 NeISS Architecture

24 NeISS Portal

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27 Data Issues and Questions Complexity Visualisation Integration Proliferation Generation

28 Complexity of data Complexity, scale and volume of data inputs

29 Data visualisation

30 Data integration Modelling and simulation as data integration Data diarrhoea, information constipation data compression missing data

31 Proliferation of data domains customer science public/ private/ commercial Crowd-sourced data

32 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?!!

33 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!!!


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