QMSS2, Leeds, 02-09/07/09 Dynamic population model and an application for Leeds B.M.Wu School of Geography University of Leeds.

Slides:



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

Methods of analysing change over time and space Ian Gregory (University of Portsmouth) & Paul Ell (Queens University, Belfast)
The Census Area Statistics Myles Gould Understanding area-level inequality & change.
Employment transitions over the business cycle Mark Taylor (ISER)
Artificial Payment Card Market: A Multi-Agent Approach Biliana Alexandrova-Kabadjova, CCFEA, EssexCCFEA Edward Tsang, CCFEA, EssexCCFEA Andreas Krause,
SETTINGS AS COMPLEX ADAPTIVE SYSTEMS AN INTRODUCTION TO COMPLEXITY SCIENCE FOR HEALTH PROMOTION PROFESSIONALS Nastaran Keshavarz Mohammadi Don Nutbeam,
ESRC Seminar, London, 02/04/09 Dynamic microsimulation with spatial interactions B.M.Wu, M.H.Birkin and P.H.Rees School of Geography University of Leeds.
Lincolnshire Research Observatory Projecting not Predicting Projecting Not Predicting – How external influences can impact Adam.
School of Geography FACULTY OF ENVIRONMENT Spatial Microsimulation and Crime Analysis Mark Birkin Professor of Spatial Analysis and Policy University of.
Research Implementation Management Disciplinary impact Research Research-implementation gap Knight et al (in prep) Conservation assessments in the primary.
Introduction to STINMOD and Microsimulation Modelling in Australia Ben Phillips: Principal Research Fellow, NATSEM, 21 Feb 2015.
Demographic challenges and statistical developments Kim Dunstan, Senior Demographer.
Towards an integrated South African Green Economy Model (SAGEM)
Modelling Crime: A Spatial Microsimulation Approach Charatdao Kongmuang School of Geography University of Leeds Supervisors Dr. Graham Clarke, Dr. Andrew.
Evaluating the future: forecasting urban development using the urbansim land use model in el paso, tx. Quinn P. Korbulic.
Understanding Population Trends and Processes: Links between internal migration, commuting and within household relationships Oliver Duke-Williams School.
Role of Economics in W&W Project and in Climate Change Projects Explain how land use patterns evolve over time Forecast future land use change Determine.
Sample of Anonymised Records: User Meeting Propensity to migrate by ethnic group: 1991 & 2001 Paul Norman 1, John Stillwell 2 & Serena Hussain 2 School.
GENESIS Web 2.0 Agent City Simulation: Establishing a user community and enabling collaborators to manipulate simulations and develop models Andy Turner.
Models of migration Observations and judgments In: Raymer and Willekens, 2008, International migration in Europe, Wiley.
From UPED to REMI: Utah’s Experience in Developing Long-Term Economic and Demographic Projections Utah Governor’s Office of Planning and Budget January.
Model Building and Simulation Chapter 43 Research Methodologies.
Multivariate volatility models Nimesh Mistry Filipp Levin.
School of Geography CENTRE FOR SPATIAL ANALYSIS AND POLICY e-Infrastructure for Large-Scale Social Simulation Mark Birkin Andy Turner.
An Introduction to Social Simulation Andy Turner Presentation as part of Social Simulation Tutorial at the.
Methodology for a school- leavers’ survey Irena Kogan MZES, University of Mannheim.
Modeling and Simulation
Population projections: Uncertainty and the user perspective Presentation to INIsPHO Seminar Newry, 2 December 2008 Tony Dignan.
The use of census data as an input in forecasting population, employment and land use change 5 th October 2010 Andy Dobson David Simmonds Consultancy.
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
School of Geography FACULTY OF ENVIRONMENT Microsimulation: population reconstruction and an application for household water demand modelling Title ESRC.
1 POPULATION PROJECTIONS Session 8 - Projections for sub- national and sectoral populations Ben Jarabi Population Studies & Research Institute University.
Income Distribution in the European Union Silvia Avram, Horacio Levy, Alari Paulus, Holly Sutherland Bucharest, 10 th November 2012.
School of something FACULTY OF OTHER School of Geography FACULTY OF EARTH AND ENVIRONMENT MOSES: A Synthetic Spatial Model of UK Cities and Regions Mark.
Sustainable rural populations: the case of two National Park areas Alan Marshall Ludi Simpson Cathie Marsh Centre for Census and Survey Research.
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Transition Rule Elicitation Methods for Urban Cellular Automata Models Junfeng.
P.O. Box AL Maastricht Hedwig van Delden Garry McDonald Jasper van Vliet 1 Integrating macro-economic developments.
Business Process Change and Discrete-Event Simulation: Bridging the Gap Vlatka Hlupic Brunel University Centre for Re-engineering Business Processes (REBUS)
Chapter 15 Unlocking the Business Environment Chapter 15 The Macro Environment – Demographic Influences By the end of this chapter you should have a better.
Introduction to Spatial Microsimulation Dr Kirk Harland.
Evaluating Transportation Impacts of Forecast Demographic Scenarios Using Population Synthesis and Data Simulation Joshua Auld Kouros Mohammadian Taha.
Physical and Human Features of Places. Exceptional Performance I explain and can predict change in the characteristics of places over time by using my.
SICSA student induction day, 2009Slide 1 Social Simulation Tutorial International Symposium on Grid Computing Taipei, Taiwan, 7 th March 2010.
Demographic change at small area level Small area statistics to develop public policy Paul Norman School of Geography, University of Leeds ESRC RES
Updating Household Projections for England Bob Garland.
CROSS-COUNTRY INCOME MOBILITY COMPARISONS UNDER PANEL ATTRITION: THE RELEVANCE OF WEIGHTING SCHEMES Luis Ayala (IEF, URJC) Carolina Navarro (UNED) Mercedes.
A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]
Autumn School Dynamic MSM16-18 November 2015 | L-Esch-sur-Alzette Slide 1 Note Combining LIAM2 and EUROMOD: a (useful?) possibility.
Exploring Microsimulation Methodologies for the Estimation of Household Attributes Dimitris Ballas, Graham Clarke, and Ian Turton School of Geography University.
Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution Samer Hassan Luis Antunes Mill á n Arroyo MABS 2008 Acknowledgments.
Hukou Identity, Education and Migration: The Case of Guangdong
The micro-geography of UK demographic change Paul Norman School of Geography, University of Leeds understanding population trends and processes.
Why use landscape models?  Models allow us to generate and test hypotheses on systems Collect data, construct model based on assumptions, observe behavior.
Introduction to decision analysis modeling Alice Zwerling, Postdoctoral fellow, JHSPH McGill TB Research Methods Course July 7, 2015.
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
Learning for test 1 Add the notes to this presentation 1.
Simulation in Operational Research form Fine Details to System Analysis.
Traffic Simulation L2 – Introduction to simulation Ing. Ondřej Přibyl, Ph.D.
An Introduction to Urban Land Use Change (ULC) Models
Using spatial microsimulation in a spatial decision support system
A Virtual Earth Model of the Dementias in China
How demographics and the economic downturn are affecting the way we live LSE Seminar: 1 July 2013 Neil McDonald: Visiting Fellow CCHPR.
Optimization Techniques for Natural Resources SEFS 540 / ESRM 490 B
POPULATION PROJECTIONS
R. W. Eberth Sanderling Research, Inc. 01 May 2007
Immigration, Diversity, Human Capital and the Future Labor Force of Developed Countries: the European Model Guillaume Marois1, Patrick Sabourin1, Alain.
Structural dynamic microsimulation modelling at the National Institute
Structural dynamic microsimulation modelling at the National Institute
Presentation transcript:

QMSS2, Leeds, 02-09/07/09 Dynamic population model and an application for Leeds B.M.Wu School of Geography University of Leeds

QMSS2, Leeds, 02-09/07/09 Outline Introduction Approaches of modelling social systems An application for Leeds Model description Initial result analysis Model improvement Summary

QMSS2, Leeds, 02-09/07/09 To understand: various social modelling approaches individual based models dynamic MSM how a typical dynamic MSM is structured  how a typical dynamic MSM works  importance of data for a MSM  alternative modelling approaches that may compliment MSM Learning objectives

QMSS2, Leeds, 02-09/07/09 Social Systems are “messy” boundaries large and complex (Moss, 2000) Approaches of modelling social systems

QMSS2, Leeds, 02-09/07/09 Individual Based Models (IBM): MSM (Microsimulation Model) CA (Cellular Automata) ABM (Agent Based Model) Approaches of modelling social systems

QMSS2, Leeds, 02-09/07/09 MSM Approaches of modelling social systems + t = 1, 2, …, t = n

QMSS2, Leeds, 02-09/07/09 MSM: Static vs Dynamic Approaches of modelling social systems Type of MSMcharacteristicsAgeing technique Entity Interactivity TimePopulation Change Impact of previous step on the next StaticDeterministic / Stochastic Static ageingNoNo time element/ stocks of entities updates No DynamicStochasticDynamic ageing PossibleChange process and events built in Yes

QMSS2, Leeds, 02-09/07/09 Advantages of Static MSM:  quicker to run  simpler to develop and understand  lower costs: computing resources, skills and development time  often with very detailed programme simulations Approaches of modelling social systems

QMSS2, Leeds, 02-09/07/09 Advantages of Dynamic MSM:  more details  better representation of population ageing, especially in long term, as it accounts interim changes in economic and demographic trends  generally accepted more realistic representation of micro population unit changes Approaches of modelling social systems

QMSS2, Leeds, 02-09/07/09 MSM: Spatial and non-Spatial Approaches of modelling social systems “One can not be at two places at the same time.” ( Hägerstrand, 1967) “Means are to be employed somewhere.” (De Man, 1998) People have to live in a local area and they are affected by local environment.

QMSS2, Leeds, 02-09/07/09 CA source :

QMSS2, Leeds, 02-09/07/09 ABM

QMSS2, Leeds, 02-09/07/09 An application of Leeds: modelling objectives Modelling objectives:  To develop a complete representation of the Leeds population at a fine spatial scale  To produce rich, detailed and robust forecasts of the future population of Leeds  To investigate scenarios which relate demographics to service provision

QMSS2, Leeds, 02-09/07/09 Modelling Description  Dynamic representation of key demographic events /transactions in a geographically identified population  Macrosimulation and microsimulation models (MSM) are alternative ways of realising the processes (van Imhoff and Post, 1998)  We use a spatial MSM of the population and its dynamics, but the structure parallels the macro multi-state cohort-component (MSCC) projection model  An MSM depends on good data on the important transitions experienced by individuals  We experimented with an Agent Based Model(ABM) for a sub-population, students, where empirical data on migration has often proved problematic

QMSS2, Leeds, 02-09/07/09 What does that mean?  Scale  Leeds population:760,000  Each individual has about 60 individual variables + 20 household variables + area variables  Various probabilities/rates eg: localised single year of age based mortality probabilities  Movement, interaction and behaviour  Distinctive behaviours from various population groups in different demographic processes  Interdependency of household and individual variables in different demographic processes

QMSS2, Leeds, 02-09/07/09 Demographic processes in the MSM 6 modularised processes :  simple processes  complex processes  individuals and households

QMSS2, Leeds, 02-09/07/09 Initial Results: Leeds population change

QMSS2, Leeds, 02-09/07/09 Initial Results: small area variation

QMSS2, Leeds, 02-09/07/09 Characteristics of student migrants  Students are highly mobile during their studies in the universities  Mostly only move around the area close to the universities where they study, NOT in the suburban areas  Most of them will leave the city once they finish their study, NOT growing old in the suburban areas  Due to the replenishment of the student population each year, the population of the small areas where university student stay tends to remain younger than other areas

QMSS2, Leeds, 02-09/07/09 ABM  An alternative approach that models individuals as agents through their interactions with each other and the environment that they live in.  It is very flexible to introduce heterogeneous agents with distinctive behaviours through their built-in rules  It is useful in modelling features of the population where knowledge and data is lacking (Billari et al., 2002).

QMSS2, Leeds, 02-09/07/09 ABM experiments: Student Migrants  We recognise the following groups: First year undergraduates Other undergraduates Master students Doctoral students  We apply the following general rules: Each group is allowed set years to stay in the area Students prefer to stay with their fellow students Students stay close to their university of study, subject to housing availability They don’t “do” marriage and fertility

QMSS2, Leeds, 02-09/07/09 Observed Predicted Comparison of Results: Pure MSM

QMSS2, Leeds, 02-09/07/09 Comparison of Results: MSM with ABM Observed Predicted

QMSS2, Leeds, 02-09/07/09 We have discussed the difficulty in modelling the social systems and various modelling approaches. IBM provide detailed info at individual level and MSM is an important social modelling approach, especially in assisting public policy development and planning. Dynamic MSM provides a more realistic reflection of the studied system than static MSM. Typical dynamic MSM structure and functions. MSM depends on quality data and may be strengthened by complementary techniques such as ABM where there is a knowledge gap. Summary

QMSS2, Leeds, 02-09/07/09 Thank you!