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Interregional Migration and Land Use Pressure B.Eiselt, N. Giglioli, R.Peckham

Interregional Migration and Land Use Pressure B.Eiselt, N. Giglioli, R.Peckham. ?. Acknowledgement. Based mainly on work carried out in the project: Lot 4: “Spatial Analysis of interregional migration in correlation with other socio-economic statistics” Performed by JRC for EUROSTAT

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Interregional Migration and Land Use Pressure B.Eiselt, N. Giglioli, R.Peckham

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  1. Interregional Migration and Land Use Pressure B.Eiselt, N. Giglioli, R.Peckham ?

  2. Acknowledgement Based mainly on work carried out in the project: Lot 4: “Spatial Analysis of interregional migration in correlation with other socio-economic statistics” Performed by JRC for EUROSTAT from July 1998-July 1999 by: B.Eiselt, N. Giglioli, R.Peckham, A. Saltelli, T.Sorensen

  3. Outline Interregional migration modeling: Data and Software Spatial Interaction models Cluster analysis Modeling Results GIS based Visualization tool Speculation on land use pressure: Link to urban expansion Ideas for modeling Index for pressure

  4. Data and Software • Databases: • GISCO - admin. boundaries (NUTS1 & 2) • REGIO - socio-economic data + flow matrices Software: • SPSS 8.0 for statistical analysis • ARC-VIEW GIS (standard in E.C.)

  5. Data

  6. Spatial Interaction Models Description Exploratory analysis Estimation of the models Parameters interpretation Simulation

  7. Models description • The General Spatial InteractionModelhas the form • where: • i - parameters which characterise the propensity of each origin to generate flows; • j -parameters which characterise the attractiveness of each destination; •  is a distance deterrence effect.

  8. Models description • Four types: • Double Constrained - exploring attractive properties of destinations and repulsive properties of origins • Origin Constrained, and Destination Constrained - finding explanatory variables • Unconstrained Model • - finding explanatory variables, and simulating

  9. Models description To apply the ordinary least squares fitting we make a Logarithmic transformation of the model in a way that the the error is Normal distributed

  10. Correlation analysis Analysis of correlation (Germany example) Variables OUT_total IN_total GDP UNEMP OUT_total 1 0.89 -0.67 0.96 IN_total 1 0.93 -0.57 GDP 1 -0.57 UNEMP 1

  11. Cluster analysis • Grouping together regions displaying similar properties, • - based on the values of: • total inflow divided by population, • total outflow divided by population, • GDP per inhabitant, • unemployment rate ( % of total workforce). • These variables are relative and are hence not influenced by the population size of the regions.

  12. Cluster analysis

  13. Cluster analysis

  14. Age structure of flows

  15. Flows by clusters

  16. Models !

  17. Models Estimation - Model choice: - Method: Least Square and stepwise regression method - Indicator Goodness of Fit: R2 adjusted

  18. Statistics ! Kurtosis ? Poisson distribution ? NORMALISED ?? ALL OK ! Normal distribution ? Assumptions ? Central Limit Theorem ? Skewness ?

  19. Models Estimation Model estimated for Germany 1991: Adj -R2 = 0.74 logYij = 1.767+0.934logGDPi+0logUnpi+ +0.829logGDPj+0.739logUnpj-1.156logdij Note: the unemployment of origin is not significant

  20. Simulation ? Model fit (1991) R2 = 74%; Forecast (1993) R2 = 65.6%

  21. Simulation ? Model fit (1990) R2 = 74.6%; Forecast (1994) R2 = 55.2%

  22. Simulation ? Model fit (1990) R2 = 78.4%; Forecast (1994) R2 = 56.8%

  23. Visualization tool

  24. Visualization tool

  25. Visualization tool

  26. Visualization tool

  27. Conclusions re migration modeling • 1) Some positive results. Some hope and possibilities for modeling. • 2) Need more complete and more detailed data, - especially on the flows, e.g. • - age structure, • - educational level, • - cost of living, crime rate etc. • 3) Need to explore and test application to other EU-Countries (e.g. DK, S, Fi, NL and UK)

  28. Speculation Can we link: migration -> land use change ? e.g. look for correlation between: populationandurban area - for major cities - using satellite data to measure changes in urban perimeter, e.g. at 5 or 10 year intervals. As it happens there is Project MURBANDY: http://www.riks.nl/RiksGeo/projects/murbandy/Index.htm

  29. Speculation Then we could establish the link: GDP -> Migration -> Land use pressure Driving force Effect Calibrate model using:Pop. : Urban area correlation - probably different in different countries (different habits, housing types etc) Improve using: - age structure of flows - education structure of flows

  30. Speculation Ideas for index of pressure:- Population/Urban area ?  Pop/Urban area ? = Net Flow /Urban Area from CORINE data (grid)

  31. Simulated pressure index for year 2000 (tentative!)

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