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Interregional Migration and Land Use PressureInterregional Migration and Land Use PressureB.Eiselt, N. Giglioli, R.PeckhamB.Eiselt, N. Giglioli, R.Peckham

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Acknowledgement

Based mainly on work carried out in the project:Based mainly on work carried out in the project:

Lot 4: “Spatial Analysis of interregional migration in Lot 4: “Spatial Analysis of interregional migration in correlation with other socio-economic statistics”correlation with other socio-economic statistics”

Performed by JRC for EUROSTATPerformed by JRC for EUROSTAT

from July 1998-July 1999from July 1998-July 1999

by: B.Eiselt, N. Giglioli, R.Peckham, A. Saltelli, T.Sorensenby: B.Eiselt, N. Giglioli, R.Peckham, A. Saltelli, T.Sorensen

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OutlineInterregional migration modeling:Interregional migration modeling:

Data and SoftwareData and Software

Spatial Interaction modelsSpatial Interaction models

Cluster analysisCluster analysis

ModelingModeling

ResultsResults

GIS based Visualization toolGIS based Visualization tool

Speculation on land use pressure:Speculation on land use pressure:

Link to urban expansionLink to urban expansion

Ideas for modelingIdeas for modeling

Index for pressureIndex for pressure

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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.)

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DataDataCountry REGIO database New data CommentsGermany 1975-1990 Only West Germany

1991-1993 1991-19931994 Missing data

Denmark 1990-19931994 Missing data

Italy 1975-1994 1990-1994Spain 1979-1994 1990-1994France 1968-1989 Aggregated into 1968-1974, 1975-

1981, and1982-1989 ( + missing data for1982-1989)

Belgium 1975-1995 1990-1994 Change of regions 1992-1995 ormissing data 1975-1991

Finland 1981-1995 1990-1994Netherlands 1972-1985 Missing data

1986-1995 1990-1994Portugal 1983-1992 Rounded numbers + missing data

(0.0)1990 NB different from the CD data

(more realistic)Sweden 1980-1995 1990-1994UnitedKingdom

1979-1989 000...???

1991-19931994 000...???1995

1990-1994 NB 1994 is different from the CDdata (realistic)

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Spatial Interaction ModelsSpatial Interaction Models

Description

Exploratory analysis

Estimation of the models

Parameters interpretation

Simulation

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The General Spatial Interaction Model has 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.

ijd

jiijeY

Models descriptionModels description

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Models descriptionModels 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

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Models descriptionModels description

ijijjiijcdbaY lnlnln

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

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Correlation analysisCorrelation analysis

Analysis of correlation (Germany example)

Variables OUT_total IN_total GDP UNEMPOUT_total 1 0.9

60.89 -0.67

IN_total 1 0.93 -0.57GDP 1 -0.57UNEMP 1

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Cluster analysisCluster 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.

Cluster analysisCluster analysis

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Cluster analysisCluster analysis

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Age structure of flowsItaly (departures=arrivals) 1993

0

10000

20000

30000

40000

50000

60000

Age Group

Per

son

s

Italy

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Flows by clusters

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Models !

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Models EstimationModels Estimation

- Model choice:

- Method: Least Square and stepwise regression method

- Indicator Goodness of Fit: R2 adjusted

ijijjj

iiij

edUnemGDP

UnemGDPcY

logloglog

logloglog

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Statistics !

Skewness ?

Kurtosis ?

ln(flow)

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20

10

0

Assumptions ?

Poisson distribution ?

Normal distribution ?

Central Limit Theorem ?

ALL OK !

NORMALISED ??

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Models EstimationModels 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

Simulation ?Simulation ?

Model fit (1991) R2 = 74%; Forecast (1993) R2 = 65.6%

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Simulation ?Simulation ?

Model fit (1990) R2 = 74.6%; Forecast (1994) R2 = 55.2%

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Simulation ?Simulation ?

Model fit (1990) R2 = 78.4%; Forecast (1994) R2 = 56.8%

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Visualization tool

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Visualization tool

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Visualization tool

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Visualization tool

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Conclusions re migration modelingConclusions 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)

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SpeculationCan we link: migration -> land use change ?

e.g. look for correlation between:

population and urban 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

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SpeculationThen 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

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Speculation

Ideas for index of pressure:-

Population/Urban area ?

Pop/Urban area ? = Net Flow /Urban Area

from CORINE data (grid)

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Simulated pressure index for year 2000 (tentative!)