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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
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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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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!)