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Attractive Vicinity
Wenjuan Li, Einar Holm, Urban Lindgren
Spatial Modelling Centre (SMC), P.O. Box 839,
SE-981 28 Kiruna Sweden
Department of Social and Economic Geography, Umeå University,
SE-901 87 Umeå Sweden
Background of the research question:
Regional growth, employment, labour market
– Labour market—demand of labour and supply of labour
–Supply of labour—population distribution & redistributionplace attractiveness
Literatures related to place attractiveness:
Economic condition and performance — Thompson et al. 1964; Champion and Green 1987; Coombes and Raybould 1988;
Indicators of quality of life — Boyer & Savageau 1981; Rogerson et al. 1989;
Population redistribution trend — Kontuly 1998; Relative intrinsic attractivity (RIA) — Fotheringham et al.
2000; Driving forces of migration — Kontuly and Schön 1994;
Westlund 2002; Lundholm et al. 2004; Place marketing—Niedomysl 2004; etc.
Among the existed studies, place is often related to administration regions. It is difficult to view administration zones as a place with homogeneous intrinsic site attributes.
************************************* Floating grid and a from within model the fundamental spatial unit of analysis
should be the immediate surrounding of the individual—kilometre square (vicinity).
The surrounding properties of each vicinity can be regarded as intrinsic attributes of the vicinity itself.
Vicinity, local area (vicinity-5km), hinterland (5-50km) (108 000 vicinities)
BA
S
S$
S
S
Vicinity (1km square)
Local area(vicinity-5km)
Hinterland(5km-50km)
Figure 1.
Data Sources:
Astrid--an individual longitude database that is collected by Statistics Sweden (SCB).
the Swedish Red Map—1: 250 000 Swedish national general maps by Swedish Land Survey (Lantmäteriet).
Two dimensions of attractiveness attributes
four categories
three spatial scales Each vicinity has its
unique attributes
Physical factors
Demographic factors
Service factors
Labour market factors
Vicinity
Local area
Hinterland
Figure 2.
Physical factors
Demographic factors
Service factors Labour market factors
Coastline100m--dummy
LogPop Build-up area 500 meter dummy
Welfare
(economic support)
Coastline500m --dummy
Average age Build-up area 5 km dummy
Earning income
Lakes --dummy
Aged people Infrastructure dummy
Specialization
Forest % Children % Shops 500m turnover
Unemployment rate
Open land % University eduated people %
Private service 500m, 5km turnover
Immigrant %
Two indicators of place attractiveness:
Migration modelY = the change of individuals at age 20-64 in
vicinity level within two years (2000-2001) Income model
Y = the average vicinity earning income
OLS models (Full models) Y=a+b1x1 +…+bixi
Partial F test – to test if a single X variable (or X variables in a group) gives a significant contribution in the model
η2 -- the explanatory power of X variable (s) to
the Y variable
Totally 84 partial models were constructed for the Partial F test and η2 calculation
0
20
40
60
80
100
Unexplained
Unexplained
Demographic factors
Demographic factors
Labour market
Labour market
Correlative effect
Service factors
Physical factors
Migration model Income model
Figure 3. The total explanatory power of the two models and the composition of the
explanatory power in the four categories
0
15
30
45
60
Total explanatorypower 66.42%
0
2
4
6
8
10
Total explanatorypower 10.83%
Vicinity
Local area
Hinterland
Migration model Income model
Vicinity
Local area
Hinterland
Vicinity& hinterland
Local area& hinterland
Vicinity, local area& hinterland
Vicinity& local area
Figure 4. The composition of explanatory power over spatial scales
Conclusions: The explanatory power of physical, demographic, socio-
economic factors to place attractiveness vary considerably, and the effects of the factors differ across different spatial scales.
For the four categories, demographic factors play dominant role in place attractiveness.
Demographic factors, labour market factors, service factors, physical factors
For the three spatial scales, vicinity is the most important scale for place attractiveness.
Vicinity,hinterland, local area
Labour market factors
Service factors
Physical factors
Demographic factors
Open land
Hinterland
Local
area
Vicinity
LogPopImmigrantsUniversity educated peopleAverage ageChildren
LogPopUniversity educated peopleAverage ageChildren
Private serviceSpeicalizationUnemployment rate
Welfare
Immigrants
Figure 5.
