Presentation on theme: "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."— Presentation transcript:
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 & redistribution place 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) 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 Figure 2.
Physical factors Demographic factors Service factorsLabour market factors Coastline100 m--dummy LogPopBuild-up area 500 meter dummy Welfare (economic support) Coastline500 m --dummy Average ageBuild-up area 5 km dummy Earning income Lakes -- dummy Aged peopleInfrastructure 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 model Y = 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+b 1 x 1 + … +b i x i Partial F test – to test if a single X variable (or X variables in a group) gives a significant contribution in the model η 2 -- the explanatory power of X variable (s) to the Y variable Totally 84 partial models were constructed for the Partial F test and η 2 calculation
Figure 3. The total explanatory power of the two models and the composition of the explanatory power in the four categories
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