Updating MS (Mobilité spatiale - Spatial Mobility) Regions and Labour market areas in Switzerland
1 Today
106 MS Regions 16 Labour market areas
Colour : Labour market areas : MS Regions
Basic units : LAU 2
2 MS Regions
Work done in the 80’s Method : based on commuters statistics But : limits were adapted to administrative boundaries (land use planning regions and mountain regions)
3 First results applying the LMA method – R algorithm
4 New LMA Regions in Switzerland
• As our country is a small one, and considering that we base our study on the previous version of Labour market areas established in the 80’s with the scope of maintaining more or less the same number of LMAs, we have chosen the following values for the various parameters : minSC : 0.57; tarSC : 0.75; minSZ : 3000; tarSZ : 5000 • Various tests have been done (see the results of some of them on the next slides)
5 Basic units : LAU 2 Basic units : LAU 2 Calcul des régions MS : tests avec différents seuils
Seuils :
minSZ : 3000 tarSZ : 5000 minSC: 0.57 tarSC 0.75
Nombre de régions : 78
Basic units : LAU 2 Problems and solutions (so far)
9 Size of LMA Regions Number of commuters
800000
700000
600000
500000
400000
300000
200000
100000
0
10 Size of LMA Regions
• Stakeholders and experts consider that these big urban areas should be subdivided (for statistical purposes)
Solution tested : subdivision of the 5 biggest urban areas (Zurich, Geneva, Basel, Bern and Lausanne) with the same method, but with lower thresholds
11 Nombre de régions MS CH : 78 Legend
LMA Region Lausanne
Present MS Regions Lausanne LMA Region subdivided Example : Lausanne
Thresholds for Switzerland :
minSZ : 3000 tarSZ : 5000 minSC: 0.57 tarSC 0.75
Thresholds for big urban areas :
minSZ : 1000 tarSZ : 5000 minSC : 0.4 tarSC : 0.75 Legend
LMA Region Zurich
Present MS Regions Zurich LMA Region subdivided Example : Zürich
Thresholds for Switzerland :
minSZ : 3000 tarSZ : 5000 minSC: 0.57 tarSC 0.75
Thresholds for big urban areas :
minSZ : 1000 tarSZ : 5000 minSC : 0.4 tarSC : 0.75 Results
• Not very convincing • LMAs in urban regions seem not to be as consistent as for rural regions, probably because of the thresholds chosen • Decision : try to apply other methods / algorithms to these types of regions : ClustGeo, Skater, PAM Ward, kmeans, all with and without spatial constraints • Results : seem to be better (work in progress)
15 Other problems
• Cantons (NUTS-3) boundaries (a certain number of LMAs are cross-border) • Language boundaries (same problem) Some stakeholders consider important LMAs boundaries to fit to these administrative or cultural bounderies : discussions in progress
• Cross-border LMAs (with France, Italy, Germany and Austria)
16 Rural / Urban typology / DEGURBA
• At that time, we didn’t consider these questions as we try to solve the other problems mentioned…
17