Kazimierz Fiedorowicz, Jacek Fiedorowicz jkmfiedorowicz@wp.pl - - PowerPoint PPT Presentation
Kazimierz Fiedorowicz, Jacek Fiedorowicz jkmfiedorowicz@wp.pl Czestochowa University of Technology, Poland 44 th ISoCaRP Congress , Urban Growth without Sprawl: A way towards sustainable urbanization . Workshop 4 g r o
Kazimierz Fiedorowicz, Jacek Fiedorowicz jkmfiedorowicz@wp.pl Czestochowa University of Technology, Poland 44th ISoCaRP Congress, Urban Growth without Sprawl: A way towards sustainable urbanization . Workshop 4
Dalian, China, 19 – 23 September 2008
2
Contents Introduction
Summary and conclusion
3
Introduction
The following spatial structure models have been compared: Poland – variants D, N and R, „Baltica” – Estonia, Latvia and Lithuania, „Visehrad” – The Czech, Slovakia and Hungary, „Danuba” – Romania and Bulgaria, „Iberia” – Spain and Portugal The models have been compared in quantitative and qualitative terms. Components of the models are: – NUTS1 regions, – metropolises (ESPON), – infrastructural relations (TEN), – protected areas (Natura 2000).
4
The aim of shaping: to obtain the basis for drawing the future vision or concept of spatial development. The approach applied in shaping: strategic approach comprising 4 phases: – phase 1 – determination of a model – phase 2 – forecasting – phase 3 – selection of a model – phase 4 – determination of a vision/concept Components of a model: – regions – metropolis – infrastructural relations – protected areas
5
Variant 2 Variant 3 Assessment and Selection
National Spatial Structure Model Current National Spatial Structure Model Vision of National Spatial Structure Variant 1
PHASES OF THE APPROACH I
II III IV
Determining a Model Forecasting Variant Selection Vision
Figure 1. Application of strategic approach for determining a vision of spatial structure.
Source: The author’s own study.
6
Features of division: Model D, division into 5 provinces (historical) Model N, division into 6 NUTS1 regions (current) Model R, division into 9 regions (future) Comparison of the models: Model D is useless, Model N is applied for the current analysis Model R is applied for forecasting (Table 1) Components of the model: Model N – 6 regions, 9 metropolis Model R – 9 regions, 12 metropolis Data referring to the model: for Model N, the ratio of diversification for 6 measures accounts to 1:1.5 (Table 2a) Spatial arrangement: in accordance with Figure 2.
7
Variants Model D – 5 regions Model N – 6 regions Model R – 9 regions
Advantages
consciousness.
separation of large regions.
the current needs.
another.
system of infrastructural relations.
features of future division.
development of metropolitan functions in 12 cities.
differences.
Disadvantages 1. The historical development is
inapplicable to current situation.
metropolises.
arrangement of infrastructural relations.
correspond to future needs.
metropolises to 9.
regional differences.
quantitative spread of the division into regions.
favouring eastern part country.
infrastructural relations.
Table 1. Comparison of variants of the spatial structure models for Poland.
Source: The author’s own study.
8
No. NUTS1 regions Population 000 GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP in % Loss Index Lisb. in % Σ (2+3+ 4+5+6+7) in % 1. Central 7.733 66.7 34.2 92.4 88.9 55.5 70.6 67.6 2. Southern 7.964 51.4 30.6 71.4 62.0 27.8 47.1 48.4 3. Eastern 6.779 36.5 21.8 71.4 85.5 16.7 52.9 47.5 4. North-Western 6.067 51.0 30.3 69.2 82.1 16.7 47.1 49.4 5. South-Western 3.949 49.5 32.1 74.1 79.1 16.7 41.2 48.8 6. Northern 3.688 45.5 29.7 66.1 79.9 16.7 39.2 46.2 7. Poland 38.180 50.7 29.9 75.0 83.4 33.3 52.9 54.2 8. UE27 489.671 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Table 2a. Description of the spatial structure of Model N for Poland.
Source: The author’s own study based on the data of Central Statistical Office and Eurostat.
9
Figure 2. The spatial structure models for Poland.
Source: The author’s own study.
a) Model D, b) Model N c) Model R
10
regions Population 000 GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP % Loss index
Σ (2+3+ 4+5+6+7) in % 1. Estonia 1.356 55.7 31.9 148.7 101.9 50.0 105.9 82.3 2. Latvia 2.313 45.5 22.3 91.5 100.0 22.2 88.2 61.6
3.436 51.1 25.7 117.4 98.9 44.4 98.0 72.6 4. „Baltica” 7.105 50.2 26.2 121.4 99.7 38.9 102. 73.1 5. EU27 489.671 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Features of division: division into 3 NUTS1 regions Components of the model: 3 regions, 4 metropolis, relations Data referring to the model: the ratio of diversification for 6 measures accounts to 1:1.35 (Table 3) Spatial arrangement: in accordance with Figure 3. Table 3. Description of the spatial structure of the „Baltica” model.
Source: The author’s own study based on the data of Eurostat.
11
Figure 3. The „Baltica” spatial structure model.
Source: The author’s own study.
1 - Estonia; 2 – Latvia; 3 – Lithuania
Legend for figures 2-6:
12
No. NUTS1 regions Population 000 GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP % Loss index Lisb. in % Σ (2+3+ 4+5+6+7) in %
1. Central (CZ) 3.431 96.6 40.2 61.6 107.4 83.3 121.6 85.1 2. Middle (CZ) 4.292 66.7 31.8 46.0 104.1 50.0 94.1 65.4 3. Eastern (CZ) 2.484 60.5 32.1 50.0 97.2 44.4 86.2 61.7 4. Western (S) 2.464 71.4 40.3 81.2 99.5 33.3 84.3 68.3 5. Eastern (S) 2.918 44.3 28.9 54.9 84.0 16.7 37.3 44.3 6. Western (H) 3.094 58.0 39.0 58.0 92.7 11.1 82.4 56.7 7. Middle (H) 2.835 101.6 56.8 118.7 100.0 33.3 119.6 88.3 8. Eastern (H) 4.178 42.8 33.1 59.4 80.7 11.1 43.1 45.0 9. „Visehrad” 25.696 66.9 38.1 67.9 98.4 50.0 100.0 70.2 10. EU27 489.671 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Features of division: division into 8 NUTS1 regions Components of the model: 8 regions, 8 metropolis, relations Data referring to the model: the ratio of diversification for 6 measures accounts to 1:1.92 (Table 4) Spatial arrangement: in accordance with Figure 4. Table 4. Description of the spatial structure of the „Visehrad” model.
Source: The author’s own study based on the data of Eurostat.
13
Figure 4. The „Visehrad” spatial structure model.
Source: The author’s own study
1+2+3 – Czech Republic; 4+5 – Slovakia; 6+7+8 – Hungary
14
No. NUTS1 regions Population 000 GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP % Loss index
Σ (2+3+ 4+5+6+7) in %
1. North-Central (RO) 5.279 34.2 14.0 42.0 86.9 5.6 49.0 38.6 2. North-Eastern (RO) 6.588 26.7 12.7 40.2 92.3 5.6 58.8 39.4 3. Southern (RO) 5.550 42.8 18.1 67.9 92.4 33.3 66.7 53.5 4. South-Eastern (RO) 4.258 33.4 14.1 48.2 92.3 11.1 58.8 43.0 5. Northern (BG) 2.950 27.9 10.4 84.4 88.2 11.1 31.4 42.2 6. Southern (BG) 4.831 39.0 13.3 103.6 91.1 38.9 62.7 58.1 7. „Danuba” 29.454 34.2 14.2 63.4 90.2 22.2 54.9 46.5 8. UE27 489.671 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Features of division: division into 6 NUTS1 regions Components of the model: 6 regions, 7 metropolis, relations Data referring to the model: the ratio of diversification for 6 measures accounts to 1:1.5 (Table 5) Spatial arrangement: in accordance with Figure 5. Table 5. Description of the spatial structure of the „Danuba” model.
Source: The author’s own study based on the data of Eurostat.
15
Figure 5. The „Danuba” spatial structure model.
Source: The author’s own study.
1+2+3+4 – Romania; 5+6 – Bulgaria
16
No. NUTS1 regions Population 000 GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP % Loss index Lisb. in % Σ (2+3+ 4+5+6+7) in % 1. North-Western (H) 4.317 84.6 86.3 126.8 94.6 38.9 94.1 87.6 2. Northern (E) 4.204 119.2 96.6 162.9 105.8 50.0 129.4 110.8 3. Madrid(E) 5.763 132.1 102.5 163.4 108.2 88.9 133.3 121.4 4. Central (E) 5.373 83.9 83.6 107.6 95.7 38.9 88.2 83.0 5. Eastern (E) 12.115 110.2 92.8 122.8 106.5 61.1 113.7 101.2 6. Southern (E) 9.033 78.7 85.5 101.3 89.1 38.9 70.6 77.3 7. Northern (P) 4.488 60.8 52.0 48.2 104.9 33.3 70.6 61.6 8. Southern (P) 6.014 79.0 68.1 72.3 107.7 44.7 88.2 76.6 9. „Iberia“ 51.307 95.0 87.5 112.9 101.4 55.6 94.1 91.1 10. EU27 489.671 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Features of division: division into 8 NUTS1 regions Components of the model: 8 regions, 7 metropolis, relations Data referring to the model: the ratio of diversification for 6 measures accounts to 1:2 (Table 6) Spatial arrangement: in accordance with Figure 6. Table 6. Description of the spatial structure of the „Iberia” model.
Source: The author’s own study based on the data of Eurostat.
17
Figure 6. The „Iberia” spatial structure model.
Source: The author’s own study.
1+2+3+4+5+6 – Spain; 7+8 – Portugal
18
No. Models Share in population
GDP per capita in % GDP per worker in % Higher education in % Activity in % R+D share in GDP % Loss Index Lisb. in % Σ (2+3+ 4+5+6+7) in % 1. Poland 7.8 50.7 29.9 75.0 83.4 33.3 52.9 54.2 2. „Baltica” 1.5 50.2 26.2 121.4 99.7 38.9 102.0 73.1 3. „Visehrad” 5.2 66.9 38.1 67.9 98.4 50.0 100.0 70.2 4. „Danuba” 6.0 34.2 14.2 63.4 90.2 22.2 54.9 46.5 5. „Iberia” 10.5 95.0 87.5 112.9 101.4 55.6 94.1 91.1 6. Σ of the models 31.0 65.2 46.6 77.7 92.0 42.0 79.1 67.1 7. EU27 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Quantitative comparison Table 7. Quantitative comparison of the spatial structure models.
Source: The author’s own calculations based on the data from Tables 2, 3, 4, 5 and 6. Order: Differences for 6 measures: Iberia – 100%, Poland – 54%, Baltica – 73%, Visehrad – 70%, Danuba – 46%.
19
Models No. Components of spatial structure Poland „Baltica” „Visehrad” „Danuba” „Iberia”
1. Regions 1pts 1pts 2pts 1pts 3pts 2. Junctions/centers 3pts 2pts 2pts 1pts 3pts 3. Relations 1pts 1pts 2pts 1pts 3pts 4. Protected areas 1pts 1pts 1pts 1pts 2pts Total 6pts 5pts 7pts 4pts 11pts Qualitative comparison Table 8. Qualitative assessment of the spatial structure models.
Source: The author’s own calculations.
Note: 3 points – good quality of a component; 2 points – average quality of a component; 1 point – poor quality of a component
Order: Iberia, Visehrad, Poland, Baltica, Danuba.
20
Summary and conclusions. General conclusions:
– comparison of the models gives benefits because we objectivist assessments, – there are some difficulties in this comparison due to the ambiguity of the concept concerning „spatial structure model”.
Detailed conclusions:
a) for Poland – the accepted division into 6 regions is suitable for the analysis of the current state, but it does not correspond to the future needs, – the already shaped complexes of metropolises, situated in the central strip in the N-S direction, evoke further concentration, which hinders the development of the areas
– the system of infrastructural relations is not sufficient for handling with future needs. b) for „Baltica” – there is a need for the division into more regions, but some lacks in the potential hinder such a division, – the capital cities of metropolitan functions are the main driving force, – the arrangement of infrastructural relations may be sufficient in the future.
21
c) for „Visehrad” – the division applied is the consequence of the historical development and has evoked considerable spatial inequalities, – these inequalities are intensified by the majority of capital cities, which evoke underdevelopment of the other cities of metropolitan functions, – there are already shaped specific arrangements of infrastructural relations, which are currently intensively developed. d) for „Danuba” – the division applied is a temporary division; there is a need to modify it significantly, – the essential feature are not very well developed functions of metropolises, – the current system of infrastructural relations requires modification in adjusting to the current conditions. e) for „Iberia” – large territorial units require introducing additional division into smaller ones, – it also means the need to derive additional metropolises, – the developed system of infrastructural relations allows to satisfy the future needs from the increase of the number of regions and additional metropolitan centers.
22