DEFINING AND MEASURING WORLD-METRO REGIONS FOR INTERNATIONAL COMPARISONS

Transcription

1 DEFINING AND MEASURING WORLD-METRO REGIONS FOR INTERNATIONAL COMPARISONS Mario Piacentini, OECD 27th Scorus Conference, August 2010, Latvia

2 Why we need comparable measures of city performance Growing economic and demographic importance of metro-regions They host an increasing fraction of world population They are national growth leaders and innovation centers They are essential spatial nodes of trade, investment and migration Large potential for policy learning from cross-city comparisons Great diversity in urban growth patterns, within and across countries Increasing interest of policy makers in instruments for shaping the urbanization process (containment, networking policies) Still lots of uncertainties about complementarities among nearby cities. Existing global datasets lack an harmonized definition of metropolitan areas While they provide data for several relevant indicators, we do not know to what exactly these data refer to. Differences in definition of urban areas make huge differences in the derived statistics (see Freeman (2004) for a comparison of city productivity from different data providers )

3 How to define metro regions? Different Approaches The functional approach Administrative approach Morphological approach Functional approach The metro regions is usually defined on the basis of commuting flows between the core and the surrounding territories If properly defined, functional metro cover the whole urban area and are better suited for analysing: Economic and labor market flows Transport networks and Infrastructures Urban networks Core-periphery dynamics

4 Trade off between defining and measuring The OECD current methodology uses TL3 regions as building blocks for the metro functional area The definition is not very accurate but data are available for TL3 regions Using smaller building blocks guarantees better approximation of metro boundaries BUT the data sources for smaller building block are more limited

5 Methodology for the definition of metro-boundaries (Formulated and approved by the network of OECD countries in the Working Party on Territorial Indicators, WPTI) D E F I N I T I O N O F T H E C O R E Definition of the building blocks (i.e. municipalities) Choice of a population density threshold for the municipality to be part of the core (i.e inhabitants per sq. km). Good practice testing different thresholds Aggregation of contiguous core municipalities. D E F I N I T I O N O F T H E F U N C T I O N A L A R E A Identify all municipalities where at least 10% of the labour force commutes to work in the core. In case of 10% commuting to more cores, connect to the core receiving a higher population share. The functional area should contain no holes I D E N T I F I C A T I O N O F M U L T I - C O R E F U N C T I O N A L A R E A S Identify poly-centric or multi-core functional areas looking at relationships among cores. If a core send to another core more than 15% of its resident population, then the two cores are considered part of the same conurbation.

6 Preliminary sensitivity testing Methodology tested on 4 metro regions by the EC and Eurostat Milan, Paris, Munich, Barcelona 3 population density thresholds for the core: 750, 1000, 1500 inhabitants/sq.km 2 percentages of grid cells (1 sq. km) meeting the thresholds within each municipality 50%, 75% Further testing ongoing on national data to identify multi-core metro areas and study relationships in city networks (Italy, Spain, Portugal, France and Japan).

7 Functional definition of Milan, 750 habitants per km2 in 75% of the grids

8 Functional definition of Milan, 1000 habitants per km2 in 75% of the grids Core with 750 hab. km2

9 Functional definition of Paris, 750 habitants per km2 in 75% of the grids

10 Functional definition of Paris, 1000 habitants per km2 in 75% of the grids Core with 750 hab. km2

11 Polycentric cities are particularly challenging Barcellona, 750 hb km2, for 50% of the grids Barcellona, 750 hb km2, for 75% of the grids

12 Conclusions from the sensitivity tests: flexibility and testing are key! Density threshold in inhabitants per km % grids above density threshold 75% 75% 75% Barcelona 3,874,116 3,703,143 3,488,094 Milan 5,756,043 4,378,182 3,571,078 Munich 1,747,839 1,636,941 1,438,378 Paris 9,524,870 9,153,704 8,841,210 Population changes: base 1500 Barcelona Milan Munich Paris

13 Country-level analysis, the Italian case >770 hab km2 periphery; 10% commuting >770 hab km2 periphery; 15% commuting

14 Monocentric & polycentric cities Milan conurbation (with Bergamo, Como, Pavia ) The functional metropolitan area of Turin

15 Work in progress Methodology to be applied and further tested on other countries for which commuting data are available Define typologies of metropolitan areas by size, provide main statistics on them and by core/periphery Provide robust definitions of multi-core metropolis (commuting + accessibility (effective distance) criteria?) and city networks

16 Rationale for indicators Lack of appropriate data for worldwide comparison of functionally defined metropolitan regions Internationally comparable indicators can be used by metropolitan authorities and national policy makers (responsible for resource allocation) Policy framework for indicators is usually goal-based. Indicators are used for policy strategy development, monitoring of outcomes, strategy reformulation

17 Categories of indicators Relevant indicators already identified in several major projects (Urban Audit, Global City Indicators ). Four core areas: Demographics Competitiveness and Labor markets Social cohesion Environmental performance

18 A focus on environmental indicators, the less developed ones Core environmental indicators Percentage of land for agricultural use Total hectares of green areas Total residential electrical use per capita Proportion of commuters to total employees Share of employment outside the 10-mile circle from the metro area center point Sprawl index (based on ratios of population in high density and low density census tracts) Proportion of commuters using public transport Supplemental indicators Percentage of city s solid waste that is recycled Number of personal automobiles per capita Proportion of buses running on clean fuel Surface of restored brownfields Share of renewable energy in energy consumption Water consumption per capita Share of wastewater treated Density (km) of public transport network Air pollution Co2 Emissions (definition to be agreed on) Average temperature Patents in green technologies

19 Access to data Working at a low geographic level makes access to data more problematic Register data are a great resource with too great access restrictions Wider use of GIS/geo-referenced sources is key, especially for environmental indicators Easier access to data and geographical layers by National Institutions (NSO, Research Centers) needed

20 The way forward case studies We will undertake a comparative analysis of urban performance for a limited number of functionally defined metro regions: Large size candidates: London, Paris, Milan, Barcelona, Istanbul, Seoul, New York, Toronto, Tokyo, Beijing, Shanghai, Rio de Janeiro and Johannesburg Medium size candidates: Porto, Brno, Gothenburg, Antwerp, Bordeaux, Cagliari, Leeds and Manchester The list is still open

21 4 steps: The way forward, practical steps Finalizing the methodology and define shares for thresholds (as shown) Assess in depth differences in urban systems and networking cross-country (3 countries to be selected) and changes in urban structure across two census rounds (emergence of new cities, core-periphery relationships, compactness) Evaluating the availability of indicators for functional metro areas, define transparent methodologies to collate administrative data to functional areas. Provide key comparative statistics for metropolitan areas in our starting sample

22 .lots of interesting and useful work, so PARTNERS ARE WELCOME TO JOIN THE PROJECT contact: