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CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES 's-Hertogenbosch, 20 April 2018 Paolo Veneri Head of Territorial Analysis and Statistics Unit RegionalDevelopment Policy Division Centre for Entrepreneurship, SMEs, Regions and Cities Data, definitions and tools to support policy analysis: an international perspective
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Page 1: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

CITIES4PEOPLE:

FROM SMART TO INTELLIGENT CITIES

's-Hertogenbosch, 20 April 2018

Paolo Veneri

Head of Territorial Analysis and Statistics Unit Regional Development Policy Division Centre for Entrepreneurship, SMEs, Regions and Cities

Data, definitions and tools to

support policy analysis: an

international perspective

Page 2: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

2

1. The EC-OECD city definition

2. The OECD Metropolitan databases

3. Evidence on OECD cities

OUTLINE

Page 3: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Many cities do not match their respective administrative boundaries

Source: OECD calculations based on population density disaggregated with Corine Land Cover.

Page 4: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Why do we need a harmonised definition of cities?

• To make sound comparisons of city indicators (i.e. SDG 11)

• To answer questions such as:

– How many cities are there in a specific country?

– Is Istanbul bigger than Paris?

– Is a specific city growing or shrinking?

– Is the growth of cities occurring in the centres or in the suburban areas (i.e. commuting zones)?

• To improve urban investments and development strategies

Page 5: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

The concept of functional urban area (FUA)

Urban centre

Commuting zone

Functional urban area

How to define functional urban areas: 1. Identification of densely inhabited and large places (urban centres or cores).

2. Definition of the commuting zone (hinterland) linked by commuting flows to the

city centre.

3. The sum of urban centre and surrounding commuting zone is the functional urban area

Page 6: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

A map of French FUAs

• In France our method allows us to identify 83 FUAs

• Total population in

2011 ranges from 85,000 to 11.7 million (Paris)

• 65% of French population live in FUAs (Paris represents 19%)

Page 7: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Where do population growth in FUAs? The most common trend is suburbanisation, but exceptions are observed

Population growth rate in city core and in their commuting zones (2000-11)

Page 8: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Metropolitan database

(for those FUAs above 500 000 inhabitants)

- Population (level and growth) - Population density - Population by age

- Total Area - Urbanised area (share &

change) - Polycentricity - Concentration of population in

core areas - Sprawl index

- Local units - Local units in core area - Territorial fragmentation

- GDP per capita - GDP per worker - Disposable income - Gini index of disposable income - Patents application - Employment dynamics (Orbis)

- Employment (level and change) - Employment rate - Labour force (level and change) - Unempl. (level and change) - Unempl. rate - Participation rate - Income segregation

- Air pollution - CO2 emissions per capita - Co2 emissions from transport

and energy sector

Demographic Urban form Territorial organisation

Labour market/Social Environmental Economic and innovation

Page 9: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

9

Cities represent an important part of GDP

and employment

Source: Regions at a Glance, 2016

281 metro areas in 30 countries concentrate 49% of population and generate 57% of GDP and 51% of employment

Per cent of population, GDP and employment in OECD metropolitan areas

Page 10: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

10 Source: Brezzi and Sanchez-Serra (2014)

Air quality in cities: PM2.5 exposure in 2013

Page 11: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Where to find the metropolitan database?

- OECD.Stat http://stats.oecd.org/Index.aspx?Datasetcode=CITIES

- Metropolitan explorer http://measuringurban.oecd.org/

Page 12: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

On average, household incomes are 18% higher in

metropolitan areas than elsewhere

12

Metropolitan vs. non metropolitan household disposable income ratio by country

per equivalent household; 2014 or latest available year

Source: OECD (2016) Making cities work for all; OECD Publishing, Paris.

Page 13: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

But in many countries cities are also more

unequal

13

Gini coefficients for household income in metropolitan areas, circa 2014

Metropolitan areas with minimum and maximum Gini coefficients, by country

Calgary Miami

Tux tla Gutiérrez

Brussels

Santiago

Paris

BariMalmö

GrazCopenhagen

Oslo

Québec

Albany

Rey nosa

Gent

Concepción

Saint-Etienne

Catania GöteborgLinz

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

CAN (11) USA (70) MEX (33) BEL (4) CHL (3) FRA (15) ITA (11) SWE (3) AUT (3) DNK (1) NOR (1)

Gin

i coeffi

cie

nt

for

hous

ehol

d d

isposa

ble

inco

me

Country (No. of metropolitan areas)

Maximum Minimum Country average

Page 14: CITIES4PEOPLE: FROM SMART TO INTELLIGENT CITIES · 2 1. The EC-OECD city definition 2. The OECD Metropolitan databases 3. Evidence on OECD cities OUTLINE

Regions at a Glance

• Publication (English): http://www.oecd.org/regional/oecd-regions-at-a-glance-19990057.htm

• Databases:

Metropolitan Areas Database http://stats.oecd.org/Index.aspx?Datasetcode=CITIES

Regional Database http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR

• Data Visualisation:

Metropolitan explorer http://measuringurban.oecd.org

Regional well-being: http://www.oecdregionalwellbeing.org

Making cities work for all

• Full publication (English): https://www.oecd.org/gov/making-cities-work-for-all-9789264263260-en.htm

• Policy Highlights (available in English, French and Spanish): http://www.oecd.org/gov/making-cities-work-for-all-policy-brief-en.pdf

Well-being in Danish cities

• Publication (English): http://www.oecd.org/fr/publications/well-being-in-danish-cities-

9789264265240-en.htm

Thank you! [email protected]


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