Metropolitan Regions
Methoden, Erläuterungen und Klassifizierung
OECD Regional Statistics - Metropolitan Regions
Abstract
In assessing regional competitiveness policies, the OECD Territorial Development Policy Committee and its Working Parties on Territorial Indicators and Urban Policy, are increasingly called to compare the economic and social performances of Metropolitan Regions across member countries.
A Metropolitan Region typically consists of a core area with significant concentration of employment or population and a surrounding area densely populated and closely tied with the core.
Data sources used
The Metropolitan database contains data for 281 metro areas with a population of 500,000 or more over 30 OECD countries. These metro areas follow a harmonized functional definition developed by the OECD, in cooperation with the European Commission.
Further information read the publication:
http://www.oecd.org/regional/redefiningurbananewwaytomeasuremetropolitanareas.htm%5D.%20Additional%20information%20can%20be%20found%20in%20the%20following%20link:%20http://measuringurban.oecd.org/content/The%20OECD%20Metropolitan%20eXplorer.pdf
Reference period
The Regional Database contains annual data from 2000 to the most recent available year.
Methoden 
Erläuterungen 
Klassifizierungsplan ********************************************************************************
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* OECD STATISTICS *
* PARIS OECD Regional Statistics: Metropolitan Regions *
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STRUCTURE OF THE CODE : XXXXX / XX / XX
SUB-CODES :
1 XXXXX.... Country/Region (Territorial level 2)
2 .....XX.. Blank
3 .......XX Variable
SUB-CODE 1 : Country/Region (Territorial level 2)
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AUS Australia
AUS01 .Sydney
AUS02 .Melbourne
AUS03 .Brisbane
AUS04 .Perth
AUS05 .Adelaide
AUS06 .Gold Coast-Tweed Heads
AUT Austria
AT001 .Vienna
AT002 .Graz
AT003 .Linz
BEL Belgium
BE001 .Brussels
BE002 .Antwerp
BE003 .Ghent
BE005 .Liege
CAN Canada
CAN01 .Edmonton
CAN04 .Calgary
CAN06 .Winnipeg
CAN09 .Vancouver
CAN16 .Quebec
CAN20 .Montreal
CAN21 .Ottawa-Gatineau
CAN26 .Toronto
CAN29 .Hamilton
CHE Switzerland
CH001 .Zurich
CH002 .Geneva
CH003 .Basel
CHL Chile
CL010 .Valparaφso
CL011 .Santiago
CL020 .Concepci≤n
CZE Czech Republic
CZ001 .Prague
CZ002 .Brno
CZ003 .Ostrava
DEU Germany
DE001 .Berlin
DE002 .Hamburg
DE003 .Munich
DE004 .Cologne
DE005 .Frankfurt
DE006 .Essen
DE007 .Stuttgart
DE008 .Leipzig
DE009 .Dresden
DE010 .Dortmund
DE011 .Dⁿsseldorf
DE012 .Bremen
DE013 .Hanover
DE014 .Nuremberg
DE015 .Bochum
DE027 .Freiburg im Breisgau
DE033 .Augsburg
DE034 .Bonn
DE035 .Karlsruhe
DE040 .Saarbrⁿcken
DE501 .Duisburg
DE502 .Mannheim
DE504 .Mⁿnster
DE507 .Aachen
DNK Denmark
DK001 .Copenhagen
EST Estonia
EE001 .Tallinn
ESP Spain
ES001 .Madrid
ES002 .Barcelona
ES003 .Valencia
ES004 .Seville
ES005 .Zaragoza
ES006 .Mßlaga
ES008 .Las Palmas
ES019 .Bilbao
FIN Finland
FI001 .Helsinki
FRA France
FR001 .Paris
FR003 .Lyon
FR004 .Toulouse
FR006 .Strasbourg
FR007 .Bordeaux
FR008 .Nantes
FR009 .Lille
FR010 .Montpellier
FR011 .Saint-╔tienne
FR013 .Rennes
FR026 .Grenoble
FR032 .Toulon
FR203 .Marseille
FR205 .Nice
FR215 .Rouen
GRC Greece
GR001 .Athens
GR002 .Thessalonica
HUN Hungary
HU001 .Budapest
IRL Ireland
IE001 .Dublin
ITA Italy
IT001 .Rome
IT002 .Milan
IT003 .Naples
IT004 .Turin
IT005 .Palermo
IT006 .Genova
IT007 .Florence
IT008 .Bari
IT009 .Bologna
IT010 .Catania
IT011 .Venice
JPN Japan
JP003 .Sapporo
JP013 .Sendai
JP015 .Niigata
JP020 .Toyama
JP021 .Nagano
JP023 .Kanazawa
JP024 .Utsunomiya
JP025 .Maebashi
JP026 .Mito
JP030 .Tokyo
JP031 .Kofu
JP034 .Nagoya
JP036 .Numazu
JP038 .Osaka
JP039 .Shizuoka
JP040 .Anjo
JP042 .Yokkaichi
JP046 .Himeji
JP047 .Toyohashi
JP048 .Hamamatsu
JP050 .Okayama
JP051 .Kurashiki
JP052 .Fukuyama
JP053 .Hiroshima
JP054 .Takamatsu
JP055 .Wakayama
JP059 .Tokushima
JP064 .Kitakyushu
JP065 .Matsuyama
JP066 .Fukuoka
JP067 .Kochi
JP071 .Oita
JP074 .Kumamoto
JP075 .Nagasaki
JP077 .Kagoshima
JP078 .Naha
KOR Korea
KR004 .Seoul Incheon
KR015 .Cheongju
KR018 .Daejeon
KR022 .Pohang
KR025 .Daegu
KR026 .Jeonju
KR029 .Ulsan
KR032 .Busan
KR033 .Changwon
KR035 .Gwangju
MEX Mexico
MEX01 .Mexicali
MEX02 .Tijuana
MEX05 .Jußrez
MEX08 .Hermosillo
MEX10 .Chihuahua
MEX16 .Reynosa
MEX19 .Monterrey
MEX20 .Torre≤n
MEX21 .Saltillo
MEX22 .Culiacßn
MEX24 .Durango
MEX28 .Tampico
MEX29 .San Luis Potosφ
MEX31 .Aguascalientes
MEX33 .Benito Jußrez
MEX34 .Le≤n
MEX35 .MΘrida
MEX37 .Guadalajara
MEX38 .Irapuato
MEX40 .QuerΘtaro
MEX42 .Celaya
MEX46 .Pachuca de Soto
MEX50 .Morelia
MEX51 .Mexico City
MEX52 .Xalapa
MEX55 .Toluca
MEX59 .Veracruz
MEX60 .Puebla
MEX61 .Cuernavaca
MEX70 .Centro
MEX73 .Oaxaca de Jußrez
MEX74 .Acapulco de Jußrez
MEX75 .Tuxtla GutiΘrrez
NLD Netherlands
NL001 .The Hague
NL002 .Amsterdam
NL003 .Rotterdam
NL004 .Utrecht
NL005 .Eindhoven
NOR Norway
NO001 .Oslo
POL Poland
PL001 .Warsaw
PL002 .L≤dz
PL003 .Krak≤w
PL004 .Wroclaw
PL005 .Poznan
PL006 .Gdansk
PL009 .Lublin
PL010 .Katowice
PRT Portugal
PT001 .Lisbon
PT002 .Porto
SWE Sweden
SE001 .Stockholm
SE002 .Gothenburg
SE003 .Malm÷
SVN Slovenia
SI001 .Ljubljana
SVK Slovak Republic
SK001 .Bratislava
GBR United Kingdom
UK001 .London
UK002 .Birmingham (UK)
UK003 .Leeds
UK004 .Bradford
UK005 .Liverpool
UK006 .Manchester
UK007 .Cardiff
UK008 .Sheffield
UK009 .Bristol
UK010 .Newcastle
UK011 .Leicester
UK017 .Portsmouth
UK023 .Nottingham
UK097 .Glasgow
UK098 .Edinburgh
USA United States
US003 .Seattle
US012 .Portland
US014 .Minneapolis
US033 .Milwaukee
US035 .Madison
US038 .Buffalo
US039 .Grand Rapids
US044 .Albany
US045 .Detroit
US048 .Boston
US055 .Chicago
US060 .Providence
US065 .Toledo (US)
US069 .Cleveland
US070 .Des Moines
US077 .Omaha
US081 .Akron
US084 .New York
US089 .Salt Lake City
US097 .Pittsburgh
US103 .Harrisburg
US106 .Philadelphia
US107 .Columbus
US114 .Denver
US115 .Indianapolis
US117 .Dayton
US122 .Baltimore
US124 .Cincinnati
US125 .Washington
US128 .Kansas City
US133 .Colorado Springs
US134 .Saint Louis (US)
US135 .Sacramento/Roseville
US141 .Louisville
US146 .San Francisco
US147 .Wichita
US149 .Richmond
US154 .Norfolk-Portsmouth-Chesapeake-Virginia beach
US155 .Fresno
US159 .Las Vegas
US160 .Nashville
US161 .Tulsa
US170 .Raleigh
US174 .Oklahoma city
US178 .Charlotte
US180 .Albuquerque
US181 .Memphis
US186 .Little Rock
US190 .Los Angeles
US195 .Columbia
US196 .Atlanta
US202 .Phoenix
US205 .Birmingham (US)
US209 .Dallas
US210 .San Diego
US212 .Fort Worth
US213 .Charleston
US223 .Tucson
US227 .El Paso
US233 .Baton Rouge
US234 .Austin
US237 .Jacksonville
US241 .New Orleans
US242 .Houston
US245 .San Antonio
US250 .Orlando
US251 .Clearwater/Saint Petersburg
US252 .Tampa
US259 .Miami
US261 .Mcallen
SUB-Code 3 : Variable
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Demographic indicators
00 Total population of the metropolitan area (persons)
01 .Population of the city area (persons)
02 ..Population, City, Youth (0-14)
03 ..Population, City, Working age (15-64)
04 ..Populaiton, City, Old (65more)
05 .Population of the commuting zone area (persons)
06 ..Population, Commuting Zone, Youth (0-14)
07 ..Population, Commuting Zone, Working age (15-64)
08 ..Populaiton, Commuting Zone, Elder (65more)
09 .Population, Total, Youth (0-14)
10 .Population, Total, Working age (15-64)
11 .Populaiton, Total, Old (65more)
12 .Old-age-dependency ratio
13 .Youth-dependency ratio
14 Population of the metropolitan area as a share of national value (%)
15 .Youth population of the metropolitan area as share of national value (%)
16 .Working age population of the metropolitan area as a share of national value (%)
17 .Elderly population of the metropolitan area as a share of national value (%)
18 .Youth population of the core area as a share of the total metropolitan area youth population (%)
19 .Working age population of the core area as a share of the total metropolitan area working age population (%)
20 .Elderly population of the core area as a share of the total metropolitan area elderly population (%)
21 Population density (persons per km2)
22 .Population density of the city area (persons per km2)
23 .Population density of the commuting zone (persons per km2)
Land cover indicators
24 Total land area (km2)
25 .Total land area of the city (km2)
26 .Total land area of the commuting zone (km2)
27 Metropolitan land share of national value (%)
28 .City land share over Metropolitan land area (%)
29 .Commuting zone land share over Metropolitan land area (%)
30 Urbanised area (km2)
31 Urbanised area growth
32 Urbanised area share (%),,
33 Green area per million people (square meters per million person),,
Urban form
34 Polycentricity
35 Concentration of population in the core (%)
36 Sprawl index
Territorial organisation
37 Local governments (count)
38 Local governments in the core (count)
39 Territorial fragmentation
40 Average population size of local government
Economic indicators
41 GDP (millions US$)
42 GDP of the metropolitan area as a share of national value (%)
43 Labour productivity
44 GDP per capita (US$)
Income and inequality
45 Equivalised household disposable income
46 Gini index
47 Spatial ordinal entropy index at a 1,000 meters scale
Environmental indicators
48 CO2 emissions per capita (tonnes per inhabitant)
49 .CO2 emissions per capita from transport (tonnes per inhabitant)
50 .CO2 emissions per capita from energy industry (tonnes per inhabitant)
51 CO2 emissions of the metropolitan area as a share of national value (%)
52 .CO2 energy emissions of the metropolitan area as a share of the national energy industry emissions (%)
53 .CO2 transport emissions of the metropolitan area as a share of the national transport emissions from transport (%)
54 Estimated average exposure to air pollution (PM2.5) based on imagery data
Labour indicators
55 Labour force (persons)
56 .Labour force of the metropolitan area as a share of national value (%)
57 Employment (persons)
58 .Employment of the metropolitan area as a share of national value (%)
59 Employment as a share of the working age population (%)
60 Unemployment (persons)
61 .Unemployment of the metropolitan area as a share of national value (%)
62 Unemployment as a share of the labour force (%)
63 Participation rate (%)
Innovation indicators
64 PCT patent applications (count)
65 PCT patent applications of the metropolitan area as % of national value
66 PCT patents applications per 10,000 inhabitants
Units:
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Units of measure refer to persons for Population by age and sex,
Labour Force, Employment, Unemployment and Annual Average
Population. Regional surface is expressed in square metres,
while population density refers to persons per square metre.
GDP in current and constant prices is expressed in million of
national currency, while GDP in PPP is expressed in million
of Dollars. Unemployment, Participation and Employment rates
are expressed as percentages. Patents statistics are
expressed as per million population.