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Version: June 2016

http://measuringurban.oecd.org/

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The Metropolitan eXplorer offers an interactive visualisation of the 281 OECD metropolitan areas identified in 30 OECD countries1, the functional urban area of Luxembourg2 and the 8 Colombian metropolitan areas3.

Comparable values and rankings of population, GDP, employment, quality of air and many other indicators can be displayed through different visual techniques developed by NComVA for the OECD.

Explore the visualisation

http://measuringurban.oecd.org

Give your feedback

[email protected]

1 The OECD-EU definition of functional urban areas (FUA) has not been applied to Iceland, Israel, New

Zealand and Turkey. 2 Luxembourg has been included in the Metropolitan eXplorer despite the fact that it has a population below

500 000 inhabitants. 3 In 2016, the OECD-EU methodology for Functional Urban Areas (FUA) has been applied to Colombia using

municipal administrative boundaries and population grid data based on the 2005 population census.

http://measuringurban.oecd.org/

[email protected]

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Table of Contents

Introduction ............................................................................................................................................ 4

I. Different techniques to visualise the data ........................................................................................... 6

II. Indicators and years available ............................................................................................................. 8

III. Data sources and estimation techniques to compile the indicators ............................................... 13

1. Socio-economic statistics .............................................................................................................. 13

2. Environmental statistics ................................................................................................................ 14

3. Innovation statistics ...................................................................................................................... 15

4. Urban form .................................................................................................................................... 16

5. Territorial organisation ................................................................................................................. 16

6. Data sources .................................................................................................................................. 16

IV. References ....................................................................................................................................... 19

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Introduction

The OECD, in cooperation with the EU, has developed a harmonised definition of urban areas which overcomes previous limitations linked to administrative definitions (OECD, 2012). According to this definition an urban area is a functional economic unit characterised by densely inhabited “city core” and “commuting zone” whose labour market is highly integrated with the core.

1) City cores are defined through gridded population data. The geographic building blocks to define functional urban areas are the municipalities (LAU2 in Eurostat terminology and the smaller administrative units for which national commuting data are available in non-European countries).

The population grid data for European countries comes from the Corine Land Cover dataset, produced by the Joint Research Centre for the European Environmental Agency (EEA). For all the non-European countries, gridded population data comes from the Landscan project.

A “city core” consists of a high-density cluster of contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2, a lower threshold of 1,000 people for km2 is applied to Australia, Canada and US. Small clusters (hosting less than 50,000 people in Australia, Canada, Chile, Colombia, Europe and the United States, 100,000 people in Japan, Korea and in Mexico) are dropped.

A municipality is defined as being part of a urban core if at least 50% of the population of the municipality lives within the urban cluster. If more than 15% of employed persons living in one city core work in another city core, these two city cores are combined into a single destination (to take into account policentricity).

2) Commuting zones are defined as all municipalities with at least 15% of their employed residents working in a certain city core. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

This methodology makes it possible to compare functional urban areas of similar size across countries. A classification of functional urban areas into four types according to population size is proposed:

• Small urban areas, with a population below 200 000 people;

• Medium-sized urban areas, with a population between 200 000 and 500 000;

• Metropolitan areas, with a population between 500 000 and 1.5 million;

• Large metropolitan areas, with a population of 1.5 million or more.

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The Metropolitan eXplorer presents socio-economic data of the large two categories of functional urban areas: Metropolitan areas and Large metropolitan areas, in other words, metropolitan areas with a population of 500 000 or more.

This Guide describes the different indicators, sources and visual techniques available in the Metropolitan eXplorer web tool.

For further information on metropolitan areas, read the publication OECD Regions at a

Glance, available on June 16, 2016 at:

http://www.oecd.org/gov/regions-at-a-glance.htm

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I. Different techniques to visualise the data

The Metropolitan eXplorer4 displays the indicators through three different linked

dynamic views: a map, a histogram and a scatterplot. Once the indicator has been selected,

it appears both on the map and on the histogram. The size of classes in a map can be

adjusted by clicking on the values of the classes in the legend. One or more cities can be

selected on the map (or on the histogram) by clicking on the city.

City cores and commuting zones are identified respectively on the map in dark/light

grey. City cores and commuting zones can be highlighted by clicking on the metropolitan

area border. Administrative boundaries of regions can be highlighted, appreciating the

difference between the administrative and economic boundaries of the metropolitan areas.

Select View and choose Show/Hide administrative boundaries and Show/Hide Metro

Boundaries

Size and colours can be customized through Settings. Colours can be associated

either to the intensity of the indicator chosen, or to countries (Select View, Color).

4 Disclaimer: maps are for illustrative purposes and are without prejudice to the status of or sovereignty

over any territory covered by these maps.

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Select the Metro Profiles to have a summary view of the performance of a single

metropolitan area compared to the national value as well as to the other metropolitan

areas.

The scatterplot allow the visualisation of three variables for each metropolitan area

at the same time. You can choose the variables to be plotted on the X-axis, on the Y-axis and

on the size of the bubbles.

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II. Indicators and years available The following indicators are available in the Metropolitan eXplorer for all the metropolitan areas as well as for the national averages.

Group Indicator Description Year

Population

Population (persons)

Population by municipality for the years 2001 and

2011 comes from the Population Census. The

population by municipality from the Census 2011 is

then recomputed according to the metropolitan

boundaries of 2001. The metropolitan population

between the years 2001 and 2011 is estimated.

2014

Population share of

national value (%)

Share of the metropolitan population over the national

value. 2014

Population growth

(%)

Annual average population growth over the period

2000-12. 2000-14

Population density

(persons per km2)

Ratio between total population and total land area. 2014

Population of the

city core (persons)







2014

Population of the

commuting zone

(persons)







2014

Population

by age

Youth population

Population between 0-14 years old by municipality for

the years 2001 and 2011 comes from the Population

Census. The population by municipality from the

Census 2011 is then recomputed according to the

metropolitan boundaries of 2001. The metropolitan

population between the years 2001 and 2011 is

estimated.

2014

Working age

population

Population between 15-64 years old by municipality

for the years 2001 and 2011 comes from the

Population Census. The population by municipality

from the Census 2011 is then recomputed according to

the metropolitan boundaries of 2001. The metropolitan


estimated.

2014

Old population

Population by above 64 years old by municipality for

the years 2001 and 2011 comes from the Population

Census. The population by municipality from the

Census 2011 is then recomputed according to the

metropolitan boundaries of 2001. The metropolitan


estimated.

2014

Old-age-dependency

ratio

Ratio between the elderly population (65+ years) over

the working age population (15-64 years old) 2014

Youth-dependency

ratio

Ratio between the youth population (0-14 years old)

over the working age population (15-64 years old) 2014

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Area

Total land area

(km2)

Total land area of the metropolitan area. 2014

Land share of

national value (%)

Share of the metropolitan land area over the national

value. 2014

Urbanised area

(km2)

The urbanised area is defined as the land area covered

by buildings or infrastructure for urban use. It

includes, for example, residential and non-residential

buildings, major roads, railways, and sport facilities.

2006

Urban area share

(%)

Share of the urbanised area over total land of a

metropolitan area. 2006

Urban area growth

(%)

Annual average growth of the urbanised area over the

period 2000-06.

2000-06 (except for Japan [1997-

2006], USA [2001-06])

Green area per

capita (m2)

Land in the metropolitan area covered by vegetation,

forest and parks in 2000 (source: MODIS MCD12Q1),

divided by the population of the metropolitan area.

2014

GDP

GDP (millions US$)

Estimates of GDP of metropolitan areas, expressed in

millions of US$, constant prices and constant PPPs,

OECD base year (2010). The estimates are derived

from the values of TL3 regions (except for Australia,

Canada, Chile and Mexico (TL2) and the United

States (Bureau of Economic Accounts)).

2013 (except for Austria,

Colombia, Germany, Estonia,

Finland, France, Hungary,

Ireland, Italy, Japan, Norway,

Poland, Spain, Sweden and

Switzerland [2012])

GDP growth (%) Annual average GDP growth over the period 2000-13.

2000-13 (except for Austria

[2000-12], Colombia [2000-12],

Germany [2000-12], Estonia

[2000-12], Finland [2000-12],

France [2000-12], Hungary

[2000-12], Ireland [2000-12],

Italy [2000-12], Japan [2001-12],

Mexico [2003-13], Norway

[2008-12], Poland [2000-12],

Spain [2000-12], Sweden [2000-

12], Switzerland [2008-12] and

United States [2001-13]).

GDP share of

national value (%) Share of metropolitan area GDP over national GDP






Switzerland [2012]).

GDP per capita

(US$)

GDP per capita expressed in US$, constant prices and

constant PPPs, OECD base year (2010).






Switzerland [2012]).

GDP per capita

growth (%)

Annual average GDP per capita growth over the

period 2000-13.


[2000-12], Colombia [2000-12],


[2000-12], Finland [2000-12],


[2000-12], Ireland [2000-12],

Italy [2000-12], Japan [2001-12],


[2008-12], Poland [2000-12],

Spain [2000-12], Sweden [2000-



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GDP

Labour productivity

(US$)

GDP per employee expressed in US$, constant prices

and constant PPPs, OECD base year (2010).






Switzerland [2012] and Slovenia

[2001]).

Labour productivity

growth (%)

Annual average GDP per employee growth over the

period 2000-13.


[2000-12], Colombia [2000-12],


[2000-12], Finland [2000-12],


[2000-12], Ireland [2000-12],

Italy [2000-12], Japan [2001-12],


[2008-12], Poland [2000-12],

Spain [2000-12], Sweden [2000-



Environment

CO2 emissions per

capita (level)

Estimates of CO2 emissions (expressed in tons) in

metropolitan areas divided by population. The values

are disaggregated from the corresponding national

values.

2008

CO2 emissions share

from energy industry

(%)

Share of CO2 emissions from the energy industry over

the total metropolitan CO2 emissions. 2008

CO2 emissions share

from transport (%)

Share of CO2 emissions from transport (road and non-

road ground transport) over total metropolitan CO2

emissions.

2008

CO2 emissions per

capita from energy

industry (level)

Ratio between estimated CO2 energy emissions and

total population in a metropolitan area. 2008

CO2 emissions per

capita from

transport (level)

Ratio between estimated CO2 transport emissions and

total population in a metropolitan area. 2008

Air pollution (µg/m³) Estimated population exposure to air pollution PM2.5

expressed in µg/m³, three year average 2012-14. 2013

Labour

market

Employment (level)

Estimated total employment in a metropolitan area.

The estimates are derived from the TL3 regional

values except for Poland, Mexico, Chile and Colombia

(TL2), Canada (NOG). Metropolitan figures for the

United States and Australia are provided by the U.S.

Bureau of Labour Statistics and Australian Bureau of

Statistics respectively.


Australia, Czech Republic and


[2011]).

Employment share

of national value

(%)

Share of metropolitan unemployment over the national

value.




[2011]).

Employment growth

(%)

Annual average employment growth over the period

2000-14.

2000-14 (except for Australia,

Austria and Czech Republic

[2000-13], Denmark [2007-14],

Germany and Colombia [2001-

14], Switzerland [2010-13],

Slovenia [2001-11]).

Unemployment

(level) Estimated total unemployment in a metropolitan area.




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Labour

market

values except for Poland, Mexico, Chile and Colombia

(TL2), Canada (NOG). Metropolitan figures for the

United States and Australia are provided by the U.S.

Bureau of Labour Statistics and Australian Bureau of

Statistics respectively.


[2011]).

Unemployment

share of national

value (%)

Share of metropolitan unemployment over the national

value.




[2011]).

Unemployment

growth (%)

Annual average unemployment growth over the period

2000-10.



[2000-13], Denmark [2007-14],




Labour force (level)

Estimated total labour force in a metropolitan area.


values except for Poland, Mexico, Chile (TL2),

Canada (NOG). Metropolitan figures for the United

States and Australia are provided by the U.S. Bureau

of Labour Statistics and Australian Bureau of Statistics

respectively.




[2011]).

Labour force share

of national value

(%)

Share of the metropolitan labour force over the

national value.




[2011]).

Labour force growth

(%)

Annual average labour force growth over the period

2000-10.



[2000-13], Denmark [2007-14],




Patents

PCT patent

applications (count)

PCT patent applications (fractional count; by inventor

city and priority year). 2013

PCT patent

applications of the

metropolitan area as

% of national value

(%)

PCT patent applications of the metropolitan area as %

of national value. 2013

PCT patent

applications annual

average growth (%)

Annual average PCT applications growth over the

period 2000-08. 2000-13

Patent intensity

(level)

PCT patent applications per 10,000 inhabitants

(fractional count; by inventor city and priority year). 2013

Urban form

Polycentricity

(count)

Number of non contiguous core areas by metro area. 2014

Concentration of

population (%)

Share of population living in the core areas over the total metropolitan population. 2014

Sprawl index (%)

The sprawl index (SI) measures the growth in built-up area adjusted for the growth in city population. When the city population changes, the index measures the increase in the built-up area relative to a benchmark where the built-up area would have increased in line with population growth. The SI index is equal to zero when both population and built-up area are stable over time. It is bigger (lower) than zero when the

2000-06 (except for Japan [1997-

2006], USA [2001-06])

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growth of built-up area is greater (smaller) than the growth of population, i.e. the city density has decreased (increased).

Territorial

organisation

Local governments

(count)

Number of local governments in a metropolitan area. Only the lowest tier of government is considered and only general- purposes governments.

2014

Local governments

in the core (count)

Number of local governments in the core areas of the metropolitan area. 2014

Territorial

fragmentation (level)

Number of local governments per 100,000 inhabitants of the metropolitan area. 2014

Average population

size of municipalities

(persons)

Ratio between population and number of local governments in a metropolitan area.

2014

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III. Data sources and estimation techniques to compile the indicators

1. Socio-economic statistics

Social, economic and labour statistics at sub-national level (such as GDP and Labour)

which are comparable across countries are generally available for administrative regions

(TL2 and TL3 regions of the OECD Regional database). While a set of indicators may in the

future become available for the OECD functional urban areas, at present we suggest to

derive estimates of the main economic indicators by adjusting existing regional data to the

non-administrative boundaries.

GIS techniques are increasingly adopted in the literature, especially in the field of

environmental indicators and other issues that are particularly attached to the geography of

the territory, rather than their functional or political organisation (Nordhaus et al., 2006;

Milego and Ramos, 2006; Doll et al., 2000). On the basis of the methods that have been

used in the literature to adjust indicators at small-scale geography, the OECD has decided to

make use of Geographic Information System (GIS) tools to disaggregate socio-economic

data. The methodology is similar to that applied by Milego and Ramos (2006) to downscale

socio-economic data from European administrative regions to a 1 km² regular grid level

within the context of an Espon research (European Observation Network for Territorial

Development).

The proposed methodology uses the socio-economic values (GDP, employment and unemployment) in TL3 regions as data inputs and the distribution of population based on census data. The methodology to adjust socio-economic data to metropolitan areas has evolved from the use of raster population data (i.e. Landscan) to municipal population census data as the input data source. This change has allowed the use of more up-to-date data (census data c.a. 2011) as well as the use of harmonised municipal boundaries over time. Indeed, long time-series have been generated using consistent boundaries of municipalities between the two census data points by using GIS techniques.

The suggested methodology is composed of three main steps:

Intersect the municipal boundaries with the TL3 boundaries by the use of GIS techniques;

Attribute each municipality a GDP value by weighting for the population in each municipality; and

Calculate the sum of municipalities’ GDP values belonging to each metro area.

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An improved method would be to use employment data rather than population data in step 2. For example, the United Kingdom Office for National Statistics provides income estimates at ward level down-scaling the regional values through various variables including household size, employment status, proportion of the ward population claiming social benefits, and proportion of tax payers in each of the tax bands, etc. A similar method is used by the U.S. Bureau of Economic Analysis to estimate the GDP for U.S. Metropolitan Statistical Areas. The Federal Statistical Office of Switzerland used CLC-Data-Classes urban continuous fabric, urban discontinuous fabric and industrial or commercial units for all neighbouring countries by calibrating with other data to estimate data for jobs in grid cells. However these types of data input are not available in most OECD countries therefore a simpler solution was adopted.

A similar technique is applied to estimate employment and unemployment in metropolitan areas with working age population (15-65 years old) used as data input in step 2.

It has to be noted that the estimates of GDP, employment and unemployment in the metropolitan areas do not adhere to international standards; the comparability among countries relies on the use of the same methodology applied to areas defined with the same criteria.

2. Environmental statistics

The development of statistics on the state and changes of local environmental assets is

a challenging task. While countries have started to invest more resources in the monitoring

of key environmental variables, such as carbon emissions, data are rarely collected and

analysed at the sub-national level. This is problematic given that national averages hide

great geographic differences in contributions to natural resources depletions and exposure

to environmental risks. Based on the need of providing environmental statistics at local

level, the OCDE has developed some methodologies to derive environmental indicators at

different territorial levels (Piacentini and Rosina, 2012).

Land cover indicators are based on remotely sensed source data at different levels of

resolution, and obtained through overlay analysis (superimposing the source data layer with

the layer of the territorial boundaries of the Functional urban areas). In this case, the

functional urban value is the sum (land cover) of the values observed in the source data

within the area of interest.

CO2 emissions are obtained through data that are available at the national level, but

have been downscaled to regularly spaced “grids” (i.e. 1 km2) through a model that uses

information of observable elements that are correlated with the production of emissions,

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such as population density, roads and factories that capture how the phenomenon of

interest is distributed across space.

CO2 emissions by sector derive the CO2 emission produced by the energy industry and

by transportation; the latter include ground transportation (road and non-road) and exclude

aviation and shipping.

The estimated average exposure to air pollution (PM2.5) is based on GIS-based methodology at metropolitan level using the satellite-based PM2.5 estimates of van Donkelaar, Martin, Brauer and Boys (2014) at 0.1o x 0.1o geographic grid resolution (Brezzi and Sanchez-Serra, 2014 and 2016). The method used to produce the estimates is the following:

The satellite-based of air pollution at 1 km2 are multiplied by the population living in that area (using a 1km2 resolution population grid);

The exposure to air pollution in a region (or a metropolitan area) is given by the sum of the population weighted values of PM2.5 in the 1 km2 grid cells falling within the boundaries of the region (metropolitan area); and

Finally, the average exposure to PM2.5 concentration in a region is given by dividing this aggregated value by the total population in the region.

This indicator is derived from global satellite observations of PM concentration. It has the advantage of being computable globally without requiring country capacity investments in data collection.

3. Innovation statistics

Patents are worldwide recognised as a useful measure of the generation of ideas and

they have actually contributed to the historical study of invention. Every PCT5 patent count

in the OECD Patent Database includes all inventor names, each inventor’s address and

detailed information about the patent’s technology, among others. Using the geographical

location of the inventive process, PCT patent counts are assigned to metropolitan areas

according to the inventor’s address. Every inventor’s hometown is matched to a zip-code

area, which is then assigned to a Metropolitan Area. Therefore, the metropolitan

distribution of patent applications is identified on the basis of the inventor’s zip-code of

residence. If one application has more than one inventor, the application is divided equally

5 . The Patent Cooperation Treaty (PCT) is an international patent law treaty, concluded in 1970. It

provides a unified procedure for filing patent applications to protect inventions in each of its contracting states.

A patent application filed under the PCT is called an international application, or PCT application.

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among all of them and subsequently among their zip-codes of residence, thus avoiding

double counting. 6

4. Urban form

The following variables were compiled to measure urban form:

The polycentricity is given by the number of city cores included in a metropolitan area.

The concentration of population in the city core is the share of metropolitan population that

lives in the urban cores of the metropolitan area. It provides a measure of the relative

importance of urban cores versus hinterlands.

The sprawl index (SI) measures the growth in built-up area adjusted for the growth in city

population. When the city population changes, the index measures the increase in the built-

up area relative to a benchmark where the built-up area would have increased in line with

population growth. The SI index is equal to zero when both population and built-up area are

stable over time. It is bigger (lower) than zero when the growth of built-up area is greater

(smaller) than the growth of population, i.e. the city density has decreased (increased).

The sprawl index is defined as

𝑆𝐼𝑖 =

[𝑢𝑟𝑏𝑖,𝑡+𝑛 − ((𝑢𝑟𝑏𝑖,𝑡𝑝𝑜𝑝𝑖,𝑡

) ∗ 𝑝𝑜𝑝𝑖,𝑡+𝑛)]

𝑢𝑟𝑏𝑖,𝑡∗ 100

where,

i refers to a particular metropolitan area.

t refers to the initial year

t+n refers to the final year

urb refers to the built-up area in square kilometres.

pop refers to the total population.

5. Territorial organisation

Several studies have analysed the structure of the metropolitan area governance in relation

to the efficiency and equity of service delivery, government efficiency, the distribution of

wealth within a metropolitan area, among others. As a first simplified measure of

administrative organisation we consider the number of local governments, the average

6 . Fractional Count has been used to count for total patents by metro area. The Fraction Count method

counts each patent as a fraction, depending on how territories are weighted. As an example, patent P, which is

produced by two inventors of different zip-code areas, this patent will count for 0.5 in each of these zip-code

areas. As a result each patent will count as one single patent for any given region avoiding double counting.

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population size of municipalities and the territorial fragmentation defined as the number of

local governments in a metropolitan area per 100,000 inhabitants.

To identify the number of local governments included in a metropolitan area two simplifying

assumptions have been considered:

Only general-purpose local governments are included, while the specific function

governments are excluded (for example school district, health agencies, etc.).

Only one local level of government has been included, notably the lowest tier, as a

measure of the horizontal fragmentation. Of course the administrative structure of a

country may include more than one level of government with relevant

responsibilities over the same territory covered by the metropolitan area.

6. Data sources

Indicator Data sources

Population The population by municipality for the years 2000 and 2014 comes from the Population Census. The

population by municipality from the Census 2012 is then recomputed according to the metropolitan boundaries

of 2001. The metropolitan population between the years 2000 and 2014 is estimated

GDP Estimated based on GDP data at TL3 level from OECD Regional Database

http://stats.oecd.org/Index.aspx?datasetcode=REG_DEMO_TL3

Labour Estimated based on labour data at TL3 level from OECD Regional Database


Total, Green and

Urbanised area

US: NLCD 2001 (Version 2) and NLCD 2006 databases;

Japan: Japan National Land Information 1997 and 2006;

EU (except Northern Ireland): CORINE Land Cover 2000 and CORINE Land Cover Changes 2000-2006;

Canada, Korea and Mexico: MODIS Land Cover data 2008, urban class refers circa to year 2001-2002. Data

are derived from medium spatial resolution (500m) satellite imagery and should be taken as rough estimates.

CO2 emissions European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL).

Emission Database for Global Atmospheric Research (EDGAR), release version 4.1.

http://edgar.jrc.ec.europe.eu, 2010.

Air pollution Van Donkelaar, A., R. V. Martin, M. Brauer, R. Kahn, R. Levy, C. Verduzco, and P. J. Villeneuve, Global

Estimates of Exposure to Fine Particulate Matter Concentrations from Satellite-based Aerosol Optical Depth,

Environ. Health Perspec., doi:10.1289/ehp.0901623, 118(6), 2010.

http://fizz.phys.dal.ca/~atmos/datasets/World-PM25-20010101-20061231-RH50.TIF.zip

Patents OECD Patent Database

www.oecd-ilibrary.org/science-and-technology/data/oecd-patent-statistics_patent-data-en



http://edgar.jrc.ec.europe.eu/

http://fizz.phys.dal.ca/~atmos/datasets/World-PM25-20010101-20061231-RH50.TIF.zip

http://www.oecd-ilibrary.org/science-and-technology/data/oecd-patent-statistics_patent-data-en

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Country Name of local governments in the territorial organisation Source

Austria Gemeinden (LAU2) EUROSTAT

Belgium Gemeenten/Communes (LAU2) EUROSTAT

Canada Census Subdivisions (towns, villages, etc.) (CSD) Statistics Canada (Statcan)

Switzerland Municipalities (LAU2) EUROSTAT

Chile

Comunas Instituto Nacional de Estadísticas (INE)

Chile

Czech Republic Obce (LAU2) EUROSTAT

Germany Gemeinden (LAU2) EUROSTAT

Denmark Sogne (LAU2) EUROSTAT

Estonia Vald, linn (LAU2) EUROSTAT

Spain Municipios (LAU2) EUROSTAT

Finland Kunnat / Kommuner (LAU2) EUROSTAT

France Communes (LAU2) EUROSTAT

Greece Demotiko diamerisma/Koinotiko diamerisma (LAU2) EUROSTAT

Hungary Települések (LAU2) EUROSTAT

Ireland Elector4al Districts (LAU1) EUROSTAT

Italy Comuni (LAU2) EUROSTAT

Japan

Shi (city), Machi or Cho (town) and Mura or Son (village) National Land Numerical Information

Service of Japan

Korea

Si (city), Gun (county), Gu (district) Korean Statistical Information Service

(KOSIS)

Luxembourg Communes (LAU2) EUROSTAT

Mexico

Municipios Instituto Nacional de Estadística y

Geografía (INEGI)

Netherlands Gemeenten (LAU2) EUROSTAT

Norway Municipalities (LAU2) EUROSTAT

Poland Gminy (LAU2) EUROSTAT

Portugal Freguesias (LAU2) EUROSTAT

Sweden Kommuner (LAU2) EUROSTAT

Slovenia Občine (LAU2) EUROSTAT

Slovak Republic Obce (LAU2) EUROSTAT

United Kingdom

County Councils. For those areas where the County Councils

were abolished the Local Authority (either a Metropolitan

District Council or a Unitary District Council) is used. For

London, the Borough Councils are used.

UK Office of National Statistics

United States

Municipalities or townships. In the geographic areas where

municipalities or townships do not represent a general

purpose government, the County governments were

considered.

U.S. Census Bureau “2002 Census of

Governments”

19

IV. References

Brezzi, M. and D. Sanchez-Serra (2014), “Breathing the Same Air? Measuring Air Pollution in Cities and Regions”, OECD Regional Development Working Papers, , No. 2014/11, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/5jxrb7rkxf21-en.

Brezzi, M. and D. Sanchez-Serra (2016), “Breathing the same air? Measuring pollution in regions and cities – updated results”. United Nations Economic and Social Council. Conference of European Statisticians. Economic Commission for Europe. Sixty-fourth plenary session. Paris, 27-29 April 2016 https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/2016/mtg/CES_34-Breathing_the_same_air__OECD_.pdf

Doll, C.N.H., J.P. Muller and C.D. Elvidge (2000), “Night-time imagery as a tool for global mapping of social-economic parameters and greenhouse gas emissions”, Ambio, 29(3): 157-162.

Milego, R. and M.J. Ramos (2006), Espo 2013 Database, Espon Publishing. Nordhaus, W., Q. Azam, D. Corderi, K. Hood, M.N. Victor, M. Mohammed, A. Miltner, J. Weiss (2006),

“The G-Econ Database on Gridded Output: methods and data”, available online at gecon.yale.edu, yale University, 75.

OECD (2012) Redefining Urban: A New Way to Measure Metropolitan Areas, OECD Publishing.

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