Everything you heard about urbanisation is wrong

Regional & Urban Policy

Everything you heard about urbanisation is wrong

By Lewis Dijkstra, Lewis.Dijkstra@ec.europa.eu

Head of the Economic Analysis Sector

DG for Regional and Urban Policy,

The well known narrative…

• The world is 50% urban.

• Middle income countries are less urbanised.

• Low income countries are least urbanised.

• Urbanisation will grow rapidly in low-income countries between 2010 and 2050.

… is purely based on national definitions

But national definitions are fine

• “I know it when I see it”, but maybe we see different things?

• The difference between national definitions must be quite small. How can we verify this without a benchmark?

• A city is a relative concept and differs between cultures and countries. A global definition is not appropriate.

What density reproduces the nationally defined urban population share?

Urban areas … lost in translation?

Big urban areas

• High Density

• Big population

• Low shares of agricultural jobs

• Specialised services (higher education, hospital, government)

Small urban areas

• Medium density

• Medium population

• Medium share of agricultural jobs

• Some services (primary school, doctor)

National definitions

• 75 countries use population size or density, but thresholds and spatial units vary

• 47 use a combination of population and other indicators

• 10 use other indicators than population

• 100 countries use administrative designations, not a statistical definition that can be replicated in other countries

Do national definitions rely on population size and or density?

Only half the countries use a minimum population size

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Minimum population size for urban areas, WUP 2014

14 countries use density

CountryMinimum

densityGermany 150Cambodia 200USA 390and195

Canada 400India 400Bhutan 1,000Philippines 1,000

China 1,500

• For the other countries we don’t know the density

• Density is highly dependent on the size of the unit, census block vslocal authority vsregion…

What indicators can be used globally

• Agricultural employment? Varies too much

• Infrastructure? No harmonised data

• Services? No harmonised data

• Poverty? Circular argument

Rural cannot be defined by problems, because then no problems = no rural

• Remoteness Separate dimension

Access to cities - Remoteness

SDG City goals, but no city definition

City Centre

Edge of the city

Open Space Low High

Air pollution High Low

Access to transport High Low

Built-up area per head Low High

Population change Low (neg) High

Built-up change Low High

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%ofpopulation

kmfromcitycentre

Accesstopublictransportbydistancetocentre

Praha

Berlin

London

Madrid

Paris

Who committed to develop a global definition?

• The European Union together with the OECD and the World Bank launched this commitment during Habitat III in Quito in 2016

• FAO has joined this commitment in 2017

• UN-Habitat follows this work closely

• Goal: present a definition to UN Statistical Commission in 2019

• UN SC side event in 2017 and in 2018

This is not a city

The population grid reduces modifiable areal unit problem

• Mongolian capital: Ulaanbaatar 1.4 million inhabitants, but a density of 272 inhab/km2

• Overcomes key obstacle: the variable size and shape of administrative and statistical units

• Allows to capture cities within a single large administrative unit AND a city spread out over multiple administrative units.

• Fixed boundaries and high spatial resolution

• Next census is going to more geo-coded

Exogenous city list reproduce distortions of national definitions

UNWUP Citydefinition2015Citiesover300,000 9 24Population 16million 32million

Nationalpopulationshareincitiesover300,000

17% 34%

Populationinsamecities 16million 24million

HoChiMinhCity 8million 12millionHanoi 3.6million 7.5million

HaiPhòng 1.1milion 1.3millionCanTho 1.2million 0.7million

An endogenous city list

• This method relies purely on the population grid to identify cities

• Other global work on cities relies on an exogenous city list

• Global Rural-Urban Mapping Project (GRUMP)

• Agglomeration index

• Shlomo Angel’s urban extent

• Any work that (in part) aims to replicate national shares will suffer from these distortions

Grid cells: same shape and size and boundaries do not change

Municipalities (LAU2)

118,504 in the EUPopulation Grid

4,404,011 in the EU

Two definitions with a common element: Cities

Cities

Towns & suburbs

Rural areas

Commuting zones

Non-metro areas

Population grid based

Creates a functional economic area

Three types of grid cells

Urban centres

Contiguous cells

above 1,500 residents per km2 and at least 50,000 people in the centre

Urban Clusters

Contiguous cells

above 300 residents per km2 and at least 5,000 people in the cluster

Rural grid cells

Cells below 300 residents per km2 + other cells outside urban clusters

Cork, Ireland: Urban centre, urban clusters and rural grid cells

Three types of municipalities

Cities > 50% pop. in urban centres

Towns and suburbs

> 50% pop. in urban clusters and not classified as city

Rural area > 50% pop. in rural grid cells

Urban areas = Cities + Towns and Suburbs

Cork, Ireland: Urban centre, urban clusters and rural grid cells

Cork, Ireland: City, towns & suburbs, and rural areas

Joint EU-OECD Definition of a Functional Urban Area

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%Population by degree of urbanisation per

major global region, 2015

Cities

Towns and suburbs

Rural areas

Urban areas WUP 2014Source: JRC 2018, GHSL SMOD v9s5, WUP 2014

More urban with the national definition or the degree of urbanisation?

Pilot projects to compare definitions

• Apply the definitions also to administrative units (census tracts, municipalities…)

• Appraise the result:

• Too urban or too rural?

• Too many or too few cities?

• Improve data (better population and/or remote sensing data)

• Find out if/how we can improve the definitions

Pilot projects by EC, OECD and WB

• Australia

• Brazil (completed)

• Colombia

• Egypt

• Haiti

• Indonesia

• India

• Jordan

• Malaysia

• Mozambique

• Pakistan

• South Africa (completed)

• Tunisia

• Turkey

• Uganda

• Ukraine

• USA

Australian cities

R² = 0.9957

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0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 4,500,000

Colombian cities

R² = 0.9985

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1,000,000

2,000,000

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Brazil

• We believe that this method offers a useful basis for statistical comparisons across national borders...

• … useful for generating Sustainable Development Goals’ indicators, producing data for these three types of cluster or for individual municipalities …

Survey by UN Statistical Division

1. Algeria

2. Argentina

3. Australia

4. Bolivia

5. China

6. Cuba

7. Ecuador

8. Ethiopia

9. Indonesia

10.Japan

11.Mexico

12.Mongolia

13.Namibia

14.New Zealand

15.Republic of Korea

16.Senegal

17.Thailand

18.USA

19.Venezuela

20.Zambia

Responses to the UN survey

• 9 out of 12 NSIs: it captured their main cities.

• 5 out of 8 NSIs the validity was good or satisfactory (1 poor and 2 unacceptable)

• 9 out of 10 NSIs could produce data by degree of urbanisation

• 9 out of 13 NSIs useful for international comparisons

• 11 out of 12 NSIs useful for measuring the SDGs

• 5 NSIs did not reply

• Some confusion about density thresholds and the distinction between cities and towns

Three key tools

• National fact sheets http://ghsl.jrc.ec.europa.eu/CFS.php

• Interactive online mapshttp://ghsl.jrc.ec.europa.eu/visualisation.php#

• City databasehttp://ghsl.jrc.ec.europa.eu/ccdb2016visual.php#

National factsheets

• Covers every country in the world

• Shows the share of population, built-up area and land by degree of urbanisation and its changes since 1975

• Lists the biggest cities

• Provides access to a list of all cities in a country with their change in population and built-up area over time

• Map of the country and the capital city

• Methodological info

Malaysia

Each city over 50,000

1975Population

1 984 026

Built-up area

888 km2

1990Population

3 323 272

Built-up area

900 km2

2000Population

4 539 169

Built-up area

945 km2

2015Population

6 472 648

Built-up area

1 039 km2

Per city

• Evolution in population, built-up area and built-up per capita, 1975-2015

• Nightlights, 1990-2014

• Greenness index 1990-2014

• Particulate matter, 2000-2010

• Share of imperviousness, 2015

• Proximity to protected natural areas

• Population at or below sea-level, 1975-2015

• Population on steep slopes, 1975-2015

Next steps

• Distinguish towns from suburbs and villages from dispersed rural areas (done)

• Validate the updated cities (June 2018)

• Estimate commuting zones (summer 2018)

• Update factsheets, city database and interactive maps (October 2018)

Refined degree of urbanisation

Driving time buffers

• Commuting data is not widely available

• We will create a driving time buffer around each urban centre. Distance will depend on the population size of the city, GDP per head of the country and the road network

• Time series in selected countries

Next events

• May 2018 Urban Economics Association Conference (Dusseldorf)

• September 2018 International Association of Official Statistics, (Paris)

• October 2018 UN World Data Forum (Dubai)

• March 2019 UN Statistical Commission (New York)

• August 2019 ISI World Statistical Congress (Kuala Lumpur)

Conclusions

• A neutral global definition of urban is needed, national definitions are too different.

• Just like the $1 a day poverty rate, it should NOT replace national definitions but complement them

• The degree of urbanisation and the functional urban area definition are likely to be approved by the UNSC and already endorsed by EU and OECD

• The World Bank could make these definitions a standard component of all work on urbanisation and work with statistical offices

More information

• https://ec.europa.eu/eurostat/cros/content/global-city-and-settlement-definition_en

• http://ghsl.jrc.ec.europa.eu/degurba.php

• http://urban.jrc.ec.europa.eu/

1975

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2015

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5,000

500 99 95 93 90 89 87 85 83 81 79 77 76 74 73 71 70 68 67 66 65 64 63 62 61 60 59 58 57 56 55 55 54 53 52 52 51 50 50 49 49 48 47 47 46 46 45 45 44 44 43 43

1,000 98 94 91 89 87 85 83 81 80 78 77 76 74 73 71 70 68 67 66 65 64 63 62 61 60 59 58 57 56 55 55 54 53 52 52 51 50 50 49 49 48 47 47 46 46 45 45 44 44 43 43

2,000 96 91 88 86 84 82 80 78 77 75 74 72 71 70 69 68 67 66 65 64 64 63 62 61 60 59 58 57 56 55 55 54 53 52 52 51 50 50 49 49 48 47 47 46 46 45 45 44 44 43 43

3,000 95 90 87 84 82 80 78 76 75 73 72 70 69 68 67 66 65 64 63 62 61 60 59 59 58 57 57 56 56 55 55 54 53 52 52 51 50 50 49 49 48 47 47 46 46 45 45 44 44 43 43

4,000 94 88 85 83 80 78 76 75 73 72 70 69 68 66 65 64 63 62 61 60 60 59 58 57 56 56 55 54 54 53 52 52 51 51 50 50 49 49 49 48 48 47 47 46 46 45 45 44 44 43 43

5,000 93 87 84 81 79 77 75 73 72 70 69 68 66 65 64 63 62 61 60 59 58 58 57 56 55 55 54 53 52 52 51 51 50 50 49 49 48 48 47 47 46 46 45 45 45 44 44 44 43 43 43

6,000 92 86 83 80 78 76 74 72 70 69 68 66 65 64 63 62 61 60 59 58 57 57 56 55 54 54 53 52 52 51 51 50 49 49 48 48 47 47 46 46 45 45 44 44 44 43 43 42 42 42 41

7,000 92 85 82 79 77 75 73 71 69 68 67 65 64 63 62 61 60 59 58 57 56 56 55 54 53 53 52 52 51 50 50 49 49 48 48 47 46 46 45 45 45 44 44 43 43 42 42 42 41 41 40

8,000 91 84 81 78 76 74 72 70 68 67 66 64 63 62 61 60 59 58 57 56 56 55 54 53 53 52 51 51 50 50 49 48 48 47 47 46 46 45 45 45 44 44 43 43 42 42 41 41 41 40 40

9,000 91 84 80 77 75 73 71 69 68 66 65 64 62 61 60 59 58 57 57 56 55 54 53 53 52 51 51 50 50 49 48 48 47 47 46 46 45 45 44 44 44 43 43 42 42 41 41 41 40 40 39

10,000 90 83 79 77 74 72 70 68 67 65 64 63 62 61 60 59 58 57 56 55 54 54 53 52 51 51 50 50 49 48 48 47 47 46 46 45 45 44 44 43 43 43 42 42 41 41 41 40 40 39 39

20,000 86 78 74 71 69 66 65 63 61 60 59 57 56 55 54 53 53 52 51 50 49 49 48 47 47 46 46 45 45 44 44 43 43 42 42 41 41 40 40 40 39 39 38 38 38 37 37 37 36 36 36

30,000 84 75 71 68 65 63 61 59 58 56 55 54 53 52 51 50 49 49 48 47 46 46 45 45 44 43 43 42 42 41 41 41 40 40 39 39 38 38 38 37 37 37 36 36 35 35 35 35 34 34 34

40,000 82 73 69 66 63 61 59 57 55 54 53 52 51 50 49 48 47 46 46 45 44 44 43 43 42 41 41 40 40 40 39 39 38 38 37 37 37 36 36 36 35 35 34 34 34 34 33 33 33 32 32

50,000 81 71 67 64 61 59 57 55 53 52 51 50 49 48 47 46 45 45 44 43 43 42 41 41 40 40 39 39 38 38 38 37 37 36 36 36 35 35 34 34 34 33 33 33 32 32 32 32 31 31 31

60,000 80 70 65 62 59 57 55 53 52 50 49 48 47 46 45 45 44 43 42 42 41 40 40 39 39 38 38 37 37 37 36 36 35 35 35 34 34 34 33 33 33 32 32 32 31 31 31 30 30 30 30

70,000 79 68 64 61 58 56 54 52 50 49 48 47 46 45 44 43 42 42 41 40 40 39 39 38 38 37 37 36 36 35 35 35 34 34 34 33 33 32 32 32 31 31 31 31 30 30 30 29 29 29 29

80,000 78 67 63 59 57 54 52 51 49 48 47 45 45 44 43 42 41 40 40 39 39 38 38 37 37 36 36 35 35 34 34 34 33 33 32 32 32 31 31 31 31 30 30 30 29 29 29 29 28 28 28

90,000 77 66 62 58 55 53 51 49 48 47 45 44 43 42 42 41 40 39 39 38 37 37 36 36 35 35 35 34 34 33 33 33 32 32 32 31 31 31 30 30 30 29 29 29 28 28 28 28 27 27 27

100,000 76 65 61 57 54 52 50 48 47 45 44 43 42 41 41 40 39 38 38 37 36 36 35 35 34 34 34 33 33 32 32 32 31 31 31 30 30 30 29 29 29 28 28 28 28 27 27 27 27 26 26

250,000 69 57 52 48 45 43 41 39 37 36 35 34 33 32 31 31 30 30 29 29 28 28 27 27 26 26 26 25 25 25 24 24 24 24 23 23 23 23 22 22 22 22 22 21 21 21 21 20 20 20 20

500,000 65 51 46 42 39 36 34 33 31 30 29 28 27 26 26 25 25 24 24 23 23 22 22 22 22 21 21 21 20 20 20 20 20 19 19 19 19 18 18 18 18 18 18 17 17 17 17 17 17 16 16

1,000,000 60 46 40 36 33 30 28 27 25 24 24 23 22 21 21 20 20 19 19 19 18 18 18 18 17 17 17 17 16 16 16 16 16 15 15 15 15 15 15 14 14 14 14 14 14 14 14 13 13 13 13

5,000,000 50 33 27 23 20 17 16 14 13 13 12 12 11 11 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6

Populationsizeofthecluster

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