Supporting Information for:Global trends towards urban street-network sprawl

Christopher Barrington-Leigh1,Y, Adam Millard-Ball2,Y,

This document is a supplement to the primary text published in PNAS:https://doi.org/10.1073/pnas.1905232116

Latest update always at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl

1 McGill University; Institute for Health and Social Policy; and McGill School of Environment;Montreal, Canada2 University of California Santa Cruz; Environmental Studies Department; Santa Cruz, California

YAuthors are listed alphabetically and contributed equally to this work.

ContentsA Urban development time series 3

B Persistence 7

C Sensitivity analysis 7C.1 Inclusion of non-urban roads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7C.2 Buffer radius used to separate nodes/junctions . . . . . . . . . . . . . . . . . . . . . . . 7C.3 Exclusion of walking and cycling paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

D Data and code release 11

E Citation and contact 11

References 11

F Supplemental reference book: results 12F.1 Distribution of empirical street-network types . . . . . . . . . . . . . . . . . . . . . . . . 12F.2 Trends and global distributions of key variables . . . . . . . . . . . . . . . . . . . . . . . 15F.3 Summary trend plots for selected street-network sprawl measures . . . . . . . . . . . . . 16F.4 Road growth rates and connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26F.5 Countries ranked by urban street-network sprawl (SNDi) of recent road development . . 28F.6 Cities ranked by urban street-network sprawl (SNDi) of recent road development . . . . 35F.7 City maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42F.8 World maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42F.9 World region trend plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42F.10 Country trend plots with sub-national regional distributions . . . . . . . . . . . . . . . . 47F.11 City trend plots (by region) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66F.12 City trend plots (by country) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

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www.pnas.org/cgi/doi/10.1073/pnas. 1905232116

List of FiguresS1 Comparison of GHSL and Atlas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4S2 Comparison of GHSL with parcel- and census-based measures, USA . . . . . . . . . . . 5S3 Validation of connectivity time series, using house construction dates . . . . . . . . . . . 6S4 Path dependence in street-network sprawl . . . . . . . . . . . . . . . . . . . . . . . . . . 7S5 Sensitivity analysis: global maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8S6 Sensitivity analysis: trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9S7 Distribution of nodes by empirical type . . . . . . . . . . . . . . . . . . . . . . . . . . . 13S8 Distribution of grid cells by empirical type . . . . . . . . . . . . . . . . . . . . . . . . . . 14S9 Global shifts over time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15S10 Rate of road growth and SNDi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

List of TablesS1 Countries listed in order of recent urban street-network sprawl (SNDi) . . . . . . . . . . 29S2 Cities listed in order of their recent urban street-network sprawl (SNDi) . . . . . . . . . 36

2

A Urban development time seriesThe majority of our analysis employs the time series from GHSL, an open-data project providing globalspatial information about the human presence on the planet over time [Pesaresi et al., 2013]. Data areavailable online (http://ghsl.jrc.ec.europa.eu). We use the built-up grid which is derived fromanalysis of Landsat image collections, and provides built-up year classifications with approximately 38m resolution. Pixels are classified as follows: land not built-up in any epoch; built-up from 2000 to2014 epochs; built-up from 1990 to 1999 epochs; built-up from 1975 to 1989 epochs; built-up before1975 epoch. For computational reasons, we aggregate to 306 m resolution, and calculate the built-upyear of the aggregated grid cells as the modal built-up year of individual pixels. Each edge and nodeis assigned the modal epoch of the intersecting pixels.

Our city-level aggregations utilize the time series from Atlas of Urban Expansion [Angel et al.,2012, 2016], an open-data online database of city boundaries. The Atlas includes a sample of 200 of theworld’s metropolitan areas that had 100,000 people or more in 2010 and provides urban extents at threetime points: circa 1990, circa 2000, and circa 2013. We calculate spatial differences between successivetime points in order to generate boundaries for new development during the periods 1990–1999 and2000–2013. We then aggregate our street-level metrics to these regions to characterize developmentover time in these cities.

Below, we analyze the consistency of the development dates from each source, through comparingthem to each other, and to our earlier work in the USA. Figure S1 shows the consistency of our twodata sources where they overlap. The GHSL dataset tends to assign an earlier year-built to each node,but the trends are consistent.

For the USA, we also compare the GHSL-based method of generating a time series to our earlierwork, which used census and county parcel assessment data [Barrington-Leigh and Millard-Ball, 2015].The census-based method covers the entire USA and assigns to each block group households’ medianreport of the year when their house was constructed. The parcel-based method covers only about a thirdof the USA but makes use of county records’ building construction date for every residential propertyin order to assign an original construction date for each street segment. As shown in Figure S2, themethods assign closely matching dates to road segments, even though the data sources are completelyindependent: one relies solely on remote sensing data, while the others rely on self-reported houseconstruction dates. For years through 1999, the inter-quartile range of the census- and parcel-datamethods matches GHSL almost exactly. For the most recent period (2000-13), while the median yearsalign (solid red lines), the inter-quartile range does not fully overlap. This is likely due to aggregationbias in census geography, as the block group boundaries do not reflect more recent development.

Consistency in the time series of road network evolution translates into consistency in our mainresults, which concern the evolution of connectivity over time. Figure S3 presents comparisons of trendsin mean degree and fraction of deadends, the two metrics used to characterize sprawl by Barrington-Leigh and Millard-Ball [2015], for the entire USA and for one metropolitan region with good coverageby parcel-based data.

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Figure S1: Comparison of GHSL and Atlas. Each plot shows the cumulative number of nodeswithin the Atlas cities, for the entire world and by select World Bank-defined regions. By construction,the cumulative number is equal in 2014.

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Figure S2: Comparison of GHSL with parcel- and census-based measures, USA. The unitsof analysis are census block groups in urban areas. We take the median GHSL year of each node witha block group, and compare that to the median year built of residential units within the block group(“census data”), and the median year of nodes within the block group, calculated from parcel assessmentdata on construction years (“parcel data”). Note that the census data have complete coverage, while theparcel-data sample accounts for about one-third of the urban US [Barrington-Leigh and Millard-Ball,2015].

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Figure S3: Validation of connectivity time series, using house construction dates. The timeseries of street-network sprawl (measured by the fraction of deadends) in our earlier work [Barrington-Leigh and Millard-Ball, 2015] is consistent with that generated by the remote-sensing method (GHSL)used in our global analysis. We show two connectivity measures calculated for the urban USA inour earlier work (top plots), as well as for the metropolitan area (Minneapolis-St Paul) where taxparcel data have the greatest coverage. For parcel and census data, thin lines show yearly averages(smoothed in the case of Minneapolis) while bars show period averages for comparison with GHSL.The first period average is the stock value up to 1974.

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B PersistenceThe main text discusses the persistence of street-network sprawl. Figure S4 repeats a figure from themain text, but provides small, zoomable labels.

AFG

ALB

DZA

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ARG

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Accra, Ghana

Addis Ababa, Ethiopia

Ahmedabad, India

Ahvaz, Iran

Alexandria, Egypt

Algiers, Algeria

Anqing, China

Antwerp, Belgium

Arusha, TanzaniaAstrakhan, Russia

Auckland, New Zealand

Bacolod, Philippines

Baghdad, Iraq

Baku, Azerbaijan

Bamako, Mali

Beijing, China

Beira, Mozambique

Belgaum, India

Belgrade, Serbia

Belo Horizonte, Brazil

Berezniki, Russia

Berlin, Germany

Bicheng, China

Bogota, Colombia

Budapest, Hungary

Bukhara, Uzbekistan

Busan, South Korea

Cabimas, Venezuela

Cairo, Egypt

Caracas, Venezuela

Cebu City, Philippines

Changzhi, ChinaChangzhou, China

Chengdu, China

Chengguan, China

Cheonan, South Korea

Chicago, United States

Cirebon, Indonesia

Cleveland, United States

Cochabamba, Bolivia

Coimbatore, India

Cordoba, Argentina

Culiacan, Mexico

Curitiba, Brazil

Dhaka, Bangladesh

Dzerzhinsk, Russia

Florianopolis, Brazil

Fukuoka, Japan

Gainesville, United States

Gaoyou, China

Gombe, Nigeria

Gomel, Belarus

Gorgan, Iran

Guadalajara, Mexico

Guatemala City, Guatemala

Guixi, China

Gwangju, South Korea

Haikou, ChinaHalle, Germany

Hangzhou, China

Hindupur, India

Ho Chi Minh City, Viet Nam

Holguin, Cuba

Hong Kong, China

Houston, United States

Hyderabad, India

Ibadan, Nigeria

Ilheus, Brazil

Ipoh, Malaysia

Istanbul, Turkey

Jaipur, India

Jalna, India

Jequie, Brazil

Jinan, China

Jinju, South Korea

Johannesburg, South Africa

Kabul, Afghanistan

Kaiping, China

Kairouan, Tunisia

Kampala, Uganda

Kanpur, India

Karachi, Pakistan

Kaunas, Lithuania

Kayseri, Turkey

Khartoum, Sudan

Kigali, Rwanda

Killeen, United States

Kinshasa, DR Congo

Kolkata, India

Kozhikode, India

Lagos, Nigeria

Lahore, Pakistan

Lausanne, Switzerland

Le Mans, France

Leon, Nicaragua

Leshan, China

Los Angeles, United States

Luanda, Angola

Lubumbashi, DR Congo

Madrid, Spain

Malatya, Turkey

Malegaon, India

Manchester, United Kingdom

Manila, Philippines

Marrakesh, Morocco

Medan, Indonesia

Milan, Italy

Minneapolis, United States

Modesto, United States

Montreal, Canada

Moscow, Russia

Mumbai, India

Myeik, Myanmar

Nakuru, Kenya

Ndola, Zambia

Nikolaev, Ukraine

Okayama, Japan

Oldenburg, Germany

Osaka, Japan

Oyo, Nigeria

Palembang, Indonesia

Palermo, Italy

Palmas, Brazil

Parepare, Indonesia

Pematangsiantar, Indonesia

Philadelphia, United States

Pingxiang, China

Pokhara, Nepal

Port Elizabeth, South Africa

Portland, United States

Pune, India

Pyongyang, North Korea

Qingdao, China

Qom, Iran

Quito, Ecuador

Rajshahi, Bangladesh

Raleigh, United States

Rawang, Malaysia

Reynosa, Mexico

Ribeirao Preto, Brazil

Riyadh, Saudi Arabia

Rovno, Ukraine

Saidpur, Bangladesh

Saint Petersburg, Russia

San Salvador, El Salvador

Sana, Yemen

Santiago, Chile

Shanghai, China

Sheffield, United Kingdom

Shenzhen, China

Shymkent, Kazakhstan

Sialkot, Pakistan

Singapore, Singapore

Singrauli, India

Sitapur, India

Springfield, United States

Suining, China

Suva, Fiji

Sydney, Australia

Taipei, China

Tangshan, China

Tashkent, Uzbekistan

Tebessa, Algeria

Tehran, Iran

Tel Aviv, Israel

Thessaloniki, Greece

Tianjin, China

Tijuana, Mexico

Toledo, United States

Tyumen, Russia

Ulaanbaatar, Mongolia

Valledupar, Colombia

Victoria, Canada

Vienna, Austria

Vijayawada, India

Vinh Long, Viet Nam

Warsaw, Poland

Wuhan, China

Xingping, China

Xucheng, China

Yamaguchi, Japan

Yanggu, China

Yiyang, China

Yucheng, ChinaYulin, China

Zhengzhou, China

Zhuji, China

Zunyi, China

Zwolle, Netherlands

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Figure S4: Path dependence in street-network sprawl. This figure is repeated from the maintext, with the addition of small labels on cities and countries (three-letter ISO) visible by zoomingin. SNDi of recent (2000–2013) construction is closely correlated with that of the earlier road stockin 1975 (countries, A) and 1990 (cities, B). Circle size denotes the length of original road stock, whilecolor indicates the scale of recent construction. Only cities and countries with at least 100 km ofnew roads are shown. Linear fits characterizing the persistence relationship (gray shaded confidenceregion) are nearly parallel to the 45 degree line of perfect persistence (dark grey line) indicating arelatively uniform shift (on average ∆SNDi = +1.56 for cities, +1.26 for countries) away from earlierhigh connectivity.

C Sensitivity analysis

C.1 Inclusion of non-urban roadsWhile our preferred metrics are based only on the portions of road network we classify as urban, whichaccount for 75% of nodes and 77% of edges (see Barrington-Leigh and Millard-Ball [2019] for details),we also calculate all measures on the full global set of nodes and metrics. Figure S5 and Figure S6(top-right panels) compare this analysis to the corresponding figures from the main text (top-leftpanels), and show that including the remaining non-urban nodes and edges makes no qualitative, andonly subtle quantitative, differences. There is a small rightward shift in the distribution of SNDi,shown in the inset to Figure S5, particularly in Russia and some countries in northern South America,indicating that non-urban streets tend to be less connected. The qualitative conclusions, however, areunchanged.

C.2 Buffer radius used to separate nodes/junctionsWe reran our entire analysis using a radius of 7 m for merging intersections into nodes in our networkrepresentation (lower-left panels in Figure S5 and Figure S6). Aggregate findings are nearly identicalto the 10 m version used in our primary analysis.

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Figure S5: Sensitivity analysis for Figure 1 from the main text. The top-left map reproduces Figure 1, recent SNDi, from the maintext. At top-right is a version calculated using all (i.e., urban and non-urban) nodes and edges, rather than only those we designate as “urban.”The lower left map is a version calculated with an alternative intersection buffer radius (7 m), and the lower right one is calculated with theexclusion of walking and bicycling paths and service roads.

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Figure S6: Top left: Sensitivity analysis for Figure 2 from the main text. The top-left set of panels reproduces Figure 2, trends inSNDi, from the main text. At top-right is a version calculated using all (i.e., urban and non-urban) nodes and edges, rather than only thosewe designate as “urban.” The lower left panels are a version calculated with an alternative intersection buffer radius (7 m), and the lower rightset are calculated with the exclusion of walking and bicycling paths and service roads. In each panel, the left two sub-plots share an ordinatescale, and the right two share a separate ordinate scale.

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C.3 Exclusion of walking and cycling pathsOur main results aggregate the properties of only those edges and nodes that are accessible by motorvehicle. However, walking and cycling paths, as well as service roads such as driveways, are consid-ered when calculating connectivity. For example, two adjacent culs-de-sac that are connected by apedestrian path would not be considered deadends. (See Barrington-Leigh and Millard-Ball [2019] fordetails.) Figure S5 and Figure S6 (lower-right panels) show the results of an analysis that excludesall walking and cycling paths and driveways. Several countries and cities, particularly in the UK,Scandinavia and other parts of Europe, show substantially higher SNDi when the extra paths arecompletely excluded, indicating that such paths make a major contribution to the connectivity of thestreet network. The increase in SNDi in London is particularly marked. The discussion in the maintext elaborates on the implications for policy.

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D Data and code releaseAll data used for this work are open. Freely available sources are listed under “Data Release” inBarrington-Leigh and Millard-Ball [2019] and in the Materials and Methods section of the main text.Our code to reproduce the data and analysis, which itself leverages exclusively open-source tools, isreleased under the GNU General Public License v3.0 as an open source project, permanently availableat:

https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl/codeThat site includes the following description of the code:

https://gitlab.com/cpbl/global-sprawl-2020/blob/master/README.mdIn addition, we provide the data corresponding to our street-network sprawl metrics aggregated to

various levels described in more detail in Barrington-Leigh and Millard-Ball [2019]. Data are servedfrom the following site:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl/data

Moreover, an interactive map interface to our high-resolution results, with links to data downloads,is available at https://sprawlmap.org.

E Citation and contactFor any use of the data or code, please cite the main paper.

For further questions, please contact:Christopher Barrington-Leigh, McGill UniversityAdam Millard-Ball, University of California, Santa Cruz

ReferencesShlomo Angel, Alejandro M Blei, Daniel L Civco, and Jason Parent. Atlas of urban expansion. LincolnInstitute of Land Policy, Cambridge, MA, 2012. URL http://atlasofurbanexpansion.org.

Shlomo Angel, Alejandro M. Blei, Jason Parent, Patrick Lamson-Hall, Nicolás Galarza Sánchez, withDaniel L. Civco, Rachel Qian Lei, and Kevin Thom. Atlas of Urban Expansion — 2016 Edition:Volume 1: Areas and Densities. NYU Urban Expansion Program at New York University, UN-Habitat, and the Lincoln Institute of Land Policy, 2016. URL http://www.lincolninst.edu/publications/other/atlas-urban-expansion-2016-edition.

Christopher Barrington-Leigh and Adam Millard-Ball. A century of sprawl in the United States.Proceedings of the National Academy of Sciences, 112(27):8244–8249, 2015.

Christopher Barrington-Leigh and Adam Millard-Ball. A global assessment of street network sprawl.PLOS ONE, 2019. doi: https://doi.org/10.1371/journal.pone.0223078.

Martino Pesaresi, Guo Huadong, Xavier Blaes, Daniele Ehrlich, Stefano Ferri, Lionel Gueguen, MatinaHalkia, Mayeul Kauffmann, Thomas Kemper, Linlin Lu, et al. A global human settlement layer fromoptical HR/VHR RS data: concept and first results. IEEE Journal of Selected Topics in AppliedEarth Observations and Remote Sensing, 6(5):2102–2131, 2013.

World Bank. World Bank Open Data, 2016. URL https://blogs.worldbank.org/opendata/new-country-classifications-2016. https://blogs.worldbank.org/opendata/new-country-classifications-2016.

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F Supplemental reference book: resultsThe sections which follow contain more detailed tabular and graphical renditions of our findings. Inaddition, extensive online visualizations of our results are available at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawlincluding an interactive map interface to our high-resolution results at:https://sprawlmap.org.

F.1 Distribution of empirical street-network typesFigure S7 shows the trends over time in the distribution of empirical street-network types by WorldBank-defined regions; it is similar to the plot in the main text, but includes additional regions. Fig-ure S8 repeats the figure, but shows the distribution according to the fraction of grid cells (rather thanthe fraction of nodes) that fall into each cluster.

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-14 all

HIPC

Figure S7: Distribution of nodes by empirical type, by World Bank-defined region andyear. The bars represent the fraction of nodes in each type.

0.0

0.5

1.0fr

acti

ongr

idce

llsAfrica European Union

H: Disconnected

G: Dendritic

F: Dead ends

E: Circuitous

D: Broken grid

C: Irregular grid

B: Degree-3

A: Grid

0.0

0.5

1.0

frac

tion

grid

cells

E. Asia & Pacific S. Asia North America

0.0

0.5

1.0

frac

tion

grid

cells

Latin America & Carib Mid-East & N. Africa High income

<19

75

1975

-89

1990

-99

2000

-14 all

0.0

0.5

1.0

frac

tion

grid

cells

Middle income

<19

75

1975

-89

1990

-99

2000

-14 all

Low income

<19

75

1975

-89

1990

-99

2000

-14 all

HIPC

Figure S8: Distribution of grid cells by empirical type, by World Bank-defined region andyear. The bars represent the fraction of grid cells in each type. (The two types that were droppedare not shown; hence, the bars do not add up to 100%.)

14

Den

sity

SNDi

Den

sity

Fraction 1- and3-degree nodes

Den

sity

Fractiondeadends

Den

sity

Log Circuity(0.5–1km)

Den

sity

Fraction non-cycle(N edges)

Den

sity

Fraction bridges(N edges)

<1975

1975–89

1990–99

2000–13

Figure S9: Global shifts over time. Curves show kernel density estimates of unweighted country-level means over urban nodes/edges, using built-up year classifications based on GHSL.

F.2 Trends and global distributions of key variablesOur analysis emphasizes our aggregate (first principal component) measure of street-network sprawl,SNDi. Figure S9 shows how the global distributions of SNDi and five of its individual connectivitymeasures have evolved over time. In each case, there is a significant shift towards lower connectivity,but with a partial leveling off in the latest time period.

15

F.3 Summary trend plots for selected street-network sprawl measuresThe following pages contain plots in the format of Figure 2 in the main text, but show trends ofindividual connectivity metrics making up our SNDi index. In each plot, the left two panels share avertical axis, and the right two panels share a separate vertical axis.

16

<1975 ’75–’89

’90–’99

’00–’13

2

3

4

5

6

Spr

awl

(SN

Di)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-130

1

2

3

4

5

6

7

8D

Mexico City

Seoul

Tokyo

ParisGuangzhou

Buenos Aires

Bangkok

New York

London

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

2.6

2.7

2.8

2.9

3.0

3.1

3.2

No

dal

deg

ree

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

2.4

2.6

2.8

3.0

3.2

3.4

3.6

D

Mexico City

Seoul

Tokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

London

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

0.125

0.150

0.175

0.200

0.225

0.250

0.275

Frc

dea

den

ds

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

0.0

0.1

0.2

0.3

0.4

D

Mexico City

SeoulTokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New YorkLondon

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

Frc

no

n-c

ycle

(len

gth

)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

0.1

0.2

0.3

0.4

0.5D

Mexico City

SeoulTokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

London

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

0.14

0.16

0.18

0.20

0.22

0.24

Lo

gC

ircu

ity

(0.5

–1

km)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

0.10

0.15

0.20

0.25

0.30

D

Mexico City

Seoul

Tokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

London

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

0.10

0.15

0.20

0.25

0.30

Frc

no

n-c

ycle

(Ned

ges

)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

0.0

0.1

0.2

0.3

0.4

0.5D

Mexico City

Seoul

Tokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

LondonSao Paulo

<1975 ’75–’89

’90–’99

’00–’13

1.05

1.06

1.07

1.08

1.09

1.10

1.11

1.12

Cu

rvin

ess

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

1.05

1.10

1.15

1.20

1.25

1.30

1.35D

Mexico City

Seoul

Tokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

London

Sao Paulo

<1975 ’75–’89

’90–’99

’00–’13

8.00

8.25

8.50

8.75

9.00

9.25

9.50

9.75

log

10(l

eng

th/

m)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

EarthB

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

<1990 90-99 2000-13

5

6

7

8

9

D

Mexico CitySeoul

Tokyo

Paris

Guangzhou

Buenos Aires

Bangkok

New York

LondonSao Paulo

<1975 ’75–’89

’90–’99

’00–’13

0

1

2

3

4

len

gth

/p

erso

n(m

)

A

Africa

European Union

E. Asia & Pacific

S. Asia

North America

Latin America & Carib

Mid-East & N. Africa

<1975 ’75–’89

’90–’99

’00–’13

Earth

B

High income

Middle income

Low income

HIPC

<1975 ’75–’89

’90–’99

’00–’13

C

Bangladesh

Nigeria

USA

Japan

China

Indonesia

India

Russia

Pakistan

Brazil

F.4 Road growth rates and connectivityOur aggregations from individual road segments (edges) according to GHSL-determined epochs nat-urally produce estimates of the rate of road growth in each GADM region, grid cell, and Atlas ofUrban Expansion city. Of course, these estimates are subject to the same uncertainties as our othercalculations, namely the completeness of the OSM street database and the accuracy of GHSL andAtlas classifications.

Because global trends are driven by the size of additions to road stock, we explore the relationshipbetween SNDi and the length of recent additions to the road stock, by city and by country (Figure S10top row). There is no statistical relationship across cities, and only a weak one across countries. Thecountries with the largest road growth — China, India, United States, Nigeria, Indonesia, and SouthAfrica — span the range of relatively low to relatively high SNDi. China’s construction of 655,000km of urban roads during this period is equal to nearly half the entire urban stock in the USA in1975. Among cities, Bangkok and Tokyo stand out again; Bangkok is among the fastest growing cities,as well as an outlier in the low connectivity of its new street development. Meanwhile, Tokyo andGuangzhou are examples of equally fast or faster growth but with high connectivity. Houston appearsto be a (fast-growing) low-connectivity city in the global picture.

In addition to analyzing SNDi against the magnitude of growth in roads, we compare it to the rateof growth. We define the growth rate of road networks as R/(S−R), where R is the addition to lengthin the most recent period (2000-2013) and S is the length of the current stock. Figure S10 (bottom row)shows the relationship between recent SNDi and the road growth rate. Faster-growing cities tend to bebuilding more-connected roads, perhaps largely due to the existence of rapidly-growing, initially-smallcities in China. While a relationship holds also across countries, it does not hold statistically withinany of our world regions (not shown). In terms of growth rate, the largest countries are in Africa.Similarly, as measured by road length per capita (not shown), African countries make up ten of thetop 14 fastest builders. Notably, China is a significant investor in growing African infrastructure.

26

105 106 107

Recent road stock growth (m, 2000-13)

0

2

4

6

8

10

SN

Di

(20

00

-13

)

Accra

Addis Ababa

Ahmedabad

Ahvaz

Alexandria

Algiers

Anqing

Antwerp

ArushaAstrakhan

Auckland

Bacolod

Baghdad

Baku

Bamako

Bangkok

Beijing

Beira

Belgaum

Belgrade

Belo Horizonte

Berezniki

Berlin

Bicheng

Bogota

Budapest

Buenos Aires

Bukhara

Busan

Cabimas

Cairo

Caracas

Cebu City

Changzhi ChangzhouChengdu

Chengguan

Cheonan

Chicago

Cirebon Cleveland

Cochabamba

Coimbatore

Cordoba

Culiacan

Curitiba

Dhaka

Dzerzhinsk

Florianopolis

Fukuoka

Gainesville

GaoyouGombe

Gomel

Gorgan

Guadalajara

Guangzhou

Guatemala City

Guixi

Gwangju

HaikouHalle

Hangzhou

Hindupur

Ho Chi Minh City

Holguin

Hong Kong

Houston

HyderabadIbadan

Ilheus

IpohIstanbul

JaipurJalna

Jequie

Jinan

Jinju

Johannesburg

Kabul

Kaiping

Kairouan

Kampala

Kanpur

Karachi

Kaunas

Kayseri

Khartoum

Kigali

Killeen

Kinshasa

Kolkata

Kozhikode

Lagos

Lahore

Lausanne

Le Mans

Leon

Leshan

London

Los Angeles

LuandaLubumbashi

Madrid

Malatya

Malegaon

Manchester

Manila

Marrakesh

Medan

Mexico City

Milan

Minneapolis

Modesto

Montreal

Moscow

Mumbai

MyeikNakuru

Ndola

New York

NikolaevOkayama

Oldenburg

Osaka

Oyo

Palembang

PalermoPalmas

Parepare

Paris

Pematangsiantar Philadelphia

Pingxiang

Pokhara

Port Elizabeth

Portland

Pune

Pyongyang

Qingdao

Qom

Quito

Rajshahi

Raleigh

Rawang

Reynosa

Ribeirao Preto

Riyadh

RovnoSaidpur

Saint Petersburg

San Salvador

Sana

Santiago

Sao Paulo

SeoulShanghai

Sheffield

Shenzhen

Shymkent

Sialkot

Singapore

SingrauliSitapur

Springfield

Suining

Suva

Sydney

Taipei

Tangshan

Tashkent

Tebessa

Tehran

Tel Aviv

Thessaloniki

Tianjin

Tijuana

Tokyo

ToledoTyumen

Ulaanbaatar

Valledupar

Victoria

Vienna

Vijayawada

Vinh Long

Warsaw

Wuhan

Xingping

Xucheng

Yamaguchi

Yanggu

YiyangYuchengYulin

ZhengzhouZhuji

Zunyi

Zwolle

R2=0.00028 b=-0.017±0.14

105 106 107 108 109

Recent road stock growth (m, 2000-13)

0

2

4

6

8

SN

Di

(20

00

-13

)

Afghanistan

Albania

AlgeriaAngola

Argentina

Armenia

AustraliaAustria

Azerbaijan

Bahrain

Bangladesh

Barbados

Belarus

Belgium

Belize

Benin

Bhutan

Bolivia

Bosnia and Herzegovina

Botswana

Brazil

Brunei Darussalam

Bulgaria

Burkina Faso

BurundiCabo Verde

Cambodia

Cameroon

CanadaCentral African Republic

Chad

Chile

China

Colombia Congo

Congo

Costa Rica

Cote d’Ivoire

Croatia

Cuba

Cyprus

Czech RepublicDenmark

Djibouti

Dominican Republic

Ecuador

Egypt

El Salvador

Eritrea

Estonia

Ethiopia

Finland France

Gabon

Gambia

Georgia

Germany

Ghana

Greece

Guatemala

GuineaGuinea-Bissau

HaitiHonduras

Hungary

Iceland

India

Indonesia

Iran

Iraq

Ireland

Israel

Italy

Jamaica

Japan

Jordan

KazakhstanKenya

Korea

Korea

Kosovo

Kuwait

Kyrgyz Republic

Lao PDR

Latvia

Lebanon

Lesotho

Liberia

Libya

Lithuania

Luxembourg

Macedonia Madagascar

Malawi

Malaysia

Mali

Malta

Mauritania

Mauritius MexicoMoldova

Mongolia

Montenegro

MoroccoMozambique

Myanmar

Namibia

Nepal

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Norway

Oman Pakistan

PanamaPapua New Guinea

Paraguay

Peru

Philippines

PolandPortugal

Puerto Rico

Qatar

Romania Russian Federation

Rwanda

Sao Tome and Principe

Saudi Arabia

Senegal

Serbia

Sierra Leone

Slovak RepublicSlovenia

Somalia

South Africa

South Sudan

Spain

Sri Lanka

Sudan

Suriname

Swaziland

Sweden

Switzerland

Syrian Arab Republic

Taiwan

TajikistanTanzania

Thailand

Timor-Leste

Togo

Tunisia TurkeyTurkmenistan

Uganda

Ukraine

United Arab Emirates

United Kingdom

United States

Uruguay

Uzbekistan

Venezuela

Vietnam

Virgin Islands (U.S.)

West Bank and Gaza

YemenZambiaZimbabwe

R2=0.051 b=-0.2±0.13

10−1 100 101

Recent road stock growth rate (2000-13)

0

2

4

6

8

10

SN

Di

(20

00

-13

)

Accra

Addis Ababa

Ahmedabad

Ahvaz

Alexandria

Algiers

Anqing

Antwerp

ArushaAstrakhan

Auckland

Bacolod

Baghdad

Baku

Bamako

Bangkok

Beijing

Beira

Belgaum

Belgrade

Belo Horizonte

Berezniki

Berlin

Bicheng

Bogota

Budapest

Buenos Aires

Bukhara

Busan

Cabimas

Cairo

Caracas

Cebu City

ChangzhiChangzhouChengdu

Chengguan

Cheonan

Chicago

CirebonCleveland

Cochabamba

Coimbatore

Cordoba

Culiacan

Curitiba

Dhaka

Dzerzhinsk

Florianopolis

Fukuoka

Gainesville

GaoyouGombe

Gomel

Gorgan

Guadalajara

Guangzhou

Guatemala City

Guixi

Gwangju

HaikouHalle

Hangzhou

Hindupur

Ho Chi Minh City

Holguin

Hong Kong

Houston

HyderabadIbadan

Ilheus

IpohIstanbul

JaipurJalna

Jequie

Jinan

Jinju

Johannesburg

Kabul

Kaiping

Kairouan

Kampala

Kanpur

Karachi

Kaunas

Kayseri

Khartoum

Kigali

Killeen

Kinshasa

Kolkata

Kozhikode

Lagos

Lahore

Lausanne

Le Mans

Leon

Leshan

London

Los Angeles

LuandaLubumbashi

Madrid

Malatya

Malegaon

Manchester

Manila

Marrakesh

Medan

Mexico City

Milan

Minneapolis

Modesto

Montreal

Moscow

Mumbai

MyeikNakuru

Ndola

New York

NikolaevOkayama

Oldenburg

Osaka

Oyo

Palembang

PalermoPalmas

Parepare

Paris

PematangsiantarPhiladelphia

Pingxiang

Pokhara

Port Elizabeth

Portland

Pune

Pyongyang

Qingdao

Qom

Quito

Rajshahi

Raleigh

Rawang

Reynosa

Ribeirao Preto

Riyadh

RovnoSaidpur

Saint Petersburg

San Salvador

Sana

Santiago

Sao Paulo

SeoulShanghai

Sheffield

Shenzhen

Shymkent

Sialkot

Singapore

SingrauliSitapur

Springfield

Suining

Suva

Sydney

Taipei

Tangshan

Tashkent

Tebessa

Tehran

Tel Aviv

Thessaloniki

Tianjin

Tijuana

Tokyo

ToledoTyumen

Ulaanbaatar

Valledupar

Victoria

Vienna

Vijayawada

Vinh Long

Warsaw

WuhanXingping

Xucheng

Yamaguchi

Yanggu

YiyangYuchengYulin

ZhengzhouZhuji

Zunyi

Zwolle

R2=0.1 b=-0.44±0.18

10−1 100

Recent road stock growth rate (2000-13)

2

4

6

8

10

SN

Di

(20

00

-13

)

Afghanistan

Albania

AlgeriaAngola

Argentina

Armenia

AustraliaAustria

Azerbaijan

Bahrain

Bangladesh

Barbados

Belarus

Belgium

Belize

Benin

Bhutan

Bolivia

Bosnia and Herzegovina

Botswana

Brazil

Brunei Darussalam

Bulgaria

Burkina Faso

BurundiCabo Verde

Cambodia

Cameroon

Canada Central African Republic

Chad

Chile

China

Colombia Congo

Congo

Costa Rica

Cote d’Ivoire

Croatia

Cuba

Cyprus

Czech RepublicDenmark

Djibouti

Dominican Republic

Ecuador

Egypt

El Salvador

Eritrea

Estonia

Ethiopia

FinlandFrance

Gabon

Gambia

Georgia

Germany

Ghana

Greece

Guatemala

GuineaGuinea-Bissau

Haiti

Honduras

Hungary

Iceland

India

Indonesia

Iran

Iraq

Ireland

Israel

Italy

Jamaica

Japan

Jordan

Kazakhstan Kenya

Korea

Korea

Kosovo

Kuwait

Kyrgyz Republic

Lao PDR

Latvia

Lebanon

Lesotho

Liberia

Libya

Lithuania

Luxembourg

MacedoniaMadagascar

Malawi

Malaysia

Mali

Malta

Mauritania

MauritiusMexicoMoldova

Mongolia

Montenegro

Morocco

Mozambique

Myanmar

Namibia

Nepal

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Norway

Oman Pakistan

PanamaPapua New Guinea

Paraguay

Peru

Philippines

Poland

Portugal

Puerto Rico

Qatar

RomaniaRussian Federation

Rwanda

Sao Tome and Principe

Saudi Arabia

Senegal

Serbia

Sierra Leone

Slovak RepublicSlovenia

Somalia

South Africa

South Sudan

Spain

Sri Lanka

Sudan

Suriname

Swaziland

Sweden

Switzerland

Syrian Arab Republic

Taiwan

TajikistanTanzania

Thailand

Timor-Leste

Togo

Tunisia TurkeyTurkmenistan

Uganda

Ukraine

United Arab Emirates

United Kingdom

United States

Uruguay

Uzbekistan

Venezuela

Vietnam

Virgin Islands (U.S.)

West Bank and Gaza

Yemen

ZambiaZimbabwe

R2=0.036 b=-0.34±0.27

Figure S10: Rate of road growth and SNDi. Relationships between recently-constructed(2000–2013) street-network sprawl and (top row) recent additions to road stock or (bottom row)recent growth rate of road stock. Cities are shown on the left, and countries on the right. Coefficients(b) from OLS fits are shown with 95% confidence range. Colors in the lower panels are defined by theabscissa of the upper ones.

27

F.5 Countries ranked by urban street-network sprawl (SNDi) of recentroad development

28

Table S1: Countries listed in order of recent urban street-network sprawl (SNDi). Countries with large (>20M) popula-tion are listed in bold.

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

Puerto Rico 4.9 7.7 8.9 9.4 6.5 2.7 2.4 2.3 2.3 2.5 54k 47k 12k 8.2k 120kVirgin Islands(U.S.)

4.9 7.9 8.1 8.6 7.4 2.8 2.4 2.4 2.4 2.5 1.1k 2.5k 880 690 5.3k

Trinidad and To-bago

6.1 7.4 8.5 6.2 2.5 2.3 2.3 2.5 28k 750 470 30k

Ireland 5.0 6.4 7.7 7.6 5.9 2.7 2.5 2.4 2.4 2.6 57k 22k 17k 19k 120kBarbados 6.1 7.2 7.6 6.4 2.5 2.4 2.3 2.4 9.3k 960 2k 13kGabon 5.5 5.8 5.8 7.2 5.9 2.6 2.5 2.5 2.4 2.5 3.3k 8.1k 2.3k 500 15kLiberia 6.2 7.7 7.6 7.1 6.9 2.6 2.4 2.4 2.4 2.5 13k 2.4k 2.4k 3k 22kPanama 3.2 4.7 6.1 6.8 4.6 3.0 2.8 2.6 2.5 2.8 15k 9.3k 7.1k 4.8k 37kMontenegro 3.4 5.0 5.9 6.7 4.5 2.9 2.6 2.5 2.3 2.7 3.7k 3.7k 310 340 8.6kLebanon 2.1 5.5 6.3 6.7 3.9 3.0 2.6 2.5 2.5 2.7 17k 18k 4k 3.9k 45kJamaica 5.2 6.3 6.3 6.6 5.6 2.6 2.5 2.5 2.5 2.6 19k 5.5k 3.8k 3.2k 33kPapua New Guinea 6.0 5.5 7.1 6.6 6.5 2.6 2.6 2.4 2.7 2.6 3.7k 870 270 250 6.2kKosovo 5.5 6.8 7.1 6.6 6.4 2.5 2.3 2.3 2.4 2.3 14k 18k 2.3k 3.5k 41kCyprus 2.8 4.0 5.6 6.5 4.2 2.8 2.7 2.6 2.5 2.7 6.7k 26k 4.8k 2.9k 41kBrunei Darussalam 6.1 7.4 7.0 6.5 6.7 2.5 2.3 2.4 2.5 2.4 6.2k 5.3k 1.5k 610 14kGuatemala 3.2 4.7 6.1 6.5 4.6 3.0 2.9 2.7 2.7 2.9 32k 41k 12k 11k 110kBahamas, The 4.8 4.5 6.2 6.3 4.8 2.7 2.8 2.6 2.6 2.7 4.8k 3.1k 630 320 9.2kThailand 4.0 4.7 4.8 6.3 4.9 2.8 2.8 2.7 2.6 2.7 140k 470k 74k 120k 920kSierra Leone 4.5 6.6 6.0 6.2 5.7 2.7 2.5 2.5 2.5 2.6 8.7k 3.2k 2.6k 9k 25kPhilippines 4.5 5.3 5.6 6.1 5.4 3.0 2.8 2.8 2.8 2.8 130k 200k 51k 59k 500kMacedonia, FYR 4.0 4.7 5.3 6.1 4.6 2.7 2.6 2.5 2.4 2.6 9k 24k 1.3k 800 36kNorway 4.0 4.8 5.7 6.1 4.6 2.9 2.7 2.6 2.6 2.8 62k 57k 10k 19k 170kMadagascar 4.1 4.6 7.3 6.0 5.3 3.1 2.8 2.6 2.6 2.7 5.8k 8.7k 1.6k 2.1k 39kSri Lanka 3.8 5.9 5.4 5.9 5.5 2.7 2.5 2.6 2.5 2.5 22k 78k 8k 26k 150kIndonesia 5.3 4.4 5.3 5.8 5.1 2.7 2.7 2.7 2.6 2.7 660k 470k 140k 220k 1.6MKorea, Dem.People’s Rep.

5.1 6.1 5.1 5.8 5.4 2.6 2.5 2.6 2.5 2.6 39k 7.2k 2.2k 2.8k 58k

Serbia 3.0 4.3 5.3 5.8 3.8 2.9 2.7 2.5 2.5 2.8 56k 32k 4.1k 4.7k 100kBosnia and Herze-govina

3.9 5.3 4.9 5.7 4.8 2.8 2.6 2.6 2.5 2.6 19k 17k 1.7k 2k 42k

29

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

United States 2.9 4.6 6.2 5.7 3.9 3.0 2.8 2.6 2.7 2.9 5.5M 2.7M 980k 1.1M 11MMalaysia 4.5 5.6 5.2 5.7 4.9 2.9 2.7 2.8 2.8 2.8 270k 49k 47k 45k 420kAlbania 4.7 4.6 5.4 5.7 4.9 2.6 2.7 2.6 2.5 2.6 8.3k 21k 5.6k 2.6k 41kCosta Rica 4.4 5.1 5.1 5.7 4.7 2.8 2.7 2.6 2.6 2.7 35k 17k 3.9k 2.6k 61kEl Salvador 3.4 4.3 5.1 5.7 4.3 2.9 2.8 2.7 2.6 2.8 16k 30k 10k 6.6k 66kWest Bank andGaza

2.9 4.4 5.2 5.4 4.5 2.9 2.6 2.6 2.5 2.6 8.3k 29k 9.4k 6.2k 55k

New Zealand 3.7 5.6 5.2 4.0 2.8 2.6 2.7 2.8 75k 4.9k 5k 96kGhana 3.7 4.4 5.0 5.2 4.6 2.8 2.7 2.6 2.6 2.7 39k 25k 26k 36k 130kSlovenia 4.2 5.5 5.5 5.1 4.5 2.7 2.5 2.6 2.6 2.6 40k 16k 1.1k 2.5k 61kBangladesh 4.2 5.1 5.1 5.1 5.3 2.7 2.7 2.6 2.6 2.6 16k 36k 7.7k 26k 160kVenezuela, RB 3.3 4.1 5.1 3.4 3.0 2.9 2.8 3.0 240k 23k 15k 290kLibya 1.7 2.4 4.1 5.1 2.3 3.1 3.0 2.8 2.7 3.0 46k 25k 5.2k 8.5k 86kGuinea 2.5 3.6 4.0 5.0 4.0 3.0 2.9 2.8 2.7 2.8 13k 12k 8k 14k 52kCameroon 3.7 3.7 5.0 5.0 4.5 2.9 2.8 2.6 2.6 2.7 36k 34k 34k 28k 150kHonduras 2.3 3.3 3.8 4.9 3.4 3.2 3.1 3.0 2.8 3.1 23k 21k 18k 9.4k 75kCongo, Rep. 2.0 2.7 3.8 4.9 2.7 3.2 3.0 2.9 2.7 3.1 33k 6k 5.5k 8.1k 53kUkraine 2.6 3.3 4.3 4.9 3.4 3.0 2.9 2.7 2.6 2.8 230k 370k 37k 28k 760kSlovak Republic 2.8 4.2 5.1 4.8 3.5 2.9 2.6 2.5 2.6 2.8 58k 39k 3.3k 5k 110kSuriname 3.1 4.9 4.8 3.2 2.9 2.7 2.7 2.9 8.4k 270 570 9.4kRomania 2.7 3.7 4.6 4.7 3.4 2.9 2.7 2.6 2.6 2.7 150k 160k 17k 24k 370kSwitzerland 1.8 3.8 4.7 4.7 2.6 3.1 2.8 2.7 2.6 3.0 120k 68k 6.2k 7k 210kUnited Kingdom 3.8 5.2 5.8 4.7 4.1 2.7 2.5 2.5 2.6 2.7 1.3M 240k 42k 44k 1.6MGeorgia 3.8 4.1 5.1 4.7 4.1 2.7 2.7 2.5 2.6 2.7 54k 27k 2.8k 2.2k 95kRussian Federa-tion

2.4 3.4 4.4 4.7 3.7 3.1 2.9 2.8 2.8 2.9 620k 600k 140k 100k 1.9M

Cote d’Ivoire 2.8 2.8 4.2 4.7 3.3 3.0 2.9 2.7 2.6 2.9 51k 9.6k 7k 8.4k 79kBahrain 2.1 2.7 3.5 4.7 2.5 3.1 3.0 2.9 2.9 3.0 17k 11k 1.6k 690 30kArmenia 3.4 3.8 3.9 4.7 3.8 2.9 2.8 2.8 2.7 2.8 21k 17k 2.6k 1.4k 46kHaiti 3.6 4.6 5.1 4.6 4.6 3.0 2.7 2.7 2.7 2.7 10k 12k 3.4k 7.1k 40kVietnam 3.9 4.2 4.4 4.6 4.4 2.8 2.7 2.8 2.8 2.8 67k 99k 28k 68k 290kLao PDR 3.3 3.7 4.5 4.4 4.0 2.8 2.7 2.7 2.7 2.7 4.7k 4.8k 2.4k 2.5k 18kIsrael 2.0 3.2 4.4 4.4 3.3 3.1 2.9 2.8 2.8 2.9 17k 53k 16k 12k 99kNicaragua 1.6 2.5 3.7 4.4 2.8 3.3 3.1 2.9 2.8 3.0 17k 14k 7.6k 6.2k 49kCroatia 3.1 4.4 5.1 4.3 3.7 2.9 2.6 2.6 2.7 2.7 46k 32k 2.7k 2.7k 87kAzerbaijan 4.8 4.5 4.4 4.3 4.6 2.6 2.7 2.7 2.7 2.6 39k 53k 7k 20k 130k

30

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

India 3.2 3.3 3.8 4.3 3.7 2.9 2.9 2.8 2.7 2.8 510k 740k 320k 550k 2.4MBulgaria 1.6 1.7 4.1 4.2 1.8 3.1 3.0 2.7 2.7 3.0 98k 110k 4.2k 4k 230kUganda 4.2 3.4 3.9 4.2 4.7 2.7 2.8 2.8 2.7 2.6 11k 4.4k 11k 15k 120kSouth Africa 3.0 3.8 3.7 4.2 3.6 3.0 2.9 2.9 2.8 2.9 170k 240k 110k 110k 660kAustria 2.5 3.4 4.6 4.2 2.9 3.0 2.8 2.7 2.7 2.9 150k 76k 13k 13k 260kLithuania 1.6 2.7 3.7 4.2 2.9 3.2 3.0 2.8 2.8 2.9 16k 15k 3.6k 1.9k 57kNepal 3.8 3.2 2.9 4.1 4.0 2.8 2.8 2.9 2.7 2.7 19k 9.6k 3.6k 5.3k 63kDenmark 2.5 3.1 3.6 4.1 2.9 2.9 2.8 2.8 2.7 2.9 130k 77k 9.7k 19k 240kJordan 2.8 2.6 3.5 4.1 3.0 3.0 3.0 2.8 2.8 2.9 5.4k 56k 13k 11k 93kItaly 2.2 3.5 4.0 4.1 2.8 3.0 2.8 2.8 2.7 2.9 790k 580k 60k 74k 1.5MAustralia 2.7 4.5 5.3 4.1 3.5 3.0 2.7 2.6 2.8 2.9 380k 220k 42k 64k 710kPoland 1.9 3.0 3.9 4.0 2.6 3.1 2.9 2.7 2.7 2.9 230k 210k 25k 39k 530kTanzania 3.0 2.4 3.8 4.0 3.5 2.9 2.9 2.8 2.8 2.8 25k 13k 20k 31k 110kFrance 2.3 3.6 3.9 4.0 2.9 3.0 2.8 2.7 2.7 2.9 1.1M 780k 140k 150k 2.2MCzech Republic 2.0 3.2 3.7 4.0 2.6 3.0 2.8 2.7 2.7 2.9 150k 89k 7.4k 15k 270kMexico 1.7 2.4 3.1 3.9 2.6 3.2 3.1 3.0 3.0 3.1 430k 760k 240k 280k 1.9MMongolia 3.4 4.2 4.6 3.9 3.7 2.8 2.7 2.8 2.8 2.8 1.5k 6.5k 830 3.4k 27kFinland 2.0 2.8 3.5 3.9 3.1 3.2 3.0 2.9 2.9 2.9 54k 40k 11k 15k 160kMauritius 2.9 3.9 3.0 2.9 2.8 2.8 17k 3k 21kEstonia 1.5 2.4 4.1 3.9 2.6 3.2 3.0 2.8 2.9 3.0 12k 9.6k 1.2k 1.5k 33kHong Kong SAR,China

1.1 2.1 1.5 3.9 1.4 3.3 3.2 3.4 3.1 3.3 7.5k 3k 530 130 11k

Uzbekistan 3.2 3.1 3.9 3.9 3.5 2.9 2.9 2.8 2.7 2.8 78k 65k 13k 18k 210kCambodia 2.3 2.8 3.2 3.9 3.3 3.1 2.9 2.9 2.9 2.9 2.5k 11k 4.4k 12k 41kTajikistan 3.8 3.3 3.5 3.8 4.0 2.8 2.9 2.8 2.8 2.8 9.9k 8.3k 1.5k 4.1k 33kDominican Repub-lic

1.7 3.1 2.8 3.7 2.7 3.2 3.0 3.0 2.9 3.0 35k 43k 20k 12k 110k

Malawi 3.8 3.6 3.9 3.7 3.7 2.8 2.8 2.7 2.7 2.8 11k 6.1k 6.6k 23k 58kPortugal 2.8 3.2 3.8 3.7 3.1 3.0 2.9 2.8 2.8 2.9 170k 140k 45k 33k 390kMoldova 2.3 2.7 3.1 3.7 2.6 2.9 2.9 2.8 2.7 2.9 29k 54k 2.6k 1.4k 88kMyanmar 2.8 2.8 2.0 3.7 3.0 3.0 3.0 3.1 2.9 2.9 30k 49k 29k 35k 180kHungary 1.6 2.8 3.5 3.6 2.3 3.1 2.9 2.8 2.7 2.9 120k 86k 8.8k 11k 230kSweden 2.0 3.1 3.6 3.6 2.6 3.1 2.9 2.9 2.9 3.0 170k 61k 11k 15k 300kGreece 0.9 2.4 3.2 3.6 1.5 3.2 2.9 2.8 2.8 3.1 200k 140k 13k 11k 380kMalta 1.0 1.9 3.1 3.6 1.7 3.3 3.1 2.9 2.8 3.1 4.5k 7.2k 290 580 13kKenya 4.5 4.1 4.9 3.6 4.1 2.8 2.8 2.7 2.8 2.7 3.2k 12k 7.5k 20k 58k

31

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

Afghanistan 5.6 3.8 5.3 3.6 4.8 2.6 2.8 2.6 3.0 2.7 6k 13k 2.1k 8.3k 97kCentral African Re-public

2.9 3.7 3.9 3.6 3.6 3.0 3.0 3.0 3.0 3.0 5.3k 1.3k 2.3k 1.5k 12k

Canada 2.2 3.2 3.7 3.5 2.7 3.1 3.0 2.9 3.0 3.0 370k 190k 49k 66k 720kRwanda 2.5 3.4 3.5 3.5 3.7 3.0 2.9 2.9 2.9 2.8 470 3.6k 1.2k 7.3k 15kBelgium 1.6 2.8 3.4 3.5 2.2 3.1 2.9 2.8 2.7 3.0 160k 93k 15k 18k 290kLatvia 1.5 2.3 3.7 3.5 2.6 3.2 3.0 2.8 2.9 2.9 14k 13k 2.2k 1.5k 46kKazakhstan 2.9 2.5 3.2 3.5 3.0 3.0 3.1 3.0 3.0 3.0 33k 64k 9.4k 15k 150kZambia 2.4 2.9 3.3 3.5 3.3 3.0 3.0 2.9 2.9 2.9 27k 9.7k 4k 15k 73kCongo, Dem.Rep.

3.0 3.2 3.3 3.5 3.6 3.1 3.1 3.0 2.9 2.9 86k 55k 36k 63k 290k

Pakistan 2.2 2.8 3.6 3.5 2.8 3.1 2.9 2.9 2.9 3.0 78k 78k 23k 53k 250kNigeria 2.5 3.4 4.2 3.4 3.5 2.9 2.8 2.7 2.8 2.8 160k 260k 110k 430k 1.3MChile 2.0 2.6 3.2 3.4 2.6 3.1 3.0 2.9 2.9 3.0 130k 80k 46k 51k 320kOman 3.7 2.8 3.5 3.4 3.4 2.7 3.0 2.8 2.9 2.8 7.7k 11k 8.4k 7.6k 53kColombia 1.6 -0.1 2.8 3.4 1.8 3.2 3.3 3.0 2.9 3.2 290k 120 22k 23k 350kZimbabwe 2.5 3.0 3.4 3.4 3.1 3.0 2.9 2.9 2.9 2.9 8k 25k 14k 9.6k 66kEgypt, ArabRep.

3.5 3.1 3.4 3.3 3.4 2.8 2.9 2.8 2.9 2.9 240k 110k 31k 54k 460k

Togo 2.1 2.0 2.3 3.3 2.6 3.0 3.0 3.0 2.8 2.9 14k 12k 8.1k 11k 53kCuba 0.9 2.2 2.7 3.3 1.4 3.3 3.0 2.9 2.8 3.1 60k 37k 3k 2.3k 100kLuxembourg 2.0 2.9 3.2 3.2 2.4 3.1 2.9 2.9 2.8 3.0 8.3k 5.3k 1.3k 910 16kGermany 1.9 3.0 3.2 3.2 2.3 3.1 2.9 2.8 2.8 3.0 1.4M 640k 100k 110k 2.3MBenin 1.3 2.1 2.3 3.2 2.5 3.1 3.0 3.0 2.8 2.9 12k 18k 15k 17k 68kCabo Verde 2.6 3.2 2.8 3.1 3.0 3.1 3.3k 480 5.7kLesotho 3.5 2.8 2.7 3.1 3.4 2.8 2.9 2.9 2.9 2.8 800 8.4k 9.1k 12k 43kYemen, Rep. 3.3 2.2 3.4 3.1 3.3 3.0 3.1 3.0 3.0 3.0 4k 13k 3k 19k 48kQatar 2.4 3.0 3.2 3.1 2.9 3.1 3.0 3.0 3.0 3.0 7.5k 17k 1.6k 4.2k 33kBurundi 2.4 3.2 3.7 3.1 3.3 3.2 2.9 2.9 2.9 2.9 1.5k 2.7k 2.7k 6.9k 20kKyrgyz Republic 2.9 3.2 3.0 3.1 3.2 2.9 2.9 2.9 2.9 2.9 11k 17k 5.3k 13k 52kNamibia 2.0 2.2 3.4 3.1 2.8 3.1 3.1 2.9 3.0 3.0 2.3k 5k 4k 3.9k 18kIran, IslamicRep.

3.5 2.7 3.0 2.9 3.0 2.7 2.9 2.9 2.9 2.9 210k 370k 79k 200k 950k

Syrian Arab Re-public

1.3 1.8 2.3 2.9 1.7 3.1 3.1 2.9 2.9 3.0 81k 70k 12k 17k 190k

Brazil 1.8 2.2 2.3 2.9 2.1 3.2 3.1 3.1 3.0 3.1 1.6M 820k 430k 280k 3.4M

32

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

Saudi Arabia 2.0 2.3 2.8 2.8 2.9 3.2 3.1 3.1 3.1 3.1 58k 160k 40k 66k 420kChina 2.9 3.0 2.7 2.8 3.1 3.0 3.0 3.1 3.1 3.0 340k 520k 220k 460k 1.9MIceland 1.9 3.3 2.8 2.5 3.1 2.9 3.0 3.0 5.6k 960 2.4k 12kUnited Arab Emi-rates

1.7 1.2 2.2 2.8 1.9 3.2 3.2 3.1 3.1 3.1 40k 11k 5.4k 9.3k 90k

Algeria 2.5 2.1 2.1 2.8 2.3 3.0 3.1 3.1 3.0 3.0 35k 240k 63k 57k 410kTunisia 1.7 2.0 1.9 2.7 1.9 3.1 3.0 3.1 3.0 3.0 37k 83k 21k 9.5k 150kEthiopia 2.8 2.5 2.4 2.7 2.7 3.0 3.0 3.1 3.1 3.0 6.7k 23k 21k 99k 190kAngola 2.8 3.4 3.4 2.6 2.9 3.0 2.9 3.0 3.1 3.0 27k 24k 11k 59k 150kTurkey 1.6 1.5 2.1 2.6 1.9 3.1 3.1 3.1 3.0 3.1 260k 510k 150k 100k 1.1MEcuador 2.0 2.5 2.6 2.2 3.1 3.1 3.1 3.1 150k 12k 17k 210kBelarus 1.8 2.2 2.7 2.6 2.8 3.2 3.0 3.0 3.0 2.9 39k 34k 8.6k 6.7k 160kSpain 1.2 2.1 2.6 2.6 1.8 3.2 3.0 3.0 3.0 3.1 580k 470k 130k 110k 1.4MKuwait 2.7 4.1 3.1 2.5 3.0 3.2 3.2 3.2 3.2 3.2 20k 10k 2k 2.5k 37kNetherlands 1.5 2.2 2.2 2.5 1.9 3.2 3.1 3.1 3.0 3.1 260k 140k 52k 54k 500kTurkmenistan 1.9 2.3 2.1 2.4 2.4 3.2 3.1 3.1 3.1 3.1 5k 7.1k 2.7k 3.6k 26kKorea, Rep. 0.9 1.4 2.0 2.4 1.4 3.2 3.2 3.1 3.1 3.2 160k 120k 37k 37k 370kGambia, The 0.5 1.5 1.6 2.3 1.4 3.2 3.1 3.1 2.9 3.1 5.7k 5.1k 4.8k 1.6k 19kIraq 2.1 1.8 2.0 2.2 2.0 3.1 3.1 3.0 3.0 3.1 28k 150k 21k 110k 320kBotswana 1.8 2.3 2.7 2.2 2.2 3.0 3.0 2.9 3.0 3.0 4.3k 20k 14k 11k 71kParaguay 1.1 1.5 1.6 2.1 1.4 3.3 3.2 3.2 3.2 3.2 34k 21k 9.8k 7.7k 89kMorocco 1.5 1.9 1.9 2.0 1.9 3.2 3.1 3.1 3.1 3.1 81k 86k 29k 59k 280kUruguay -0.3 0.5 1.7 2.0 0.4 3.5 3.3 3.1 3.0 3.4 24k 29k 6.9k 2.9k 66kMali 0.7 1.0 1.5 1.9 1.5 3.3 3.2 3.2 3.1 3.2 7.8k 33k 8.8k 28k 89kNiger 0.2 0.6 1.3 1.8 1.4 3.3 3.3 3.2 3.2 3.2 4.2k 8.1k 4.5k 17k 46kSudan 0.6 1.1 1.6 1.8 1.6 3.3 3.4 3.3 3.3 3.3 64k 52k 12k 92k 320kJapan 1.1 1.5 2.2 1.8 1.3 3.2 3.1 3.1 3.1 3.1 2.8M 990k 130k 110k 4MTaiwan, China 1.1 2.1 2.0 1.8 1.3 3.2 3.1 3.2 3.2 3.2 130k 12k 7.9k 7.5k 160kMozambique 2.1 1.2 1.7 1.7 1.8 3.1 3.1 3.1 3.0 3.0 16k 33k 16k 38k 130kSenegal 0.9 1.2 1.3 1.6 1.5 3.3 3.3 3.2 3.1 3.2 30k 47k 19k 31k 150kSouth Sudan 0.9 0.6 2.4 1.6 1.9 3.4 3.4 3.4 3.3 3.3 2k 1.2k 1.1k 11k 21kEritrea 3.6 2.6 3.0 1.6 2.6 3.0 3.1 3.1 3.2 3.1 2.2k 1.7k 630 2.4k 18kPeru 1.5 0.8 1.5 1.5 1.5 3.2 3.3 3.2 3.2 3.2 190k 11k 9.1k 23k 280kArgentina 0.2 0.5 1.1 1.5 0.6 3.5 3.4 3.3 3.3 3.4 300k 310k 84k 69k 820kBurkina Faso 0.6 1.3 1.4 1.5 1.5 3.3 3.2 3.2 3.1 3.1 16k 10k 12k 28k 74kBelize 1.4 2.1 2.8 1.5 2.2 3.1 3.1 3.0 3.1 3.0 2.7k 1.6k 2.1k 620 9k

33

Sprawl (SNDi) Nodal degree N(nodes)<1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock <1975 75–89 90–99 2000–13 Stock

Bolivia 1.3 1.1 1.0 1.2 1.5 3.3 3.3 3.3 3.3 3.2 42k 34k 26k 36k 170kSingapore 1.9 1.3 1.1 1.9 3.3 3.4 3.5 3.3 17k 380 170 18kChad 0.0 1.2 1.0 1.1 1.2 3.4 3.4 3.3 3.3 3.3 5.6k 3.1k 4.9k 12k 34kSomalia 0.9 0.9 1.6 1.0 1.1 3.4 3.3 3.3 3.3 3.3 16k 16k 4.7k 19k 60kMauritania 0.1 0.2 0.5 0.5 0.6 3.4 3.3 3.3 3.3 3.3 5.8k 3.5k 2.1k 4.1k 26kMaldives -0.6 -0.3 0.7 3.3 3.3 3.0 440 280 7k

34

F.6 Cities ranked by urban street-network sprawl (SNDi) of recent roaddevelopment

35

Table S2: Cities listed in order of their recent urban street-network sprawl (SNDi).

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Suva, Fiji 3.5 8.4 10.4 8.2 2.9 2.3 2.1 2.4 150 1.4k 150 1.7kCaracas, Venezuela 4.9 10.4 10.1 5.3 2.8 2.3 2.3 2.8 11k 770 540 12kBangkok, Thailand 6.8 7.6 8.1 7.3 2.5 2.5 2.4 2.5 74k 30k 46k 150kGuatemala City, Guatemala 3.8 5.6 7.5 4.5 3.0 2.8 2.5 2.9 22k 7.3k 4.7k 34kCebu City, Philippines 6.1 6.9 7.3 6.6 2.6 2.5 2.6 2.6 5.7k 2.3k 4.5k 12kGorgan, Iran 3.6 5.8 6.7 3.9 2.7 2.5 2.4 2.7 3.3k 470 240 4kLos Angeles, United States 3.6 6.2 6.3 3.9 2.9 2.6 2.6 2.9 250k 26k 9.7k 290kRaleigh, United States 4.8 5.4 6.3 5.6 2.8 2.7 2.5 2.6 11k 21k 19k 51kPalembang, Indonesia 4.7 5.6 6.3 5.2 2.6 2.5 2.5 2.6 15k 19k 3.9k 39kNew York, United States 2.9 5.2 6.3 3.1 3.0 2.7 2.5 2.9 320k 42k 3.5k 360kSan Salvador, El Salvador 4.4 7.1 6.1 5.0 2.8 2.5 2.6 2.7 11k 1.7k 4.7k 17kBelgrade, Serbia 2.9 6.6 6.0 4.2 3.0 2.3 2.4 2.7 9.1k 2.2k 4k 15kTijuana, Mexico 4.3 3.6 6.0 4.4 2.9 3.0 2.7 2.9 15k 9.2k 8.5k 33kFlorianopolis, Brazil 2.9 5.9 5.9 4.1 2.9 2.5 2.6 2.8 7.2k 2.5k 3.2k 13kPune, India 3.4 5.0 5.9 4.9 2.8 2.6 2.5 2.7 6.3k 15k 10k 31kSao Paulo, Brazil 2.1 4.9 5.8 2.5 3.1 2.8 2.6 3.0 150k 23k 5.5k 180kMedan, Indonesia 4.0 5.2 5.8 4.9 2.7 2.5 2.5 2.6 20k 29k 16k 64kGainesville, United States 3.0 6.4 5.6 3.8 3.1 2.7 2.8 3.0 3.9k 800 1.2k 5.8kJohannesburg, South Africa 2.9 4.1 5.5 3.8 3.0 2.9 2.8 2.9 63k 16k 30k 110kDhaka, Bangladesh 3.3 4.9 5.5 4.0 2.9 2.7 2.6 2.8 14k 5.5k 6.6k 26kKolkata, India 3.2 4.6 5.4 3.9 2.9 2.7 2.6 2.8 20k 10k 6.4k 36kPematangsiantar, Indonesia 3.2 4.6 5.4 3.7 2.9 2.8 2.6 2.9 1.5k 300 280 2.1kUlaanbaatar, Mongolia 4.1 4.8 5.3 4.5 2.8 2.6 2.6 2.7 5.9k 1.4k 3.1k 10kKampala, Uganda 4.9 5.6 5.3 5.1 2.6 2.5 2.6 2.6 8.4k 3.8k 3.2k 15kHouston, United States 2.9 5.1 5.3 3.9 3.1 2.8 2.8 2.9 88k 31k 45k 160kPhiladelphia, United States 2.3 4.9 5.2 2.8 3.1 2.8 2.7 3.0 120k 25k 15k 160kVictoria, Canada 2.9 4.6 5.2 3.3 2.9 2.7 2.8 2.9 6.6k 1.8k 450 8.8kHo Chi Minh City, Viet Nam 1.8 4.2 5.2 4.6 3.2 2.8 2.6 2.7 6.1k 13k 45k 64kManila, Philippines 3.9 5.1 5.1 4.4 3.0 2.9 2.9 2.9 62k 26k 29k 120kSheffield, United Kingdom 3.4 5.1 5.1 3.5 2.8 2.5 2.5 2.7 29k 2.5k 810 32kRawang, Malaysia 3.5 4.7 5.0 4.7 3.0 3.0 2.9 2.9 540 820 3.8k 5.1kPokhara, Nepal 3.2 4.5 5.0 3.6 2.7 2.6 2.5 2.7 1.8k 280 470 2.6kBaku, Azerbaijan 4.1 5.2 5.0 4.6 2.8 2.5 2.6 2.7 11k 7.4k 5.1k 23k

36

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Lagos, Nigeria 4.2 4.6 5.0 4.5 2.8 2.6 2.6 2.7 24k 22k 19k 65kVijayawada, India 2.8 4.1 4.9 3.2 3.0 2.7 2.6 2.9 6.9k 1.4k 730 9kSialkot, Pakistan 2.7 4.9 4.9 4.0 3.0 2.6 2.6 2.8 1.6k 2.3k 320 4.3kRajshahi, Bangladesh 1.4 3.7 4.9 3.9 3.1 2.7 2.6 2.7 130 1.6k 710 2.4kCirebon, Indonesia 2.7 4.2 4.9 4.5 2.9 2.7 2.7 2.7 1.1k 2.3k 7.9k 11kPortland, United States 3.0 4.9 4.8 3.4 3.0 2.7 2.7 2.9 51k 10k 6.1k 67kCleveland, United States 2.8 5.4 4.8 3.8 3.0 2.7 2.8 2.9 24k 9.5k 14k 47kTyumen, Russia 1.7 4.5 4.8 3.0 3.3 2.8 2.8 3.1 2.3k 920 1.2k 4.4kMexico City, Mexico 1.8 4.0 4.8 2.7 3.2 2.8 2.7 3.0 120k 32k 40k 190kAlgiers, Algeria 3.9 3.8 4.7 4.1 2.9 2.9 2.8 2.9 12k 10k 8.9k 31kMinneapolis, United States 2.8 4.6 4.7 3.3 3.1 2.8 2.7 3.0 63k 14k 11k 88kKaunas, Lithuania 2.2 5.3 4.7 2.7 3.2 2.7 2.8 3.1 3.2k 170 680 4kCoimbatore, India 3.5 4.2 4.7 3.9 2.8 2.8 2.7 2.8 7.2k 5.3k 3.4k 16kArusha, Tanzania 1.4 4.5 4.7 3.6 3.1 2.6 2.7 2.8 270 400 250 910Manchester, United Kingdom 4.0 5.2 4.6 4.0 2.7 2.5 2.6 2.7 71k 2.5k 220 74kSpringfield, United States 3.0 4.7 4.6 3.7 2.9 2.8 2.7 2.9 9.6k 7.1k 2.2k 19kMumbai, India 3.3 4.3 4.6 3.7 2.9 2.8 2.7 2.8 23k 2.5k 8.4k 34kAstrakhan, Russia 2.0 3.5 4.6 2.5 3.1 2.8 2.6 3.0 3.7k 2.4k 150 6.2kToledo, United States 2.1 4.2 4.6 2.8 3.2 2.9 2.8 3.0 9.9k 3.4k 2.8k 16kKozhikode, India 1.7 3.8 4.5 3.5 3.0 2.7 2.6 2.7 340 530 970 1.8kParepare, Indonesia 2.4 3.8 4.4 3.3 3.0 2.7 2.7 2.8 750 300 520 1.6kGuadalajara, Mexico 1.7 3.8 4.4 2.5 3.2 3.0 2.9 3.1 46k 8.8k 21k 75kChicago, United States 2.6 4.6 4.4 2.9 3.1 2.8 2.8 3.0 170k 30k 13k 210kJequie, Brazil 1.2 1.9 4.4 1.5 3.2 3.1 2.9 3.2 2.4k 840 240 3.4kSantiago, Chile 1.5 2.1 4.4 1.9 3.1 3.1 2.9 3.1 66k 14k 13k 93kCabimas, Venezuela 2.4 2.8 4.3 2.6 2.9 3.0 2.7 2.9 6.4k 1.3k 700 8.4kTashkent, Uzbekistan 2.7 4.3 4.3 3.3 3.0 2.8 2.7 2.9 17k 8.6k 4.1k 30kBacolod, Philippines 2.6 5.1 4.3 4.2 3.1 2.9 3.1 3.0 1.1k 1.7k 870 3.7kIpoh, Malaysia 3.6 4.4 4.3 3.8 2.9 2.8 2.9 2.9 11k 3.3k 1.5k 16kAhvaz, Iran 1.8 3.7 4.3 2.4 3.2 2.8 2.8 3.1 9.2k 2.3k 1.8k 13kKilleen, United States 2.7 3.5 4.3 3.4 3.0 2.9 2.8 2.9 4.1k 710 3.2k 8kSingrauli, India 4.7 4.0 4.3 4.2 2.8 2.9 2.9 2.9 220 780 420 1.4kAccra, Ghana 3.3 3.5 4.2 3.9 2.9 2.8 2.7 2.8 7.9k 18k 31k 57kSydney, Australia 3.0 5.2 4.2 3.3 2.9 2.7 2.9 2.9 61k 7.8k 7.8k 76kBogota, Colombia 1.2 2.0 4.2 1.3 3.2 3.1 2.8 3.2 44k 4.7k 1.1k 50kQuito, Ecuador 1.9 4.0 4.1 3.3 3.1 2.9 2.8 2.9 10k 6k 16k 32k

37

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Moscow, Russia 1.3 3.3 4.1 2.2 3.4 3.0 2.9 3.2 30k 11k 13k 54kIbadan, Nigeria 4.1 3.7 4.1 4.0 2.7 2.8 2.7 2.7 20k 12k 16k 47kBelo Horizonte, Brazil 1.5 3.7 4.1 1.8 3.2 2.9 2.9 3.2 42k 4.7k 2.8k 50kLondon, United Kingdom 3.3 4.3 4.1 3.4 2.8 2.7 2.7 2.8 150k 25k 2.8k 180kSaidpur, Bangladesh 2.7 4.0 3.2 2.8 2.6 2.8 270 19 130 420Istanbul, Turkey 1.0 1.6 4.0 1.5 3.2 3.2 2.8 3.2 76k 48k 21k 140kWarsaw, Poland 1.5 2.5 4.0 2.2 3.3 3.0 2.7 3.1 17k 6.9k 5.3k 29kCuritiba, Brazil 1.8 3.0 4.0 2.4 3.2 3.0 2.8 3.1 23k 15k 6.8k 45kSana, Yemen 1.1 2.5 4.0 1.5 3.2 3.0 2.8 3.1 9.2k 1.1k 1.1k 11kTehran, Iran 2.4 3.5 3.9 2.6 3.0 2.9 2.8 2.9 63k 11k 7.7k 82kHyderabad, India 2.6 3.1 3.8 3.1 2.9 2.9 2.8 2.9 53k 41k 43k 140kHalle, Germany 1.1 2.9 3.8 1.5 3.3 3.0 2.8 3.2 2.9k 960 180 4.1kModesto, United States 3.1 3.6 3.7 3.4 2.9 2.8 2.8 2.8 7.3k 3.2k 2.6k 13kRovno, Ukraine 2.3 3.1 3.7 3.0 3.1 2.8 2.8 2.9 930 250 1.3k 2.5kMilan, Italy 1.9 3.6 3.7 2.9 3.1 2.8 2.7 2.9 58k 48k 31k 140kLausanne, Switzerland 1.3 2.7 3.7 1.8 3.4 3.1 2.8 3.2 2.6k 1.1k 290 4.1kHaikou, China 2.8 2.6 3.7 3.0 3.1 3.1 2.9 3.0 1.2k 460 1k 2.6kHong Kong, China 0.7 1.7 3.7 0.9 3.4 3.3 3.0 3.4 5.1k 820 220 6.1kAuckland, New Zealand 3.2 4.0 3.7 3.3 2.9 2.9 3.0 2.9 17k 1.8k 1.7k 20kOsaka, Japan 1.2 2.8 3.7 1.3 3.2 3.0 2.9 3.2 160k 12k 2.7k 180kKabul, Afghanistan 3.6 3.7 3.6 3.6 2.8 2.8 2.8 2.8 12k 1.9k 7k 21kQom, Iran 1.9 2.3 3.6 2.2 3.1 3.1 2.9 3.0 7.2k 1.7k 2.2k 11kMalatya, Turkey 1.1 2.6 3.6 1.6 3.2 3.0 2.8 3.1 3.8k 730 830 5.3kJaipur, India 2.6 3.1 3.6 3.1 3.0 2.9 2.8 2.9 8.6k 19k 13k 40kNakuru, Kenya 1.8 3.5 3.5 2.5 3.1 2.9 2.8 3.0 820 180 650 1.6kShymkent, Kazakhstan 3.6 3.3 3.5 3.5 2.9 2.9 2.9 2.9 4.3k 830 4.4k 9.5kKigali, Rwanda 3.0 3.3 3.5 3.3 3.0 2.9 2.9 2.9 980 1.9k 1.7k 4.6kCuliacan, Mexico 0.7 2.0 3.5 1.5 3.4 3.2 3.0 3.3 8.2k 3.6k 3.5k 15kBeira, Mozambique 2.5 4.5 3.5 3.2 3.0 2.9 2.8 2.9 1.3k 110 2k 3.4kTangshan, China 2.9 3.1 3.4 3.3 3.0 3.0 2.8 2.9 1.7k 870 3.1k 5.6kTel Aviv, Israel 1.1 2.5 3.4 2.0 3.2 3.1 3.0 3.1 9.6k 14k 4.2k 28kHindupur, India 3.4 2.7 3.0 3.0 32 42 250 320Myeik, Myanmar 1.9 3.3 3.3 2.4 3.1 2.8 2.8 3.0 720 140 500 1.4kPalermo, Italy 2.2 6.4 3.3 2.6 3.0 2.5 2.8 2.9 7.1k 220 3.5k 11kMontreal, Canada 1.8 3.5 3.3 1.9 3.3 3.0 3.1 3.2 45k 2.4k 4.7k 52kPort Elizabeth, South Africa 2.3 4.2 3.3 2.7 3.0 2.8 3.0 3.0 9.1k 990 6.3k 16k

38

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Paris, France 1.8 3.0 3.3 2.0 3.1 2.9 2.8 3.1 120k 13k 11k 140kAlexandria, Egypt 0.7 2.3 3.3 1.1 3.3 3.0 2.9 3.2 8.2k 1.2k 1.8k 11kAntwerp, Belgium 1.5 2.8 3.2 1.9 3.2 2.9 2.8 3.1 16k 6.2k 1.1k 23kAhmedabad, India 2.5 3.5 3.2 2.7 3.0 2.8 2.9 2.9 8.9k 1.5k 1.5k 12kSaint Petersburg, Russia 0.5 3.4 3.2 1.9 3.6 2.9 3.0 3.3 9.6k 3.6k 6.8k 20kPalmas, Brazil 3.1 3.5 3.2 3.3 3.1 2.9 3.1 3.0 1.2k 3k 1.1k 5.3kLahore, Pakistan 2.3 2.8 3.2 2.5 3.0 2.9 3.0 3.0 25k 6k 8.7k 40kOyo, Nigeria 3.4 3.4 3.2 3.3 2.7 2.8 2.8 2.8 1.5k 780 2.3k 4.5kKinshasa, DR Congo 2.1 3.2 3.2 2.7 3.2 3.0 2.9 3.1 19k 13k 15k 46kShanghai, China 1.6 2.9 3.1 2.1 3.3 3.1 3.1 3.2 22k 10k 4.3k 36kZunyi, China 3.6 3.4 3.1 3.3 2.9 3.0 3.1 3.0 450 210 400 1.1kSeoul, South Korea 0.9 2.3 3.1 1.2 3.2 3.1 3.0 3.2 87k 23k 15k 130kGuixi, China 1.3 3.1 2.5 3.0 2.8 2.9 120 98 150 370Vienna, Austria 0.5 2.3 3.0 1.0 3.4 3.0 2.9 3.3 16k 5.9k 1.5k 23kTebessa, Algeria 2.0 2.6 3.0 2.2 3.1 3.1 2.9 3.1 2.5k 850 370 3.8kShenzhen, China 2.5 2.6 3.0 2.7 3.1 3.1 3.0 3.1 6.8k 10k 5.6k 23kReynosa, Mexico 1.9 2.9 3.0 2.2 3.2 3.2 3.0 3.2 7.5k 720 3k 11kOldenburg, Germany 2.0 3.0 3.0 2.2 3.2 3.0 2.9 3.1 2.5k 670 400 3.5kGuangzhou, China 1.8 2.8 2.9 2.8 3.2 3.0 3.0 3.0 5.6k 34k 33k 73kBaghdad, Iraq 2.1 3.3 2.9 2.2 3.1 2.8 2.9 3.1 45k 800 1.1k 47kGomel, Belarus 1.6 2.4 2.9 2.0 3.3 3.0 2.9 3.1 1.4k 1.1k 190 2.7kRiyadh, Saudi Arabia 1.7 2.7 2.9 2.1 3.2 3.1 3.1 3.1 50k 14k 26k 90kQingdao, China 1.6 1.0 2.8 2.4 3.2 3.2 3.0 3.0 2.7k 2.3k 14k 19kSingapore, Singapore 1.9 1.5 2.8 1.9 3.3 3.4 3.2 3.3 14k 2.3k 620 17kCochabamba, Bolivia 0.6 2.2 2.8 1.6 3.4 3.1 3.0 3.2 9.5k 6.2k 5.8k 21kCairo, Egypt 1.4 2.2 2.7 2.1 3.2 3.1 3.1 3.1 23k 5.1k 33k 62kCordoba, Argentina 0.6 2.3 2.7 1.0 3.4 3.2 3.1 3.3 22k 2.4k 4.5k 29kXingping, China 2.4 2.7 2.5 3.0 2.9 2.9 110 34 260 410Beijing, China 2.0 2.7 2.7 2.3 3.2 3.1 3.1 3.1 19k 4.6k 15k 39kBukhara, Uzbekistan 0.9 2.9 2.7 2.5 3.3 2.9 2.9 2.9 380 910 1.3k 2.6kBelgaum, India 1.2 2.1 2.7 2.2 3.1 3.1 3.0 3.1 560 740 1.5k 2.8kTaipei, China 0.4 1.9 2.6 1.2 3.4 3.1 3.0 3.2 15k 9.3k 7.7k 33kYulin, China 2.7 2.1 2.5 2.7 2.9 3.1 2.9 3.0 210 320 300 820Bicheng, China 0.2 0.4 2.5 1.7 3.3 3.3 3.1 3.1 150 130 630 910Zwolle, Netherlands 2.0 1.6 2.5 1.9 3.1 3.3 3.0 3.1 3.1k 470 200 3.7kTianjin, China 1.7 2.9 2.5 2.3 3.3 3.1 3.1 3.1 5k 1.2k 14k 20k

39

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Yucheng, China 3.0 2.8 2.5 2.8 2.9 2.8 2.9 2.9 310 290 250 850Hangzhou, China 1.1 1.9 2.5 2.0 3.4 3.3 3.1 3.2 2.7k 7.9k 11k 22kKayseri, Turkey 1.2 1.4 2.5 1.7 3.2 3.2 3.0 3.1 1.7k 6.6k 4.1k 12kChengdu, China 2.0 2.9 2.4 2.5 3.1 3.1 3.1 3.1 4.5k 6.7k 10k 21kYamaguchi, Japan 2.5 2.5 2.4 2.5 2.9 3.1 3.0 2.9 11k 850 240 12kNdola, Zambia 2.1 2.2 2.4 2.1 3.0 3.0 2.9 3.0 2.7k 980 450 4.2kWuhan, China 2.0 2.4 2.3 2.3 3.1 3.1 3.0 3.1 5.5k 2k 7.9k 15kLeshan, China 0.6 1.5 2.3 1.4 3.2 3.1 3.0 3.1 200 190 200 590Yiyang, China 1.1 1.9 2.3 1.8 3.3 3.2 3.0 3.1 270 200 260 730Karachi, Pakistan 1.8 1.4 2.3 1.8 3.2 3.2 3.2 3.2 20k 9k 7.5k 36kChangzhou, China 1.6 1.9 2.3 2.0 3.2 3.2 3.2 3.2 810 2k 2.9k 5.7kLe Mans, France 1.6 2.7 2.3 1.7 3.2 3.2 3.0 3.2 4.2k 130 490 4.8kOkayama, Japan 1.6 2.4 2.3 1.6 3.1 3.1 3.1 3.1 51k 1.6k 3.4k 56kZhengzhou, China 2.4 2.3 2.2 2.4 3.2 3.2 3.2 3.2 2.9k 1.1k 5.4k 9.4kBudapest, Hungary 0.5 1.3 2.2 1.2 3.4 3.1 3.0 3.2 20k 640 14k 34kChangzhi, China 0.8 3.3 2.2 1.8 3.4 3.1 3.0 3.2 370 180 310 860Zhuji, China 1.3 2.1 2.1 1.8 3.1 3.1 3.0 3.1 900 800 340 2kAddis Ababa, Ethiopia 2.6 2.2 2.1 2.3 2.9 3.1 3.1 3.0 16k 11k 16k 43kRibeirao Preto, Brazil 1.1 3.0 2.1 1.4 3.3 3.0 3.1 3.3 9.3k 1.6k 1.5k 12kBerlin, Germany 0.3 1.0 2.1 0.9 3.5 3.3 3.1 3.3 18k 11k 11k 40kThessaloniki, Greece 0.1 1.9 2.1 0.5 3.4 3.2 3.1 3.3 11k 1.2k 2.5k 14kFukuoka, Japan 1.1 1.8 2.0 1.2 3.2 3.1 3.1 3.2 47k 6.7k 2.7k 56kGombe, Nigeria 1.6 3.7 2.0 2.0 3.2 2.9 3.1 3.1 1.8k 560 4.5k 6.9kValledupar, Colombia -0.2 0.6 2.0 0.2 3.5 3.4 3.2 3.5 3k 1.1k 650 4.8kGwangju, South Korea 0.4 1.3 1.9 0.9 3.4 3.2 3.2 3.3 5.3k 2.7k 2.3k 10kAnqing, China 0.5 1.9 1.0 3.3 3.1 3.2 200 96 260 560Busan, South Korea 1.3 1.6 1.8 1.4 3.2 3.1 3.2 3.2 16k 4.1k 4.2k 24kGaoyou, China 1.8 1.8 3.1 3.1 83 32 150 260Kanpur, India 2.1 3.2 1.8 2.1 3.0 2.9 3.1 3.0 4.6k 440 420 5.5kTokyo, Japan 1.1 1.6 1.8 1.1 3.2 3.1 3.1 3.1 660k 38k 97k 800kMarrakesh, Morocco 1.1 1.5 1.7 1.3 3.2 3.2 3.2 3.2 6.3k 2.2k 3.6k 12kLeon, Nicaragua 1.0 3.2 1.7 1.5 3.3 2.9 3.1 3.2 1.1k 380 390 1.9kMadrid, Spain 0.7 1.5 1.7 1.1 3.4 3.3 3.2 3.3 28k 12k 15k 55kBuenos Aires, Argentina -0.1 0.9 1.6 0.3 3.6 3.5 3.3 3.5 120k 17k 26k 160kSuining, China 1.6 1.0 3.2 3.3 51 61 120 230Bamako, Mali 0.6 1.0 1.6 1.1 3.3 3.2 3.1 3.2 13k 7.5k 16k 36k

40

Sprawl (SNDi) Nodal degree N(nodes)<1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock <1990 90-99 ’00-13 Stock

Jinju, South Korea 0.8 2.4 1.6 1.2 3.3 3.2 3.1 3.2 2.5k 120 2.5k 5.1kLuanda, Angola 2.0 2.2 1.5 1.8 3.0 3.0 3.1 3.1 12k 10k 28k 51kCheonan, South Korea 0.2 0.7 1.3 0.8 3.4 3.4 3.2 3.3 1.6k 1.2k 2.6k 5.3kVinh Long, Viet Nam 1.3 1.4 3.1 3.2 72 99 160 330Lubumbashi, DR Congo 1.7 1.8 1.3 1.5 3.1 3.0 3.1 3.1 6.7k 2.3k 7.7k 17kJinan, China 1.8 1.5 1.2 1.7 3.2 3.3 3.3 3.2 4.3k 620 990 5.9kYanggu, China 0.7 0.4 3.3 3.4 79 66 130 280Khartoum, Sudan 0.2 0.6 0.7 0.5 3.4 3.3 3.3 3.3 56k 14k 38k 110kXucheng, China 0.5 0.8 3.6 3.5 12 51 260 330

41

F.7 City mapsMaps for a number of cities are available as a ∼ 300 MB PDF at:

http://sprawl.research.mcgill.ca/publications/2020-PNAS-sprawl/citiesA higher resolution version of the map for Seoul (included in the main text) is available at

http://sprawl.research.mcgill.ca/publications/2020-PNAS-sprawl/SI/fig3-ts-citymap-Seoul.png

F.8 World mapsWorld maps for our sprawl metrics are available on an interactive map site

F.9 World region trend plotsIn the following pages, trends of individual countries (in each region or group) are represented bycolored lines, and their distribution in each time range is summarized in a box plot. Each dotted lineshows the latest stock value for all nodes in the group of countries. Country ISO codes are markedin microfont. Region classifications are from the World Bank [2016]. For more detail, see the tabulardata.

The following pages show results only for SNDi. Other metrics are available online at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl.

42

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GHA

GHA

MOZ

MOZ

EGY

EGY

DZA

DZA

MAR

MAR

AGO

AGO

COD

COD

SDN

SDN

ETHETH

ZAF

ZAF

NGA

NGA

Africa

COM

COM

BHR

BHR

DJIDJI

KWT

KWT

LBN

LBN

MRT

MRT

QAT

QAT

PSE

PSE

OMN

OMN

LBY

LBY

ARE

ARE

TUN

TUN

JOR

JORSYR

SYR

SOMSOM

YEM

YEM EGY

EGY

DZA

DZA

MAR

MAR

SAU

SAUSDN

SDN

IRQIRQ

Arab World

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCA

VCT

VCT

ATG

ATG

KNA

KNA

BHS

BHS

TTO

TTO

SUR

SUR

BLZBLZ

BRB

BRB

JAM

JAM

Caribbean small states

EST

EST

LVA

LVA

LTU

LTU

SVN

SVN

HRV

HRV

BGR

BGR

SVK

SVK

HUN

HUN

CZE

CZE

ROU

ROU

POL

POL

Central Europe and theBaltics

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

MNPMNP

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

ASM

ASM

GUM

GUM

WSM

WSM

HKG

HKG

TLS

TLS

MAC

MAC

SGP

SGP

FJI

FJI

PNG

PNGNCL

NCL

BRN

BRN

LAO

LAO

PRK

PRK

MNG

MNG

NZL

NZL

TWN

TWN

KHM

KHM

MMR

MMR

KOR

KOR

MYS

MYS

PHL

PHL

AUS

AUS

VNM

VNM

JPN

JPN

THA

THA

IDN

IDN

CHNCHN

FSMFSM

East Asia & Pacific(all income levels)

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

ASM

ASM

WSM

WSM

TLS

TLS

FJI

FJI

PNG

PNG

LAO

LAO

PRK

PRK

MNG

MNG

KHM

KHM

MMR

MMR

MYS

MYS

PHL

PHLVNM

VNM

THA

THA

IDN

IDN

CHNCHN

FSMFSM

East Asia & Pacific(developing only)

MLT

MLT

LUX

LUX

EST

EST

LVA

LVA

LTU

LTU

SVN

SVN

CYP

CYP

SVK

SVK

GRC

GRC

AUT

AUT

FIN

FIN

BEL

BEL

IRL

IRL

PRT

PRT

NLD

NLD

ITA

ITA

DEU

DEU

ESP

ESP

FRA

FRA

Euro area

SMR

SMR

AND

AND

LIE

LIE

GRL

GRL

IMN

IMN

MNE

MNE

MKD

MKD

LUX

LUX

ARM

ARM

MDA

MDA

EST

EST

LVA

LVA

LTU

LTU

BIH

BIH

GEO

GEO

ISL

ISL

SVN

SVN

ALB

ALB

HRV

HRV

CYP

CYP

XKO

XKO

TKM

TKM

BGR

BGR

TJKTJK

SRB

SRB

SVK

SVK

BLR

BLR

CHE

CHE

GRC

GRC

HUN

HUN

AUT

AUT

KGZ

KGZ

KAZ

KAZ

FIN

FIN

SWE

SWE

CZE

CZE

UZB

UZB

BEL

BEL

IRL

IRL

NOR

NORDNK

DNK

AZE

AZE ROU

ROU

UKR

UKR

PRT

PRT

POL

POL

GBR

GBR

NLD

NLD

ITA

ITA

RUS

RUSTUR

TUR

DEU

DEU

ESP

ESP

FRA

FRA

MCOMCO

Europe & Central Asia(all income levels)

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

MNE

MNE

MKD

MKD

ARM

ARM

MDA

MDA

BIH

BIH

GEO

GEO

ALB

ALB

XKO

XKO

TKM

TKM

BGR

BGR

TJKTJK

SRB

SRB

BLR

BLR

KGZ

KGZ

KAZ

KAZ

UZB

UZB

AZE

AZE ROU

ROU

UKR

UKR TUR

TUR

Europe & Central Asia(developing only)

<1975 ’75–’89

’90–’99

’00–’13

MLT

MLT

LUX

LUX

EST

EST

LVA

LVA

LTU

LTU

SVN

SVN

HRV

HRV

CYP

CYP

BGR

BGR

SVK

SVK

GRC

GRC

HUN

HUN

AUT

AUT

FIN

FIN

SWE

SWE

CZE

CZE

BEL

BEL

IRL

IRL

DNK

DNK

ROU

ROU

PRT

PRT

POL

POL

GBR

GBR

NLD

NLD

ITA

ITA

DEU

DEU

ESP

ESP

FRA

FRA

European Union

<1975 ’75–’89

’90–’99

’00–’13

SLB

SLB

TLS

TLS

COM

COM

GNB

GNB CAF

CAF

GMB

GMB

BIH

BIH

MDG

MDG

ERI

ERI

LBR

LBR

XKO

XKO

LBN

LBN

PSE

PSE

BDI

BDI

HTI

HTI AFG

AFG

CIV

CIV

LBY

LBY

SLE

SLE

ZWE

ZWE

TGO

TGO

SSD

SSD

TCD

TCD

SYR

SYR

SOMSOM

YEM

YEM

MLI

MLI

MMR

MMR

COD

COD

SDN

SDN

IRQIRQ

FSMFSM

Fragile and conflictaffected situations

<1975 ’75–’89

’90–’99

’00–’13

GUY

GUY

STP

STP

COM

COM

GNB

GNB CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

LBR

LBR

MRT

MRT

NIC

NIC

BDI

BDI

HTI

HTI RWA

RWA

COG

COG

AFG

AFG

CIV

CIV

SLE

SLE

HND

HND

TGO

TGO

TCD

TCD

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

SOMSOM

MWIMWI

CMR

CMR

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

BOLBOL

GHA

GHA

MOZ

MOZ

COD

COD

SDN

SDN

ETHETH

Heavily indebted poorcountries (HIPC)

0

2

4

6

8

10

12S

praw

l(S

ND

i)

ABW

ABW

SXM

SXM

BMUBMU

MNPMNP

SMR

SMR

MAF

MAF

SYC

SYC

TCA

TCA

AND

AND

CUW

CUW

LIE

LIE

GUM

GUM

CYM

CYM

HKG

HKG

ATG

ATG

MAC

MAC

GRL

GRL

SGP

SGP

IMN

IMN

NCL

NCL

KNA

KNA

BHS

BHS

GNQ

GNQ

TTO

TTO

MLT

MLT

BRN

BRN

BHR

BHR

VIR

VIR

LUX

LUX

EST

EST

LVA

LVA

LTU

LTU

BRB

BRB

ISL

ISL

SVN

SVN

KWT

KWT

HRV

HRV

URY

URY

CYP

CYP

QAT

QAT

SVK

SVK

NZL

NZL

CHE

CHE TWN

TWN

OMN

OMN

PRI

PRI

ARE

ARE

GRC

GRC

HUN

HUN

ISR

ISR

AUT

AUT

FIN

FIN

VEN

VEN

SWE

SWE

CZE

CZE

BEL

BEL

IRL

IRL

NOR

NORDNK

DNK

PRT

PRT

KOR

KOR

POL

POL

GBR

GBR

CHL

CHL

NLD

NLD

AUS

AUS

CAN

CAN

SAU

SAU

ARG

ARG

ITA

ITA

RUS

RUS

DEU

DEU JPN

JPN

ESP

ESP

FRA

FRA

USA

USA

MCOMCO

High income

LUX

LUX

EST

EST

ISL

ISL

SVN

SVN

SVK

SVK

NZL

NZL

CHE

CHE

GRC

GRC

HUN

HUN

ISR

ISR

AUT

AUT

FIN

FIN

SWE

SWE

CZE

CZE

BEL

BEL

IRL

IRL

NOR

NORDNK

DNK

PRT

PRT

KOR

KOR

POL

POL

GBR

GBR

CHL

CHL

NLD

NLD

AUS

AUS

CAN

CAN

ITA

ITA

DEU

DEU JPN

JPN

ESP

ESP

FRA

FRA

USA

USA

High income: OECD

ABW

ABW

SXM

SXM

BMUBMU

MNPMNP

SMR

SMR

MAF

MAF

SYC

SYC

TCA

TCA

AND

AND

CUW

CUW

LIE

LIE

GUM

GUM

CYM

CYM

HKG

HKG

ATG

ATG

MAC

MAC

GRL

GRL

SGP

SGP

IMN

IMN

NCL

NCL

KNA

KNA

BHS

BHS

GNQ

GNQ

TTO

TTO

MLT

MLT

BRN

BRN

BHR

BHR

VIR

VIR

LVA

LVA

LTU

LTU

BRB

BRB

KWT

KWT

HRV

HRV

URY

URY

CYP

CYP

QAT

QAT

TWN

TWN

OMN

OMN

PRI

PRI

ARE

ARE

VEN

VEN

SAU

SAU

ARG

ARG

RUS

RUS

MCOMCO

High income: nonOECD

PLW

PLW

SYC

SYC

ATG

ATG

FJI

FJI

KNA

KNA

MNE

MNE

GNQ

GNQ

TTO

TTO

GAB

GAB

SUR

SUR

BLZBLZ

MKD

MKD

SWZ

SWZ

ARM

ARM

BIH

BIH

GEO

GEO

ALB

ALB

CRI

CRI HRV

HRV

URY

URY

MUS

MUS

JAM

JAM

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

SRB

SRB

PAN

PAN

SLV

SLV

BLR

BLR

PRY

PRY

LBY

LBY

TUN

TUN

BWA

BWA

JOR

JOR

GTM

GTM

DOM

DOM

KAZ

KAZ

VEN

VEN

SYR

SYR

ECU

ECU

AZE

AZE

PERPER

COL

COL

ROU

ROU

UKR

UKR

KOR

KOR

POL

POL

MYS

MYS

CHL

CHL

EGY

EGY

DZA

DZA

PHL

PHL

MAR

MAR

AGO

AGO

ARG

ARG

RUS

RUSTUR

TUR

ZAF

ZAF

IRQIRQ

THA

THA

IRN

IRN

IDN

IDN

MEX

MEX

BRA

BRA

CHNCHN

IND

IND

IBRD only

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

SYC

SYC

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCAWSM

WSM

VCT

VCT

TLS

TLS

ATG

ATG

STP

STP

FJI

FJI

PNG

PNG

COM

COM

MDV

MDV

KNA

KNA

MNE

MNE

BTN

BTN

GNQ

GNQ

TTO

TTO

CPV

CPV

GAB

GAB

SUR

SUR

BLZBLZ

GNB

GNB

MKD

MKD

SWZ

SWZ

DJIDJI

ARM

ARM

MDA

MDA

CAF

CAF

GMB

GMB

BIH

BIH

MDG

MDG

GEO

GEO

ERI

ERI

LAO

LAO

ALB

ALB

CRI

CRI HRV

HRV

URY

URY

MUS

MUS

LBR

LBR

JAM

JAM

MNG

MNG

XKO

XKO

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

MRT

MRT

TJKTJK

SRB

SRB

PAN

PAN

NPL

NPL

NIC

NIC

SLV

SLV

BLR

BLR

BDI

BDI

HTI

HTI RWA

RWA

PRY

PRY

COG

COG

AFG

AFG

CIV

CIV

LBY

LBY

SLE

SLE

HND

HND

TUN

TUN

ZWE

ZWE

BWA

BWA

TGO

TGO

JOR

JOR

SSD

SSD

GTM

GTM

KHM

KHM

LSO

LSO

TCD

TCD

DOM

DOM

KGZ

KGZ

GIN

GIN

KAZ

KAZ

UGAUGA

VEN

VEN

ZMB

ZMB

SYR

SYR

ECU

ECUNER

NER

BEN

BEN

UZB

UZB

SOMSOM

YEM

YEM

AZE

AZE

KEN

KEN

PERPER

MWIMWI

COL

COL

ROU

ROU

LKA

LKA

BGD

BGD

CMR

CMR

BFA

BFA

MLI

MLI

UKR

UKR

SEN

SEN

TZA

TZA

MMR

MMR

BOLBOL

GHA

GHA

KOR

KOR

MOZ

MOZ

POL

POL

MYS

MYS

CHL

CHL

PAK

PAK

EGY

EGY

DZA

DZA

PHL

PHL

MAR

MAR

AGO

AGO

COD

COD

VNM

VNM

ARG

ARG

SDN

SDN

ETHETH

RUS

RUSTUR

TUR

ZAF

ZAF

IRQIRQ

THA

THA

IRN

IRN

IDN

IDN

MEX

MEX

BRA

BRA

NGA

NGA

CHNCHN

IND

IND

FSMFSM

IDA & IBRD total

GRD

GRD

DMA

DMA

LCA

LCA

VCT

VCT

TLS

TLS

PNG

PNG

CPV

CPV

MDA

MDA

MNG

MNG

COG

COG

ZWE

ZWE

UZB

UZB

LKA

LKA

CMR

CMR

BOLBOL

PAK

PAK

VNM

VNM

NGA

NGA

IDA blend

TON

TON

SLB

SLB

VUT

VUT

GUY

GUY

WSM

WSM

STP

STP

COM

COM

MDV

MDV

BTN

BTN

GNB

GNB

DJIDJI

CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

LAO

LAO

LBR

LBR

XKO

XKO

MRT

MRT

TJKTJK

NPL

NPL

NIC

NIC

BDI

BDI

HTI

HTI RWA

RWA

AFG

AFG

CIV

CIV

SLE

SLE

HND

HND

TGO

TGO

SSD

SSD

KHM

KHM

LSO

LSO

TCD

TCD

KGZ

KGZ

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

SOMSOM

YEM

YEM

KEN

KEN

MWIMWI

BGD

BGD

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

MMR

MMR

GHA

GHA

MOZ

MOZ

COD

COD

SDN

SDN

ETHETH

FSMFSM

IDA only

TON

TON

SLB

SLB

VUT

VUT

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCAWSM

WSM

VCT

VCT

TLS

TLS

STP

STP

PNG

PNG

COM

COM

MDV

MDV

BTN

BTN

CPV

CPV

GNB

GNB

DJIDJI

MDA

MDA

CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

LAO

LAO

LBR

LBR

MNG

MNG

XKO

XKO

MRT

MRT

TJKTJK

NPL

NPL

NIC

NIC

BDI

BDI

HTI

HTI RWA

RWA

COG

COG

AFG

AFG

CIV

CIV

SLE

SLE

HND

HND

ZWE

ZWE

TGO

TGO

SSD

SSD

KHM

KHM

LSO

LSO

TCD

TCD

KGZ

KGZ

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

UZB

UZB

SOMSOM

YEM

YEM

KEN

KEN

MWIMWI

LKA

LKA

BGD

BGD

CMR

CMR

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

MMR

MMR

BOLBOL

GHA

GHA

MOZ

MOZ

PAK

PAK

COD

COD

VNM

VNM

SDN

SDN

ETHETH

NGA

NGA

FSMFSM

IDA total

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

ABW

ABW

SXM

SXM

MAF

MAF

TCA

TCA

CUW

CUW

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCA

CYM

CYM

VCT

VCT

ATG

ATG

KNA

KNA

BHS

BHS

TTO

TTO

SUR

SUR

BLZBLZ

VIR

VIR

BRB

BRB

CUB

CUB

CRI

CRI

URY

URY

JAM

JAM

PAN

PAN

NIC

NIC

SLV

SLV

HTI

HTI

PRY

PRY

PRI

PRI HND

HND

GTM

GTM

DOM

DOM

VEN

VEN

ECU

ECU

PERPER

COL

COL

BOLBOL

CHL

CHL

ARG

ARG

MEX

MEX

BRA

BRA

Latin America &Caribbean (all incomelevels)

<1975 ’75–’89

’90–’99

’00–’13

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCA

VCT

VCT

SUR

SUR

BLZBLZ

CUB

CUB

CRI

CRI

JAM

JAM

PAN

PAN

NIC

NIC

SLV

SLV

HTI

HTI

PRY

PRY

HND

HND

GTM

GTM

DOM

DOM

ECU

ECU

PERPER

COL

COL

BOLBOL

MEX

MEX

BRA

BRA

Latin America &Caribbean (developingonly)

<1975 ’75–’89

’90–’99

’00–’13

SLB

SLB

VUT

VUTTLS

TLS

STP

STP

COM

COM

BTN

BTN

GNQ

GNQ

GNB

GNB

DJIDJI

CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

LAO

LAO

LBR

LBR

MRT

MRT

NPL

NPL

BDI

BDI

HTI

HTI RWA

RWA

AFG

AFG

SLE

SLE

TGO

TGO

SSD

SSD

KHM

KHM

LSO

LSO

TCD

TCD

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

SOMSOM

YEM

YEM

MWIMWI

BGD

BGD

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

MMR

MMR

MOZ

MOZ

AGO

AGO

COD

COD

SDN

SDN

ETHETH

Least developedcountries: UNclassification

<1975 ’75–’89

’90–’99

’00–’13

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

ASM

ASM

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCAWSM

WSM

VCT

VCT

TLS

TLS

STP

STP

FJI

FJI

PNG

PNG

COM

COM

MDV

MDV

MNE

MNE

BTN

BTN

CPV

CPV

GAB

GAB

SUR

SUR

BLZBLZ

GNB

GNB

MKD

MKD

SWZ

SWZ

DJIDJI

ARM

ARM

MDA

MDA

CAF

CAF

GMB

GMB

BIH

BIH

MDG

MDG

GEO

GEO

CUB

CUB

ERI

ERI

LAO

LAO

ALB

ALB

CRI

CRI

PRK

PRK

MUS

MUS

LBR

LBR

JAM

JAM

MNG

MNG

XKO

XKO

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

MRT

MRT

TJKTJK

SRB

SRB

PAN

PAN

NPL

NPL

NIC

NIC

PSE

PSE

SLV

SLV

BLR

BLR

BDI

BDI

HTI

HTI RWA

RWA

PRY

PRY

COG

COG

AFG

AFG

CIV

CIV

LBY

LBY

SLE

SLE

HND

HND

TUN

TUN

ZWE

ZWE

BWA

BWA

TGO

TGO

JOR

JOR

SSD

SSD

GTM

GTM

KHM

KHM

LSO

LSO

TCD

TCD

DOM

DOM

KGZ

KGZ

GIN

GIN

KAZ

KAZ

UGAUGA

ZMB

ZMB

SYR

SYR

ECU

ECUNER

NER

BEN

BEN

UZB

UZB

SOMSOM

YEM

YEM

AZE

AZE

KEN

KEN

PERPER

MWIMWI

COL

COL

ROU

ROU

LKA

LKA

BGD

BGD

CMR

CMR

BFA

BFA

MLI

MLI

UKR

UKR

SEN

SEN

TZA

TZA

MMR

MMR

BOLBOL

GHA

GHA

MOZ

MOZ

MYS

MYS

PAK

PAK

EGY

EGY

DZA

DZA

PHL

PHL

MAR

MAR

AGO

AGO

COD

COD

VNM

VNM

SDN

SDN

ETHETH

TUR

TUR

ZAF

ZAF

IRQIRQ

THA

THA

IRN

IRN

IDN

IDN

MEX

MEX

BRA

BRA

NGA

NGA

CHNCHN

IND

IND

FSMFSM

Low & middle income

0

2

4

6

8

10

12S

praw

l(S

ND

i)

COM

COM

GNB

GNB CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

PRK

PRK

LBR

LBR

NPL

NPL

BDI

BDI

HTI

HTI RWA

RWA

AFG

AFG

SLE

SLE

ZWE

ZWE

TGO

TGO

SSD

SSD

KHM

KHM

TCD

TCD

GIN

GIN

UGAUGA

NER

NER

BEN

BEN

SOMSOM

MWIMWI

BFA

BFA

MLI

MLI

TZA

TZA

MOZ

MOZ

COD

COD

ETHETH

Low income

SLB

SLB

VUT

VUT

GUY

GUY

WSM

WSM

TLS

TLS

STP

STP

PNG

PNG

BTN

BTN

CPV

CPV

SWZ

SWZ

DJIDJI

ARM

ARM

MDA

MDA

GEO

GEO

LAO

LAO

XKO

XKO

MRT

MRT

TJKTJK

NIC

NIC

PSE

PSE

SLV

SLV

COG

COG

CIV

CIV

HND

HND

GTM

GTM LSO

LSO

KGZ

KGZ

ZMB

ZMB

SYR

SYR

UZB

UZB YEM

YEM

KEN

KEN

LKA

LKA

BGD

BGD

CMR

CMR

UKR

UKR

SEN

SEN

MMR

MMR

BOLBOL

GHA

GHA

PAK

PAK

EGY

EGY

PHL

PHL

MAR

MAR

VNM

VNM

SDN

SDN

IDN

IDN

NGA

NGA

IND

IND

FSMFSM

Lower middle income

MLT

MLT

BHR

BHR

DJIDJI

KWT

KWT

LBN

LBN

QAT

QAT

PSE

PSE

OMN

OMN

LBY

LBY

ARE

ARE

TUN

TUN

JOR

JOR

ISR

ISR

SYR

SYR

YEM

YEM EGY

EGY

DZA

DZA

MAR

MAR

SAU

SAU

IRQIRQ

IRN

IRN

Middle East & NorthAfrica (all incomelevels)

DJIDJI

LBN

LBN

PSE

PSE

LBY

LBY

TUN

TUN

JOR

JORSYR

SYR

YEM

YEM EGY

EGY

DZA

DZA

MAR

MAR

IRQIRQ

IRN

IRN

Middle East & NorthAfrica (developingonly)

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

ASM

ASM

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCAWSM

WSM

VCT

VCT

TLS

TLS

STP

STP

FJI

FJI

PNG

PNG

MDV

MDV

MNE

MNE

BTN

BTN

CPV

CPV

GAB

GAB

SUR

SUR

BLZBLZ

MKD

MKD

SWZ

SWZ

DJIDJI

ARM

ARM

MDA

MDA

BIH

BIH

GEO

GEO

CUB

CUB

LAO

LAO

ALB

ALB

CRI

CRI

MUS

MUS

JAM

JAM

MNG

MNG

XKO

XKO

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

MRT

MRT

TJKTJK

SRB

SRB

PAN

PAN

NIC

NIC

PSE

PSE

SLV

SLV

BLR

BLR

PRY

PRY

COG

COG

CIV

CIV

LBY

LBY

HND

HND

TUN

TUN

BWA

BWA

JOR

JOR

GTM

GTM LSO

LSO

DOM

DOM

KGZ

KGZ

KAZ

KAZ

ZMB

ZMB

SYR

SYR

ECU

ECU

UZB

UZB YEM

YEM

AZE

AZE

KEN

KEN

PERPER

COL

COL

ROU

ROU

LKA

LKA

BGD

BGD

CMR

CMR

UKR

UKR

SEN

SEN

MMR

MMR

BOLBOL

GHA

GHA

MYS

MYS

PAK

PAK

EGY

EGY

DZA

DZA

PHL

PHL

MAR

MAR

AGO

AGO

VNM

VNM

SDN

SDN

TUR

TUR

ZAF

ZAF

IRQIRQ

THA

THA

IRN

IRN

IDN

IDN

MEX

MEX

BRA

BRA

NGA

NGA

CHNCHN

IND

IND

FSMFSM

Middle income

BMUBMU

CAN

CAN

USA

USA

North America

LUX

LUX

EST

EST

ISL

ISL

SVN

SVN

SVK

SVK

NZL

NZL

CHE

CHE

GRC

GRC

HUN

HUN

ISR

ISR

AUT

AUT

FIN

FIN

SWE

SWE

CZE

CZE

BEL

BEL

IRL

IRL

NOR

NORDNK

DNK

PRT

PRT

KOR

KOR

POL

POL

GBR

GBR

CHL

CHL

NLD

NLD

AUS

AUS

CAN

CAN

ITA

ITA

TUR

TUR

DEU

DEU JPN

JPN

ESP

ESP

FRA

FRA

MEX

MEX

USA

USA

OECD members

SYC

SYC

TLS

TLS

STP

STP

COM

COM

MDV

MDV

MNE

MNE

BTN

BTN

GNQ

GNQ

CPV

CPV

GAB

GAB

GNB

GNB

SWZ

SWZ

DJIDJI

GMB

GMB

MUS

MUS

NAM

NAMBWA

BWA

LSO

LSO

Other small states

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

WSM

WSM

FJI

FJI

FSMFSM

Pacific island smallstates

<1975 ’75–’89

’90–’99

’00–’13

TON

TON

SLB

SLB

VUT

VUT

PLW

PLW

SYC

SYC

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCAWSM

WSM

VCT

VCT

TLS

TLS

ATG

ATG

STP

STP

FJI

FJI

COM

COM

MDV

MDV

KNA

KNA

BHS

BHS

MNE

MNE

BTN

BTN

GNQ

GNQ

TTO

TTO

CPV

CPV

GAB

GAB

SUR

SUR

BLZBLZ

GNB

GNB

SWZ

SWZ

DJIDJI

GMB

GMB

BRB

BRB

MUS

MUS

JAM

JAM

NAM

NAMBWA

BWA

LSO

LSO

FSMFSM

Small states

<1975 ’75–’89

’90–’99

’00–’13

MDV

MDV

BTN

BTN

NPL

NPL

AFG

AFG

LKA

LKA

BGD

BGD

PAK

PAK

IND

IND

South Asia

<1975 ’75–’89

’90–’99

’00–’13

SYC

SYC

STP

STP

COM

COM

GNQ

GNQ

CPV

CPV

GAB

GAB

GNB

GNB

SWZ

SWZ

CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

MUS

MUS

LBR

LBR

NAM

NAM

MRT

MRT

BDI

BDI

RWA

RWA

COG

COG

CIV

CIV

SLE

SLE

ZWE

ZWE

BWA

BWA

TGO

TGO

SSD

SSD

LSO

LSO

TCD

TCD

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

SOMSOM

KEN

KEN

MWIMWI

CMR

CMR

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

GHA

GHA

MOZ

MOZ

AGO

AGO

COD

COD

SDN

SDN

ETHETH

ZAF

ZAF

NGA

NGA

Sub-Saharan Africa(all income levels)

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12S

praw

l(S

ND

i)

STP

STP

COM

COM

CPV

CPV

GAB

GAB

GNB

GNB

SWZ

SWZ

CAF

CAF

GMB

GMB

MDG

MDG

ERI

ERI

MUS

MUS

LBR

LBR

NAM

NAM

MRT

MRT

BDI

BDI

RWA

RWA

COG

COG

CIV

CIV

SLE

SLE

ZWE

ZWE

BWA

BWA

TGO

TGO

SSD

SSD

LSO

LSO

TCD

TCD

GIN

GIN

UGAUGA

ZMB

ZMB

NER

NER

BEN

BEN

SOMSOM

KEN

KEN

MWIMWI

CMR

CMR

BFA

BFA

MLI

MLI

SEN

SEN

TZA

TZA

GHA

GHA

MOZ

MOZ

AGO

AGO

COD

COD

SDN

SDN

ETHETH

ZAF

ZAF

NGA

NGA

Sub-Saharan Africa(developing only)

<1975 ’75–’89

’90–’99

’00–’13

TON

TON

PLW

PLW

ASM

ASM

GRD

GRD

DMA

DMA

LCA

LCA

VCT

VCT

FJI

FJI

MDV

MDV

MNE

MNE

GAB

GAB

SUR

SUR

BLZBLZ

MKD

MKD

BIH

BIH

CUB

CUB

ALB

ALB

CRI

CRI

MUS

MUS

JAM

JAM

MNG

MNG

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

SRB

SRB

PAN

PAN

BLR

BLR

PRY

PRY

LBY

LBY

TUN

TUN

BWA

BWA

JOR

JOR

DOM

DOM

KAZ

KAZ

ECU

ECU

AZE

AZE

PERPER

COL

COL

ROU

ROU

MYS

MYS

DZA

DZAAGO

AGO

TUR

TUR

ZAF

ZAF

IRQIRQ

THA

THA

IRN

IRN

MEX

MEX

BRA

BRA

CHNCHN

Upper middle income

<1975 ’75–’89

’90–’99

’00–’13

ABW

ABW

SXM

SXM

BMUBMU

MNPMNP

SMR

SMR

TON

TON

MAF

MAF

SLB

SLB

VUT

VUT

PLW

PLW

SYC

SYC

ASM

ASM

TCA

TCA

AND

AND

CUW

CUW

GRD

GRD

GUY

GUY

DMA

DMA

LCA

LCALIE

LIE

GUM

GUM

WSM

WSM

CYM

CYM

HKG

HKG

VCT

VCT

TLSTLS

ATG

ATG

STP

STP

MAC

MAC

GRL

GRL

SGP

SGP

IMN

IMN

FJI

FJI

PNG

PNG

COM

COM

MDV

MDV

NCL

NCL

KNA

KNA

BHS

BHS

MNE

MNE

BTN

BTN

GNQ

GNQ

TTO

TTO

CPV

CPV

GAB

GAB

SUR

SUR

MLT

MLT

BRN

BRN

BLZBLZ

BHR

BHR

VIR

VIRGNB

GNB

MKD

MKD

LUX

LUX

SWZ

SWZ

DJIDJI

ARM

ARM

MDA

MDA

EST

EST

LVA

LVA

CAF

CAF

GMB

GMB

LTU

LTU

BRB

BRB

BIH

BIH

MDG

MDG

GEO

GEO

CUB

CUB

ERI

ERI

ISL

ISL

SVN

SVN

KWT

KWT

LAO

LAO

ALB

ALB

CRI

CRI HRV

HRV

PRK

PRK

URY

URY

CYP

CYP

MUS

MUS

LBR

LBR

JAM

JAM

MNG

MNG

XKO

XKO

TKM

TKM

NAM

NAM

LBN

LBN

BGR

BGR

MRT

MRT

TJKTJK

QAT

QAT

SRB

SRB

PAN

PAN

SVK

SVK

NZL

NZL

NPL

NPL

NIC

NIC

PSE

PSE

SLV

SLV

BLR

BLR

BDI

BDI

CHE

CHE

HTI

HTI RWA

RWA

TWN

TWN

OMN

OMN

PRY

PRY

COG

COG

PRI

PRI

AFG

AFG

CIV

CIV

LBY

LBY

SLE

SLE

ARE

ARE

HND

HND

TUN

TUN

ZWE

ZWE

BWA

BWA

GRC

GRC

TGO

TGO

HUN

HUN

JOR

JOR

SSD

SSD

GTM

GTM

KHM

KHM

ISR

ISR

LSO

LSO

TCD

TCD

DOM

DOM

AUT

AUT

KGZKGZ

GIN

GIN

KAZ

KAZ

FIN

FIN

UGAUGA

VEN

VEN

SWE

SWE

ZMB

ZMB

CZE

CZE

SYR

SYR

ECU

ECUNER

NER

BEN

BEN

UZB

UZB

BEL

BEL

SOMSOM

IRL

IRL

NOR

NORDNK

DNK

YEM

YEM

AZE

AZE

KEN

KEN

PERPER

MWIMWI

COL

COL

ROU

ROU

LKA

LKA

BGD

BGD

CMR

CMR

BFA

BFA

MLI

MLI

UKR

UKR

SEN

SEN

TZA

TZA

PRT

PRT

MMR

MMR

BOLBOL

GHA

GHA

KOR

KOR

MOZ

MOZ

POL

POL

GBR

GBR

MYS

MYS

CHL

CHL

PAK

PAK

NLD

NLD

EGYEGY

DZA

DZA

PHL

PHL

MAR

MAR

AGO

AGO

COD

COD

AUS

AUS

CAN

CAN

SAU

SAU

VNM

VNM

ARG

ARG

ITA

ITA

SDN

SDN

ETHETH

RUS

RUSTUR

TUR

ZAF

ZAF

DEU

DEU JPN

JPN

IRQIRQ

ESP

ESP

THA

THA FRA

FRA

IRN

IRN

IDN

IDN

MEX

MEX

BRA

BRA

NGA

NGA

CHNCHN

IND

IND

USA

USA

MCOMCO

FSMFSM

World

F.10 Country trend plots with sub-national regional distributionsIn the following pages, box plots are used to describe the distribution, within each time period in eachcountry, of the metric over sub-national regions (i.e., GADM level 1). The blue shaded region showsthe full extent of the minimum and maximum value across these sub-national regions. The dark lineshows the mean over the entire country.

The following pages show results only for SNDi. Other metrics are available online at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl.

47

0

2

4

6

8

10

12S

praw

l(S

ND

i)

United States Japan Brazil Germany

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

United Kingdom France Italy Mexico

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

India

<1975 ’75–’89

’90–’99

’00–’13

Indonesia

<1975 ’75–’89

’90–’99

’00–’13

Russian Federation

<1975 ’75–’89

’90–’99

’00–’13

Spain

0

2

4

6

8

10

12S

praw

l(S

ND

i)

China Turkey Thailand Nigeria

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Australia Canada Ukraine Iran, Islamic Rep.

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Argentina

<1975 ’75–’89

’90–’99

’00–’13

Colombia

<1975 ’75–’89

’90–’99

’00–’13

Malaysia

<1975 ’75–’89

’90–’99

’00–’13

Netherlands

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Venezuela, RB South Africa Algeria Egypt, Arab Rep.

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Poland Philippines Greece Peru

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Portugal

<1975 ’75–’89

’90–’99

’00–’13

Sweden

<1975 ’75–’89

’90–’99

’00–’13

Saudi Arabia

<1975 ’75–’89

’90–’99

’00–’13

Belgium

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Romania Korea, Rep. Czech Republic Iraq

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Austria Ecuador Denmark Taiwan, China

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Chile

<1975 ’75–’89

’90–’99

’00–’13

Switzerland

<1975 ’75–’89

’90–’99

’00–’13

Hungary

<1975 ’75–’89

’90–’99

’00–’13

Bulgaria

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Sudan Ethiopia Vietnam Morocco

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Congo, Dem. Rep. Tunisia Uganda Syrian Arab Republic

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Uzbekistan

<1975 ’75–’89

’90–’99

’00–’13

Sri Lanka

<1975 ’75–’89

’90–’99

’00–’13

Pakistan

<1975 ’75–’89

’90–’99

’00–’13

Bangladesh

0

2

4

6

8

10

12S

praw

l(S

ND

i)

New Zealand Belarus Afghanistan Kazakhstan

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Norway Cuba Angola Slovak Republic

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Ireland

<1975 ’75–’89

’90–’99

’00–’13

Jordan

<1975 ’75–’89

’90–’99

’00–’13

Serbia

<1975 ’75–’89

’90–’99

’00–’13

Moldova

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Georgia Puerto Rico Finland Azerbaijan

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Israel Cote d’Ivoire Myanmar Senegal

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Croatia

<1975 ’75–’89

’90–’99

’00–’13

Libya

<1975 ’75–’89

’90–’99

’00–’13

Dominican Republic

<1975 ’75–’89

’90–’99

’00–’13

Bolivia

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Guatemala Slovenia United Arab Emirates Ghana

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Korea, Dem. People’s Rep. Mozambique Cameroon Costa Rica

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Paraguay

<1975 ’75–’89

’90–’99

’00–’13

Congo, Rep.

<1975 ’75–’89

’90–’99

’00–’13

Mali

<1975 ’75–’89

’90–’99

’00–’13

Tanzania

0

2

4

6

8

10

12S

praw

l(S

ND

i)

El Salvador Uruguay West Bank and Gaza Trinidad and Tobago

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Burkina Faso Zambia Cyprus Nepal

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Zimbabwe

<1975 ’75–’89

’90–’99

’00–’13

Macedonia, FYR

<1975 ’75–’89

’90–’99

’00–’13

Honduras

<1975 ’75–’89

’90–’99

’00–’13

Malawi

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Botswana Albania Armenia Madagascar

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Lithuania Kuwait Kenya Jamaica

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Yemen, Rep.

<1975 ’75–’89

’90–’99

’00–’13

Somalia

<1975 ’75–’89

’90–’99

’00–’13

Bosnia and Herzegovina

<1975 ’75–’89

’90–’99

’00–’13

Lebanon

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Oman Benin Kosovo Mauritius

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Singapore Kyrgyz Republic Niger Nicaragua

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Bahrain

<1975 ’75–’89

’90–’99

’00–’13

Qatar

<1975 ’75–’89

’90–’99

’00–’13

Latvia

<1975 ’75–’89

’90–’99

’00–’13

Panama

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Mongolia Togo Guinea Liberia

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Lesotho Estonia Haiti Chad

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Cambodia

<1975 ’75–’89

’90–’99

’00–’13

Eritrea

<1975 ’75–’89

’90–’99

’00–’13

South Sudan

<1975 ’75–’89

’90–’99

’00–’13

Mauritania

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Tajikistan Barbados Sierra Leone Suriname

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Luxembourg Guyana Gabon Turkmenistan

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Hong Kong SAR, China

<1975 ’75–’89

’90–’99

’00–’13

Rwanda

<1975 ’75–’89

’90–’99

’00–’13

Malta

<1975 ’75–’89

’90–’99

’00–’13

Burundi

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Curacao Maldives Brunei Darussalam Gambia, The

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Iceland Central African Republic Namibia Lao PDR

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Bahamas, The

<1975 ’75–’89

’90–’99

’00–’13

Aruba

<1975 ’75–’89

’90–’99

’00–’13

Comoros

<1975 ’75–’89

’90–’99

’00–’13

Papua New Guinea

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Montenegro Tonga Guam Cabo Verde

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Fiji New Caledonia Antigua and Barbuda Belize

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Grenada

<1975 ’75–’89

’90–’99

’00–’13

Virgin Islands (U.S.)

<1975 ’75–’89

’90–’99

’00–’13

Bermuda

<1975 ’75–’89

’90–’99

’00–’13

Djibouti

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Isle of Man St. Vincent and the Grenadines Swaziland St. Kitts and Nevis

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Equatorial Guinea Macao SAR, China Timor-Leste Sint Maarten (Dutch part)

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Guinea-Bissau

<1975 ’75–’89

’90–’99

’00–’13

St. Lucia

<1975 ’75–’89

’90–’99

’00–’13

Northern Mariana Islands

<1975 ’75–’89

’90–’99

’00–’13

Seychelles

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Greenland American Samoa Bhutan Cayman Islands

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Samoa St. Martin (French part) Solomon Islands Vanuatu

<1975 ’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Dominica

<1975 ’75–’89

’90–’99

’00–’13

Liechtenstein

<1975 ’75–’89

’90–’99

’00–’13

Sao Tome and Principe

<1975 ’75–’89

’90–’99

’00–’13

San Marino

0

2

4

6

8

10

12S

praw

l(S

ND

i)

Andorra

<1975’75–’89

’90–’99

’00–’13

Turks and Caicos Islands

<1975’75–’89

’90–’99

’00–’13

Micronesia, Fed. Sts.

<1975’75–’89

’90–’99

’00–’13

Monaco

<1975’75–’89

’90–’99

’00–’13

0

2

4

6

8

10

12

Spr

awl

(SN

Di)

Palau

F.11 City trend plots (by region)The following plots show city trends in each region. Each line represents one city; in some cases thereis more than one city per country. For more detail, see the tabular data. The following pages showresults only for SNDi. Other metrics are available online at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl.

66

<1990 90-99 2000-13

0

2

4

6

8

10

WorldSprawl (SNDi)

<1990 90-99 2000-13

0

2

4

6

8

10 AfricaDZA

COD

NGA

EGY

AGO

MOZ

UGA

MAR

RWA

MLI

TZA

KEN

ZAF

ZMB

SDN

ETH

TUN

GHA

<1990 90-99 2000-13

0

2

4

6

8

10 European UnionNLD

LTU

FRA

ITA

DEU

GRC

GBR

ESP

AUT

BEL

POL

HUN

<1990 90-99 2000-13

0

2

4

6

8

10

East Asia &Pacific (allincome levels)

<1990 90-99 2000-13

0

2

4

6

8

10 South AsiaIND

NPL

BGD

PAK

AFG

<1990 90-99 2000-13

0

2

4

6

8

10 North AmericaCAN

USA

<1990 90-99 2000-13

0

2

4

6

8

10 Latin America &Caribbean (allincome levels)

COL

SLV

MEX

BRA

CHL

GTM

CUB

NIC

ECU

ARG

BOL

VEN

<1990 90-99 2000-13

0

2

4

6

8

10 Central Europe andthe Baltics

HUN

LTU

POL

<1990 90-99 2000-13

0

2

4

6

8

10

High income

<1990 90-99 2000-13

0

2

4

6

8

10

Middle income

<1990 90-99 2000-13

0

2

4

6

8

10 Low incomeCOD

ETH

PRK

TZA

MLI

RWA

NPL

MOZ

UGA

AFG

<1990 90-99 2000-13

0

2

4

6

8

10 Middle East &North Africa (allincome levels)

ISR

IRQ

SAU

IRN

MAR

TUN

DZA

EGY

YEM

<1990 90-99 2000-13

0

2

4

6

8

10 Arab WorldSprawl (SNDi)

EGY

IRQ

YEM

SAU

MAR

SDN

TUN

DZA

<1990 90-99 2000-13

0

2

4

6

8

10 Sub-Saharan Africa(developing only)

ZAF

COD

UGA

RWA

NGA

AGO

SDN

MOZ

KEN

MLI

TZA

ZMB

ETH

GHA

<1990 90-99 2000-13

0

2

4

6

8

10 Euro areaNLD

FRA

BEL

ESP

AUT

DEU

GRC

ITA

LTU

<1990 90-99 2000-13

0

2

4

6

8

10

OECD members

<1990 90-99 2000-13

0

2

4

6

8

10 Heavily indebtedpoor countries(HIPC)

ZMB

COD

RWA

SDN

UGA

AFG

NIC

BOL

MOZ

MLI

TZA

GHA

ETH

<1990 90-99 2000-13

0

2

4

6

8

10 Middle East &North Africa(developing only)

IRN

DZA

MAR

YEM

TUN

EGY

IRQ

<1990 90-99 2000-13

0

2

4

6

8

10

Lower middleincome

<1990 90-99 2000-13

0

2

4

6

8

10 Europe & CentralAsia (developingonly)

UZB

TUR

AZE

KAZ

SRB

UKR

BLR

<1990 90-99 2000-13

0

2

4

6

8

10

IDA & IBRD total

<1990 90-99 2000-13

0

2

4

6

8

10 IDA onlyYEM

MOZ

MMR

ZMB

COD

NIC

RWA

NPL

SDN

UGA

AFG

BGD

KEN

GHA

ETH

MLI

TZA

<1990 90-99 2000-13

0

2

4

6

8

10 Sub-Saharan Africa(all incomelevels)

ZAF

COD

UGA

RWA

NGA

AGO

SDN

MOZ

KEN

MLI

TZA

ZMB

ETH

GHA

<1990 90-99 2000-13

0

2

4

6

8

10

Low & middleincome

<1990 90-99 2000-13

0

2

4

6

8

10

IDA totalSprawl (SNDi)

<1990 90-99 2000-13

0

2

4

6

8

10 Fragile andconflict affectedsituations

YEM

AFG

COD

SDN

MMR

MLI

IRQ

<1990 90-99 2000-13

0

2

4

6

8

10 Latin America &Caribbean(developing only)

COL

BRA

CUB

MEX

ECU

SLV

BOL

NIC

GTM

<1990 90-99 2000-13

0

2

4

6

8

10

East Asia &Pacific(developing only)

<1990 90-99 2000-13

0

2

4

6

8

10

Small statesFJI

<1990 90-99 2000-13

0

2

4

6

8

10

Europe & CentralAsia (all incomelevels)

<1990 90-99 2000-13

0

2

4

6

8

10

IBRD only

<1990 90-99 2000-13

0

2

4

6

8

10

High income: OECD

<1990 90-99 2000-13

0

2

4

6

8

10 High income:nonOECD

RUS

ARG

LTU

VEN

SAU

SGP

<1990 90-99 2000-13

0

2

4

6

8

10 Least developedcountries: UNclassification

YEM

BGD

MMR

COD

AGO

NPL

RWA

SDN

AFG

UGA

ZMB

TZA

MLI

ETH

MOZ

<1990 90-99 2000-13

0

2

4

6

8

10

Upper middleincome

<1990 90-99 2000-13

0

2

4

6

8

10 IDA blendVNM

NGA

UZB

PAK

MNG

BOL

<1990 90-99 2000-13

0

2

4

6

8

10

Pacific islandsmall statesSprawl (SNDi)

FJI

F.12 City trend plots (by country)In the plots below, the widest gray line is centered on the trend for the entire country’s urban streetnetwork, while the less wide gray line is the node-weighted average over only the cities shown (i.e., allthose available in the Atlas of Urban Expansion). Quantities are exact; line width does not representconfidence intervals. Countries are ordered by the number of nodes in the 2018 road stock. For moredetail, see the tabular data.

The following pages show results only for SNDi. Other metrics are available online at:https://alum.mit.edu/www/cpbl/publications/2020-PNAS-sprawl.

71

0

2

4

6

8

10 United StatesSprawl (SNDi)

New York

Cleveland

Gainesville

Killeen

Modesto

Los Angeles

Springfield

Toledo

Raleigh

Portland

Minneapolis

Philadelphia

Houston

Chicago

JapanTokyo

Osaka

Fukuoka

Okayama

Yamaguchi

United KingdomLondon

Manchester

Sheffield

BrazilSao Paulo

Belo Horizonte

Curitiba

Florianopolis

Ribeirao Preto

Palmas

Jequie

Ilheus

0

2

4

6

8

10 MexicoMexico City

Guadalajara

Tijuana

Culiacan

Reynosa

ChinaGuangzhou

Pingxiang

Yucheng

Yulin

Kaiping

Yiyang

Leshan

Beijing

Xingping

Bicheng

Guixi

Xucheng

Yanggu

Gaoyou

Suining

Chengguan

Changzhi

Anqing

Zunyi

Qingdao

Zhuji

Shanghai

Taipei

Shenzhen

Hangzhou

Tianjin

Chengdu

Wuhan

Zhengzhou

Hong Kong

Jinan

Changzhou

Tangshan

Haikou

IndiaHyderabad

Belgaum

Sitapur

Hindupur

Jalna

Malegaon

Jaipur

Kozhikode

Singrauli

Kanpur

Vijayawada

Ahmedabad

Coimbatore

Pune

Mumbai

Kolkata

ArgentinaBuenos Aires

Cordoba

<1990 90-99 2000-13

0

2

4

6

8

10 FranceParis

Le Mans

<1990 90-99 2000-13

Korea, Rep.Seoul

Busan

Gwangju

Cheonan

Jinju

<1990 90-99 2000-13

Iran, Islamic Rep.Tehran

Ahvaz

Qom

Gorgan

<1990 90-99 2000-13

TurkeyIstanbul

Kayseri

Malatya

0

2

4

6

8

10 ThailandSprawl (SNDi)

Bangkok

South AfricaJohannesburg

Port Elizabeth

PhilippinesManila

Cebu City

Bacolod

ChileSantiago

0

2

4

6

8

10 ItalyMilan

Palermo

AustraliaSydney

SudanKhartoum

CanadaMontreal

Victoria

<1990 90-99 2000-13

0

2

4

6

8

10 IndonesiaMedan

Palembang

Cirebon

Pematangsiantar

Parepare

<1990 90-99 2000-13

Saudi ArabiaRiyadh

<1990 90-99 2000-13

ColombiaBogota

Valledupar

<1990 90-99 2000-13

Russian FederationMoscow

Saint Petersburg

Astrakhan

Tyumen

Berezniki

Dzerzhinsk

0

2

4

6

8

10 NigeriaSprawl (SNDi)

Lagos

Ibadan

Gombe

Oyo

PakistanLahore

Karachi

Sialkot

VietnamHo Chi Minh City

Vinh Long

IraqBaghdad

0

2

4

6

8

10 Egypt, Arab Rep.Cairo

Alexandria

GhanaAccra

SpainMadrid

AngolaLuanda

<1990 90-99 2000-13

0

2

4

6

8

10 Congo, Dem. Rep.Kinshasa

Lubumbashi

<1990 90-99 2000-13

GermanyBerlin

Halle

Oldenburg

<1990 90-99 2000-13

GuatemalaGuatemala City

<1990 90-99 2000-13

HungaryBudapest

0

2

4

6

8

10 UzbekistanSprawl (SNDi)

Tashkent

Bukhara

Venezuela, RBCaracas

Cabimas

PolandWarsaw

New ZealandAuckland

0

2

4

6

8

10 EthiopiaAddis Ababa

EcuadorQuito

BelgiumAntwerp

AustriaVienna

<1990 90-99 2000-13

0

2

4

6

8

10 MaliBamako

<1990 90-99 2000-13

BangladeshDhaka

Rajshahi

Saidpur

<1990 90-99 2000-13

AlgeriaAlgiers

Tebessa

<1990 90-99 2000-13

IsraelTel Aviv

0

2

4

6

8

10 SingaporeSprawl (SNDi)

Singapore

AfghanistanKabul

MalaysiaIpoh

Rawang

AzerbaijanBaku

0

2

4

6

8

10 El SalvadorSan Salvador

GreeceThessaloniki

BoliviaCochabamba

Yemen, Rep.Sana

<1990 90-99 2000-13

0

2

4

6

8

10 SerbiaBelgrade

<1990 90-99 2000-13

UgandaKampala

<1990 90-99 2000-13

MoroccoMarrakesh

<1990 90-99 2000-13

MongoliaUlaanbaatar

0

2

4

6

8

10 KazakhstanSprawl (SNDi)

Shymkent

LithuaniaKaunas

NetherlandsZwolle

UkraineRovno

Nikolaev

0

2

4

6

8

10 ZambiaNdola

TunisiaKairouan

SwitzerlandLausanne

Korea, Dem. People’s Rep.Pyongyang

<1990 90-99 2000-13

0

2

4

6

8

10 CubaHolguin

<1990 90-99 2000-13

MozambiqueBeira

<1990 90-99 2000-13

RwandaKigali

<1990 90-99 2000-13

NepalPokhara

0

2

4

6

8

10 BelarusSprawl (SNDi)

Gomel

FijiSuva

<1990 90-99 2000-13

NicaraguaLeon

<1990 90-99 2000-13

KenyaNakuru

<1990 90-99 2000-13

0

2

4

6

8

10 MyanmarMyeik

<1990 90-99 2000-13

TanzaniaArusha