supporting informationfor: global trends towards …...2020/01/13 · north america 1975 1990 2000...
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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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<1975 1975-89 1990-99 2000-13
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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
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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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-89
1990
-99
2000
-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
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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.
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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