Bright lights, big city: Causal effects of population and GDP on urban brightness
Bright lights, big city: Causal effects of population and GDP on urban brightness
Yuhao Lu 0 1
Nicholas C. Coops☯ 0 1
0 Integrated Remote Sensing Studio, Forest Recourses Management, University of British Columbia , Vancouver, BC , Canada
1 Editor: Lixiang Li, Beijing University of Posts and Telecommunications , CHINA
Cities are arguably both the cause, and answer, to societies' current sustainability issues. Urbanization is the interplay between a city's physical growth and its socio-economic development, both of which consume a substantial amount of energy and resources. Knowledge of the underlying driver(s) of urban expansion facilitates not only academic research but, more importantly, bridges the gap between science, policy drafting, and practical urban management. An increasing number of researchers are recognizing the benefits of innovative remotely sensed datasets, such as nighttime lights data (NTL), as a proxy to map urbanization and subsequently examine the driving socio-economic variables in cities. We further these approaches, by taking a trans-pacific view, and examine how an array of socio-economic ind0icators of 25 culturally and economically important urban hubs relate to long term patterns in NTL for the past 21 years. We undertake a classic econometric approachÐ panel causality tests which allow analysis of the causal relationships between NTL and socio-economic development across the region. The panel causality test results show a contrasting effect of population and gross domestic product (GDP) on NTL in fast, and slowly, changing cities. Information derived from this study quantitatively chronicles urban activities in the pan-Pacific region and potentially offers data for studies that spatially track local progress of sustainable urban development goals.
Data Availability Statement: Remote sensing and
census data used in this manuscript are freely
accessible for the general public. Landsat images
can be downloaded from USGS Earth Explore via
https://earthexplorer.usgs.gov/. Nighttime lights
images (version 4) can be downloaded from NOAA
Appendix provides the source and link(s) to local
GDP and population information used in this
Funding: This work was supported by funding
provided to Coops by Natural Sciences and
Cities have multi-faceted descriptions, including the permanent areas of heavily
humaninduced infrastructure and the socio-economic entities that facilitate industrial development
and population growth [
]. City growth, commonly known as urbanization, is thus the
interplay between a city's physical and socio-economic environment. Reliable assessment and
quantification of urbanization is critical to better allocate resources and optimize the efficiency
at which cities develop According to recent estimates , approximately over 50% of the global
human population resides in cities. To accommodate such enormous populations, cities are
responsible for nearly 78% of global carbon emissions, 60% of water use, and 76% of wood
]. Equally substantial is the regional variability across cities. Urbanization has
Engineering Research Council of Canada (RGPIN
shifted from developed countries to less developed regions such as south-east Asia, which is
seeing the fastest and most intense development activities, a trend that is predicted to continue
well into this century. In North America, more than 80% of the population lives in urbanized
areas, while in Asia this number falls to 48% [
]. Yet from 2000 to 2010 approximately 200
million people migrated from rural environments to cities in East Asia alone .
Key reliable, and consistent, measurements of urbanization are critical to understanding
this ongoing movement of people into urban environments, and typically fall into two main
categories. First consists of demographic metrics such as births, deaths, immigration, and
migration, as well as derived estimates of population size and density. The second is associated
with the wealth of a city such as regional gross domestic product (GDP). Data can be acquired
in a number of ways. Population data are often recorded through census where the resident
population is polled locally using forms and interviews. Alternatively, economic data are most
often complied directly by state government or local administrative units. Census and
economic data are often in tabular format with limited value for monitoring spatially explicit
changes that are needed in urban studies [
]. It was not until the 1970s that remote sensing
satellite imagery became an alternative data source for monitoring city growth in a more
repeatable and comprehensive manner, and has the potential to offer a richer source of information
than conventional survey data alone [
]. However, most urban remote sensing applications
mainly focused on extracting physical features such as delineating city boundaries [
mapping and quantifying land cover changes [
]. Characterizing the socioeconomic nature of cities
has still primarily remained the domain of census data.
In 1992 the first digital nighttime lights (NTL) data were acquired by the Defense
Meteorological Satellite Program's Operational Linescan System (DMDP/OLS) and was released by
NOAA's National Geographical Data Center (NGDC). NTL has been used extensively to track
urban activity and its associated temporal characteristics, enabling researchers and urban
planners to quantitatively compare and contrast spatio-temporal patterns. The full NTL temporal
record enables us to chronicle the development of urban patterns and produce spatially explicit
estimates that reflect a city's growth or decline [
]. Early studies such as Welch et al. (1980)
] use NTL to model urban population and energy consumption, while Croft (1973) [
uses the nighttime space photographs to map burning waste in oil fields [
]. More recently,
digital NTL data have been increasingly used on mapping urban and urbanization related
human activities such as delineating urban expansion [
], modelling economic activities [
and CO2 emissions [
]. Yet, the interpretation of NTL brightness, also known as Digital
Number (DN) values, can be highly subjective and varies from study to study [
]. In this
paper we interpret NTL values in a more general fashion to represent overall human activities.
Thus, we assume that an increasing NTL value is indicative of increasing human activity rather
than directly linked to one specific variable as in previous studies.
Much of the existing research has shown encouraging results correlating NTL with other
ancillary variables such as GDP and population size. However, few have investigated the causal
interaction between NTL and socio-economic development. Analyzing the causal
relationships between NTL and socio-economic variables can be more valuable than traditional
correlation approaches for understanding the drivers of city growth, as well as prioritizing
longterm policy drafting and practical urban planning. For example, Hoffmann et al. [
the causal relationship between Foreign Direct Investment (FDI) and pollution level,
discovering a significant direction of causality from FDI to CO2 level in middle-income nations. Seto &
] examined socioeconomic drivers of urban land use changes in the Pearl River
Delta using high spatial resolution satellite data and demographic records from 1988 to 1996.
In this work, we extend the spatial coverage to 25 cities in pan-Pacific region using annual
composites of NTL composite from 1992 to 2012. We first intercalibrate NTL data using a
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localized modelling approach to ensure temporal consistency and minimize any effect caused
by saturated NTL pixels [
]. Second, we spatially delineate and track urbanization patterns
using the Theil-Sen estimator for 25 urban environments across the pan-Pacific region. Then,
we examine the causality of two common socio-economic variables (population and GDP) on
NTL using panel Granger causality procedures. This approach allows a statistical verification
of the possible drivers of urban development by examining the effect of exogenous macro-level
socio-economic factors on physical city growth. This work demonstrates new ways of
investigating relationships between NTL data and socio-economic development.
Materials and methods
We selected 25 urban environments across the pan-Pacific region, covering 12 countries across
a broad spectrum of population size, economic, and ecological conditions. Urban
environments located in less developed regions (e.g. Eastern and South-eastern Asia, and South
America) were expected to have greater increasing anthropogenic activity level (i.e. increasing
nighttime lights) compared to more developed areas (e.g. North America). Mega-cities such as
Manila, Mexico City, and Tokyo were also included to represent urban changes in highly
populated areas with contrasting economic conditions. Ecologically, urban environments such as
Las Vegas offer a unique perspective on how cities develop in less suitable environmental
conditions (e.g. arid environments). We also included urban environments such as Changsha and
Nanchang, China, which are not often in the spotlight of urban studies but are of critical
cultural and economic importance.
Intercalibrate nighttime lights time series
Urban boundaries, particularly administrative boundaries, are often vaguely defined and
highly dependent on local jurisdiction systems [
]. Rather than using an administrative
boundary, a 60-km radius circular buffer was generated for each urban environment.
Waterbodies were masked out from subsequent analysis using a previously generated water mask
]. Annual average visible cloud-free nighttime lights composites (Version 4) were
acquired from NOAA (http://ngdc.noaa.gov/eog/dmsp.html) covering 1992±2013. Images
were formatted as Digital Numbers (DNs) ranging from 0 to 63, with a higher DN
representing greater illumination or brightness of lights.
Due to the lack of an onboard calibration mechanism, robust intercalibration is a critical
step to allow images from different years or sensors to be directly comparable. Recently Pandey
et al. [
] quantitatively evaluated nine most commonly used intercalibration techniques using
a Summed Normalized Difference Index (SNDI, Equation 1). Similar to Zhang et al. [
Elvidege et al. [
], we built a 3rd degree polynomial model to calibrate each image to a
reference year. A reference year is selected based on maximal DN values across the selected cites,
an approach which has been used previously [25±29]. Rather than using one single model for
all cities, we fit a polynomial model for each individual city to account for local NTL
We evaluate our calibration results for each city using SNDI, which quantifies the level of
convergence in NTL temporal series of a given city (2017). SNDI is the total of Normalized
Difference Index (NDI, Equation 2) and assesses the absolute difference of total DN values
(TDN, Equation 3) between two sensors in the same year. As suggested by Zhang et al. [
and Pandey et al. [
], an effective intercalibration should yield a much lower SNDI than the
raw images. Our intercalibration SNDI was then compared against raw data, Zhang et al. [
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and Elvidge et al [
1t and 2t represent NDI values at a given time t f rom 2 dif f erent working sensors
A challenge associated with NTL data is pixel saturation, which can occur due to the limited
radiometric range of NTL sensors. Recently Zhang et al. [
] incorporated a series of
vegetation images to de-saturate NTL data on the assumption of an inverse relationship between
vegetation abundance and NTL brightness. However, since our goal of this study is to investigate
the casual relationship between NTL, GDP and population, the inclusion of another input
variable (e.g. vegetation) complicates the process of interpreting statistical analysis. In addition,
Zhang et al. [
] suggested a limited improvement of NTL variability for fast growing cities
compared to more established legacy cities. The inverse relationship between NTL and
vegetation may not hold for developing cities in this study. As a result, NTL images used in this study
are calibrated but not alerted to accommodate potential saturation issues.
Generate NTL temporal trend
As indicated by previous studies, a ªlitº pixel does not necessarily coincide with human
activities due to the potential ªbloomingº effect caused by diffused or scattered light from
neighboring pixels [
]. We therefore used a DN of 12 as a threshold between lit and non-lit, or dimed,
]. We then generated NTL trends for all 25 urban environments over the 21 years. In
order to capture any development in initially low-lit areas, NTL trends were generated for all
non-water pixels, including the ones with a DN value below 12. A Mann-Kendall
nonparametric test [
] was used to determine the significance of the monotonic trend in NTL.
The TS estimator, which has been widely used with time series data [
] to describe
temporal change in intensity, was applied to pixels identified by Mann-Kendall as statistically
significant (p < 0.05). Those pixels were then used to calculate the trend slope values based on the
median of pairwise data points from 1992 to 2013.
Based on the slope values, we then grouped the 25 cities into two classes. The first class
contained cities that have experienced rapid NTL increase over the 21-year period. The second
class represented cities with much lower or no slope trend in NTL, indicative of little urban
growth over the time. We also examined the NTL with a predefined threshold (e.g. DN = 12)
to determine in which year a given pixel exceeded the threshold value and its urban
establishment passed the brightness threshold.
Granger causality test
Although NTL has been extensively used as a proxy to anthropogenic activities, sophisticated
and well-tested econometric tools have rarely been applied with the NTL time series. The most
notable challenge is that econometric tools often require decadal or even centurial time series
as input in order to capture the often weak relationship between two given economic variables
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]. NTL imagery collected by DMSP/OSL has a relatively short time span (i.e. 1992±2013)
and therefore is often not well suited for econometric tools. Recent studies [
], however, have
shown encouraging results for utilizing relatively short time series of data for causation testing
through panel data that are a collection of entities (e.g. cities), where the variables are observed
Statistically, the Granger causality test [
] describes the strength of association between
two time series by testing whether or not the inclusion of one time series (xt) can improve the
forecasting of future values in another time series (yt). If the addition of xt significantly
improves a model's explanatory power in predicting yt, we could conclude that xt ªgranger
The panel version of the Granger causality test combines individual short-time series data
in the form of cross-sectional structures that increase the test efficiency and power by raising
the number of observations and degrees of freedom [
]. In this work, a total of three panel
data sets were generated for causality testing, namely, total DN (TDN), total population
(TPOP_Total), and total GDP (TGDP_Total), for each of the 25 cities (see data source in Appendix 1). As a
result, each panel data set had a total of 25 cross-sections (N = 25 cities) and 22 temporal units
(T = 21 years). Total DN was calculated as the sum of DN values of all lit pixels for each year to
represent both the area and intensity of the NTL. We also examined the differences between
cities that are rapidly developing (N = 13) versus those that are more established (N = 12).
Granger causality tests require all panel data to be stationary and co-integrated based on
two panel unit root tests; the Levin & Lin [
], known hereafter as the LLC test and Im &
], known hereafter as IPS. The panel co-integration test of Johansen [
applied to examine co-integration among all pairs of temporal variables (see test results in
The rejection of Granger causality tests H0 (p < 0.01) indicates a unidirectional causal
relationship from one input variable to the other. We employed the panel Granger causality test
proposed by Dumitrescu and Hurlin [
], hereafter DH, which respects the heterogeneity
within relatively small panel data sets.
Intercalibrating NTL time series
Overall, all calibration methods successfully reduced the systematic biases in the NTL images
with a lower SNDI than for the raw data across most cities (Fig 1). Although Zhang et al.
(2016) and Elvidge et al. (2014) yield lower SNDI at the global scale, our city level calibration
shows a marginally better calibration result in terms of minimizing systematic biases. Haikou
(HAK), Nanchang (NCX), and Vancouver (VAN) all have a relatively higher SNDI value
compared to the other cities tested.
Quantifying spatio-temporal changes
Large inter- and intra-city variations are apparent; for example, in Denver (DEN), steeper
slopes are clustered in the north and east of the city while in Kuala Lumpur (KUL), intensive
NTL changes are located in the south (Fig 2). The majority of pixels with rapid change are
found in less developed cities (e.g. HAR) while more developed cities exhibit more stable NTL
trends (e.g. CAL). Variations within a city also clearly highlight NTL change hotspots, such as
the growth of surrounding satellite cities during the study period (e.g. BAK, SHH).
Spatially, the recent urban development generally occurred on the outer rings of each
urban area (Fig 3; e.g. FUZ and CSX). Timing of urban development was also variable with
cities, such as Seoul and Kuala Lumpur, which were dominated by land cover changes in the
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Fig 1. Sum of normalized difference index (SNDI) derived from raw images, Zhang et al., (2016), Elvidge et al.,
(2014), and Lu (this paper).
early stage of the time series, while changes in Changsha and Dalian were relatively more
We observed a wide range of variation within and across all 25 cities in urban development
(Fig 4). For example, Tokyo (TKO) and Shen ZhenÐHong Kong (HKSZ) have over 75% of
land urbanized prior to 1992 while most cities in China have less than 10%. Cities such as
Shanghai (SHH) and Tianjin (TJN) experienced substantial growth over the period studied
with nearly 50% of their land crossing the pre-defined threshold value. A few cities, however,
had less growth with approximately 75% of land remaining undeveloped.
Granger causality test
The causality test results differ depending on the city analyzed (Fig 5). Expectedly, across all
cities, both population and GDP play a major role in directing changes of NTL. Additionally
GDP and NTL also ªgranger causeº changes in population (p < 0.01) (Fig 5A). This implies
that the brightness of cities follows increases in both population and GDP equally and that
neither population nor GDP alone is responsible for increasing the NTL. Among all cities, the test
also unexpectedly suggests GDP and NTL ªgranger causeº population growth suggesting that
population change is the outcome rather than the cause of urban development (Fig 5A).
After stratifying the cities by development stage we find contrasting and unexpected results.
In the case of more established cities with few NTL changes over the analysis period, the causal
relationship from NTL to population is no longer significant yet changes in population
ªgranger causeº both GDP and NTL (Fig 5B). This suggests that in cities with relatively stable
NTL, population and GDP are likely the key drivers of local economic and urban development,
and not the other way around.
For fast changing and more dynamic cities there are only two significant casual
relationships±growth in NTL or GDP leading to an increase in population. Unexpectedly, there is no
significant causal association between GDP and NTL (dashed lines in Fig 5C). This suggests
that in rapidly changing cities population increases are driven by brighter and more
economically active urbanization.
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Fig 2. NTL change rate represented by Theil-Sen slope values showing the rate of change from 1992 to 2013. Water is colored as white. Cities were grouped
based on their growth intensity. Left panel contains cities with fast and more dynamic urban growth, while the right panel includes cities with more stable and
Intercalibration of nighttime time series
Our calibration method successfully minimizes the systematic biases at the city scale, enabling
direct comparisons among images taken by different sensors (Fig 1). Pandey et al. [
that a global calibration (i.e. national level) could outperform regional models, however this
does not appear to be the case in this study. We found that intercalibrated models at the city
scale achieved relatively lower SNDI across all 25 cities than using calibration parameters from
previous studies (Fig 1). One rationale is that the majority of the pixels used in the calibration
process are brightly lit (i.e. pixels located in urban areas), which have a much higher
contribution to the overall SNDI statistics than dimly lit pixels [
]. Future studies could focus on
developing a more systematic procedure to select reference images and empirical models to
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Fig 3. The year when a given pixel within each urban environment exceeded the pre-defined DN value. Dark grey pixels represent existing urban areas
prior to 1992, while light grey indicates areas with no sufficient light sources in 2013.
accommodate for the need of individual studies at varying spatial scales. It is also noticeable
that the calibration performance varies across cites (Fig 1). Images with larger portions of
dimly lit pixels are more likely to suffer from less optimal calibration due to the existence of
random noise and the skewed radiometric DN values. Island cities or cities surrounded by
large green spaces may have a relatively less even distribution of DN values, which may explain
our inconsistent calibration performance in Fig 1. Cities with higher SNDI values are either
located in developing regions (e.g. Changsha and Haikou) or cities with higher cover of
vegetation cover (e.g. Vancouver). Generally, those cities have fewer brightly lit pixels than cities
such as Tokyo. Therefore, we conclude that a locally fitted intercalibration model will likely
work better in areas dominated by high DN pixels. Future studies involving areas with a
substantial amount of dark pixels may need a verified image histogram before selecting any
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Fig 4. Urban land breakdown of changed, undeveloped, and existing urban areas, ranking the 25 cities from the
highest proportion of lit pixels (i.e. TKO) to the least (i.e. HAK).
Is a 60-km buffer enough?
It is unsurprising to see that cities with more dynamic and fast changing rates are located in
Asia. According to United Nation's review in 2001, on average, Asian cities are at least 50 years
behind Europe and North America in terms of their urbanization level [
]. Mega-cities in
Asia on the other hand show highly dominating and disproportional impact on regional and
national economic development. Studies [
] have suggested that urban dwellers have an
overall better living standard, such as education and consumption level, hence attracting a
substantial amount of migrants from rural areas. Understanding the spatial pattern and the timing of
urban development in these fast changing cities can offer valuable information on efficient
land resources allocation, which can further reduce the per capita cost of infrastructure and
basic services [
]. In contrast, urbanization tends to be less concentrated in more developed
cities due to their advanced urban network [
]. As a result we noticed that while a 60-km
radius buffer was sufficient for fast changing and more dynamic cities, it is clearly not large
enough to capture recent urbanization activities in more developed cities (Figs 2±3). Other
alternatives such as algorithmically derived urban extent have been used in previous literature,
focusing on primarily tracking urban land cover and land use over time. This approach,
however, still requires a fixed boundary to define where the city ends.
Although this study uses a ground distance of 60 km to delineate the urban boundary,
other distance measuring approaches such as travel time ratio [
] or Manhattan distance [
may affect the casualty tests. One limitation regarding the boundaries of selected cities is the
spatial scale difference between remote sensing data and census record, which is often
collected using administrative units. The scale inconsistency between these two data sets may
alter the final results. Yet, since the census data used in this work represents the metropolitan
area, which in general covers a relative larger area than normal administrative units, we believe
our results are still valuable in decoupling the relationship between NTL and socio-economic
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Fig 5. Causal interactions among NTL (nighttime lights), POP (population size), and GDP (Gross Domestic Product) of a) all cities, b) established cities, and
c) dynamic cities. A solid line represents a statistically significant causal relationship, while a dotted line indicates no significant causality. An arrow head
indicates the direction of causal relationship, and a double-headed arrow represents a bi-directional causal relationship.
Chicken or the egg: Causal relationship between NTL and socio-economic factors
A large number of econometric studies have reported inconsistent results when examining
interactions between socio-economic and environmental variables. Mozumder and Marathe
] summarized a number of studies and found mixed causal relationship results depending
on the study location, types of variables, and duration of time series used. Knapp [
] tested the
underlying interaction between population growth and global CO2, and concluded a weak
long-term equilibrium but strong short-term relationships between population and CO2. Seto
& Kaufmann [
] also employed panel causality procedures with remotely sensed images to
estimate the economic drivers of land conversion in urban areas and concluded that investment
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in capital construction is driving urban land conversion. It has long been thought that
population is the primary driver of urban growth while economic development is rather an outcome
of urbanization. In this work, we found that population and GDP revealed contrasting effects
on NTL trends between stable and more dynamic cities. Statistically, changes in NTL are
significantly driven by both population and GDP growth in more established and slow changing cities.
Previous work [
] has indicated that rather than growing population alone, it is the high
consumption lifestyle, as well as economic and political decisions that lead to urban growth.
Our results also show that in more developed cities it is in fact both the population and
economic development that drives urban growth.
However, in fast changing, yet often less developed, cities the growth of NTL and GDP are
driving population changes rather than the other way around. In those cities, a major source of
population increase is through large in-migration from rural and neighboring areas, and
involves densification and conversion of existing farm, forest, or barren land to urban land
cover types [
]. Our results suggest that migration is more attracted to cities with promising
economic conditions and undergoing fast urbanization paces.
Consistent and comparable measurement is key to evaluating sustainable urban
development. Satellite derived images produce spatially explicit profiles of cities' development. The
integration of time series of satellite images and conventional census-based measurements
reveals new ways of monitoring urban development, offering continuous and calibrated
measurements of city brightness. Combined with temporally consistent variables, this work also
offers valuable input toward local urban planning, such as resources allocation and public
transportation networking [
]. Furthermore, given the nature of satellite-derived data, such
information is also highly comparable for large-scale cross-city comparison studies.
The new nighttime lights images from the Visible Infrared Imaging Radiometer Suite (VIIRS)
Day/Night Band (DNB) extend the timeline of such research to 2017 with more spatially and
radiometrically improved data. Using the proposed panel causality tests, researchers and
urban planners can quantitatively evaluate effects of population and GDP on city brightness
during more recent years. Information derived from this work not only offers insight into the
performance of past urban development and local policy effectiveness, but also builds a strong
foundation for guiding future sustainable development strategies. The extended time series of
data also provides a better foundation for investigating temporal lag or delay between
nighttime lights changes and real-time census derived variables [52±54]. The quantification of such
delays could be an ideal candidate indicator to measure the effectiveness of local policy and
Pervious studies [
] have also suggested the critical role of Foreign Direct Investment
(FDI) in driving and reshaping urban development. However, FDI is not a common metric
across all 25 cities. In developing regions, previous research indicated a bi-directional casual
relationship between FDI and GDP . Similar results were found in Li & Liu [
] where 84
countries were tested using panel data from 1970 to 1999. Although GDP is a common and justified
metric representing a common and justified metric representing a city's economic performance
in this work, other metrics such as FDI could be further investigated to more comprehensively
examine the relationship between city brightness and economic development.
Contemporary cities are collectively more dynamic, multi-dimensional, and complex.
Urbanization and its associated physical and socio-economic characteristics are interacting at a much
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faster pace and occurring much beyond local level. In this work, such characteristics are
reflected by inter- and intra-city variations derived from the nighttime lights imagery for 25
cities in the pan-Pacific region. Panel causality tests were applied to statistically examine the
long-run interaction between NTL and socio-economic variables. This work has the advantage
of being intuitively appealing, as well as simple to reproduce and implement in practical urban
planning and management. The derived information is critical for city planners to efficiently
compare, visualize, and allocate resources, as well as policy-makers to quantitatively evaluate
inter-and city development in a polycentric and highly connected global urban system.
S1 Appendix. Appendix 1 Census data sources summary for each city. Appendix 2 Granger
causality test results.
This work was supported by funding provided to Coops by NSERC (RGPIN 311926±13). We
thank Dr. Jeanine Rhemtulla for her valuable comments, and Yu Chen and Marie Nosten for
collecting and translating local census data for this work.
Conceptualization: Nicholas C. Coops.
Data curation: Yuhao Lu.
Formal analysis: Yuhao Lu.
Funding acquisition: Nicholas C. Coops.
Investigation: Yuhao Lu.
Methodology: Yuhao Lu, Nicholas C. Coops.
Software: Yuhao Lu.
Supervision: Nicholas C. Coops.
Validation: Yuhao Lu.
Visualization: Yuhao Lu.
Writing ± original draft: Yuhao Lu.
Writing ± review & editing: Nicholas C. Coops.
12 / 15
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