research paper of urbanization

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• Published: 02 January 2020

Urbanization: a problem for the rich and the poor?

• Md Abdul Kuddus 1 , 2 , 4 ,
• Elizabeth Tynan 3 &
• Emma McBryde 1 , 2  

Public Health Reviews volume  41 , Article number:  1 ( 2020 ) Cite this article

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Urbanization has long been associated with human development and progress, but recent studies have shown that urban settings can also lead to significant inequalities and health problems. This paper is concerned with the adverse impact of urbanization on both developed and developing nations and both wealthy and poor populations within those nations, addressing issues associated with public health problems in urban areas. The discussion in this paper will be of interest to policy makers. The paper advocates policies that improve the socio-economic conditions of the urban poor and promote their better health. Further, this discussion encourages wealthy people and nations to become better informed about the challenges that may arise when urbanization occurs in their regions without the required social supports and infrastructure.

Urbanization refers to the mass movement of populations from rural to urban settings and the consequent physical changes to urban settings. In 2019, the United Nations estimated that more than half the world’s population (4.2 billion people) now live in urban area and by 2041, this figure will increase to 6 billion people [ 1 ].

Cities are known to play multifaceted functions in all societies. They are the heart of technological development and economic growth of many nations, while at the same time serving as a breeding ground for poverty, inequality, environmental hazards, and communicable diseases [ 2 ]. When large numbers of people congregate in cities, many problems result, particularly for the poor. For example, many rural migrants who settle in an urban slum area bring their families and their domesticated animals—both pets and livestock—with them. This influx of humans and animals leads to vulnerability of all migrants to circulating communicable diseases and the potential to establish an urban transmission cycle. Further, most urban poor live in slums that are unregulated, have congested conditions, are overcrowded, are positioned near open sewers, and restricted to geographically dangerous areas such as hillsides, riverbanks, and water basins subject to landslides, flooding, or industrial hazards. All of these factors lead to the spread of communicable and non-communicable diseases, pollution, poor nutrition, road traffic, and so on [ 3 , 4 , 5 ]. The problems faced by the poor spill over to other city dwellers. As the trend to urbanization continues, this spillover effect increases and takes on a global dimension as more and more of the world’s populations are affected [ 3 ].

Some of the major health problems resulting from urbanization include poor nutrition, pollution-related health conditions and communicable diseases, poor sanitation and housing conditions, and related health conditions. These have direct impacts on individual quality of life, while straining public health systems and resources [ 6 ].

Urbanization has a major negative impact on the nutritional health of poor populations. Because they have limited financial resources and the cost of food is higher in cities, the urban poor lack nutritious diets and this leads to illness, which contributes to loss of appetite and poor absorption of nutrients among those affected. Furthermore, environmental contamination also contributes to undernutrition; street food is often prepared in unhygienic conditions, leading to outbreaks of food-borne illnesses (e.g., botulism, salmonellosis, and shigellosis) [ 6 ]. Urban dwellers also suffer from overnutrition and obesity, a growing global public health problem. Obesity and other lifestyle conditions contribute to chronic diseases (such as cancers, diabetes, and heart diseases). Although obesity is most common among the wealthy, international agencies have noted the emergence of increased weight among the middle class and poor in recent years [ 7 ].

Populations in poor nations that suffer from protein-energy malnutrition [ 8 ] have increased susceptibility to infection [ 9 ] through the impact of micronutrient deficiency on immune system development and function [ 10 ]. Around 168 million children under 5 are estimated to be malnourished and 76% of these children live in Asia [ 11 ]. At the same time, the World Health Organization is concerned that there is an emerging pandemic of obesity in poor countries that leads to non-communicable diseases such as diabetes, cardiovascular disease, cancer, hypertension, and stroke [ 12 ].

Obesity is caused by increased caloric intake and decreased physical activity [ 13 ], something historically associated with wealth. However, people in urbanized areas of developing countries are also now vulnerable to obesity due to lack of physical space, continually sitting in workplaces, and excessive energy intake and low energy expenditure. In these areas, infrastructure is often lacking, including sufficient space for recreational activities. Further, in developing countries, as in developed countries, large employers frequently place head offices in urban capitals and work is increasingly sedentary in nature [ 14 ]. Another culprit associated with the risk of developing obesity is the change in food intake that has led to the so-called nutrition transition (increased the consumption of animal-source foods, sugar, fats and oils, refined grains, and processed foods) in urban areas. For instance, in China, dietary patterns have changed concomitantly with urbanization in the past 30 years, leading to increased obesity [ 15 ]. In 2003, the World Health Organization estimated that more than 300 million adults were affected, the majority in developed and highly urbanized countries [ 16 ]. Since then, the prevalence of obesity has increased. For example, in Australia, around 28% of adults were obese in 2014–2015 [ 17 ].

Pollution is another major contributor to poor health in urban environments. For instance, the World Health Organization estimated that 6.5 million people died (11.6% of all global deaths) as a consequence of indoor and outdoor air pollution and nearly 90% of air-pollution-related deaths occurred in low- and middle-income countries [ 18 ]. Poor nutrition and pollution both contribute to a third major challenge for urban populations: communicable diseases. The poor live in congested conditions, near open sewers and stagnant water, and are therefore constantly exposed to unhealthy waste [ 6 ]. Inadequate sanitation can lead to the transmission of helminths and other intestinal parasites. Pollution (e.g., from CO 2 emission) from congested urban areas contributes to localized and global climate change and direct health problems, such as respiratory illnesses, cardiovascular diseases, and cancer for both the rich and the poor.

In addition to human-to-human transmission, animals and insects serve as efficient vectors for diseases within urban settings and do not discriminate between the rich and poor. The prevalence and impact of communicable diseases in urban settings, such as tuberculosis (TB), malaria, cholera, dengue, and others, is well established and of global concern.

National and international researchers and policy makers have explored various strategies to address such problems, yet the problems remain. For example, research on solutions for megacities has been ongoing since the early 1990s [ 19 , 20 ]. These studies have concluded that pollution, unreliable electricity, and non-functioning infrastructure are priority initiatives; nevertheless, air pollution, quality of water in cities, congestion, disaster management issues, and infrastructure are not being systematically addressed [ 19 , 20 ].

The impact of inner city transportation on health, such as road traffic, is emerging as a serious problem. Statistics show that a minimum of 10 people die every day on the railways in the city of Mumbai, India [ 21 ]. Vietnam is another example of a country that has seen a remarkable increase in road traffic accidents [ 22 ]. Improvements to the country’s infrastructure have not been able to meet the increasing growth of vehicular and human traffic on the street. Vietnam reportedly has a population of 95 million and more than 18 million motorbikes on its roads. A deliberate policy is needed to reduce accidents [ 21 ].

Although urbanization has become an irreversible phenomenon, some have argued that to resolve the problems of the city, we must tackle the root causes of the problem, such as improving the socio-economic situation of the urban poor.

Until the conditions in rural areas improve, populations will continue to migrate to urban settings. Given the challenges that rural development poses, the root causes are unlikely to be addressed in the near future. Therefore, governments and development agencies should concentrate on adapting to the challenges of urbanization, while seeking to reduce unplanned urbanization.

Some examples of policies and practices that should be considered include (i) policies that consider whole-of-life journeys, incorporating accessible employment, community participation, mobility/migration and social transition, to break generational poverty cycles; (ii) policies addressing urban environmental issues, such as planned urban space and taxes on the use of vehicles to reduce use or to encourage vehicles that use less fuel as well as encourage bicycle use, walking, and other forms of human transportation; (iii) greater cooperative planning between rural and urban regions to improve food security (e.g., subsidies for farmers providing locally produced, unprocessed and low cost food to urban centers); (iv) social protection and universal health coverage to reduce wealth disparity among urban dwellers; including introduction of programs and services for health, for example by establishing primary healthcare clinics accessible and affordable for all including those living in urban slums [ 23 ].

Availability of data and materials

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Acknowledgements

The authors would like to thank the editor for his/her thoughtful comments and efforts towards improving the manuscript.

This work was conducted as a part of a PhD programme of the first authors and funded by the College of Medicine and Dentistry at the James Cook University, Australia (JCU-QLD-933347).

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MAK planned the study, analyzed, and prepared the manuscript. ET and EM helped in the preparation of the manuscript. All authors read and approved the final manuscript.

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Kuddus, M.A., Tynan, E. & McBryde, E. Urbanization: a problem for the rich and the poor?. Public Health Rev 41 , 1 (2020). https://doi.org/10.1186/s40985-019-0116-0

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Research Article

Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations CUNY Institute for Demographic Research, City University of New York, New York, New York, United States of America, Marxe School of Public and International Affairs, Baruch College, City University of New York, New York, New York, United States of America

Roles Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation University of Colorado, Boulder, Colorado, United States of America

Roles Conceptualization, Data curation, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Affiliation Population Council, New York and Stony Brook University, Stony Brook, New York, United States of America

Roles Data curation, Software, Validation, Writing – original draft

Affiliation CUNY Institute for Demographic Research, City University of New York, New York, New York, United States of America

• Deborah Balk, 
• Stefan Leyk, 
• Bryan Jones, 
• Mark R. Montgomery, 
• Anastasia Clark

• Published: December 26, 2018
• https://doi.org/10.1371/journal.pone.0208487
• Reader Comments

Most of future population growth will take place in the world’s cities and towns. Yet, there is no well-established, consistent way to measure either urban land or people. Even census-based urban concepts and measures undergo frequent revision, impeding rigorous comparisons over time and place. This study presents a new spatial approach to derive consistent urban proxies for the US. It compares census-designated urban blocks with proxies for land-based classifications of built-up areas derived from time-series of the Global Human Settlement Layer (GHSL) for 1990–2010. This comparison provides a new way to understand urban structure and its changes: Most land that is more than 50% built-up, and people living on such land, are officially classified as urban. However, 30% of the census-designated urban population and land is located in less built-up areas that can be characterized as mainly suburban and peri-urban in nature. Such insights are important starting points for a new urban research program: creating globally and temporally consistent proxies to guide modelling of urban change.

Citation: Balk D, Leyk S, Jones B, Montgomery MR, Clark A (2018) Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010. PLoS ONE 13(12): e0208487. https://doi.org/10.1371/journal.pone.0208487

Editor: Itzhak Benenson, Tel Aviv University, ISRAEL

Received: January 28, 2018; Accepted: November 19, 2018; Published: December 26, 2018

Copyright: © 2018 Balk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available from: Census Data from https://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml ; Global Human Settlement Layer Data from http://ghsl.jrc.ec.europa.eu/datasets.php .

Funding: The work was funded, in large part, by the US National Science Foundation award #1416860 to the City University of New York, the Population Council, the National Center for Atmospheric Research (NCAR) and the University of Colorado at Boulder, and with additional support from NSF award # CHE-1314040 to Bryan Jones at the City University of New York, an Andrew Carnegie Fellowship (#G-F-16-53680) from the Carnegie Corporation of New York to Deborah Balk. Stefan Leyk also received funding under grant # P2CHD066613 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development to the University of Colorado Population Center (CUPC) at the Institute of Behavioral Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In 2014, urban dwellers were estimated to account for 54 percent of the world's total population, with that percentage projected to grow to 66 percent by 2050 [ 1 ]. Yet the meaning of urban in such often-cited figures is decidedly unclear: there exists substantial variation across countries in the urban definitions adopted by their statistical authorities, and countries commonly change definitions over time [ 2 ]. Even in the United States, where urban definitions have been well documented and accompanied by census data available in fine spatial detail, the evolution of concepts and measures over the past few decades has made it difficult to craft a consistent analytic account of urbanization [ 3 , 4 ]. In other countries lacking comparable documentation and data, and certainly when comparisons are made across countries, the barriers to understanding urban change can be formidable (see, for example, continental [ 5 ] and global [ 6 ] efforts to harmonize urban delineations).

In the demographic research community, urban population has been mainly defined in terms of population density, contiguity and size, with urban measures often enriched by consideration of socioeconomic indicators of connectivity such as commuting zones [ 7 , 8 , 9 , 10 , 11 ]. Substantial differences exist across countries in the criteria applied to define populations and land areas as urban [ 1 ]. According to the UN [ 1 ], 20 percent of countries use a purely population-count based definition, while about 28 percent of all countries have adopted a single administrative criterion. Others employ functional definitions associated either with the presence of urban or the absence of rural agricultural characteristics; and many draw upon a combination of criteria (e.g., China, USA, India, Philippines, Sweden)–which may include housing density, land use types and other factors in addition to population size, density, and administrative or economic status. The density or count threshold used to identify urban populations in any given country often appears to reflect the country's overall population size and average density. For example, any community of more than 2,500 people is considered urban in Mexico, whereas in Nigeria, a settlement defined as urban must exceed a population of 20,000. Density-based definitions are often supplemented with contiguity indicators, allowing for the inclusion of low-density communities when they border larger and denser settlements. In this paper, as in the US Census, we refer to ‘traditional’ population density measured as population/area, sometimes referred to as net density. Alternative measures of density such as urban or residential densities, particularly for cities, are described elsewhere [ 12 ]. Additionally, some measures of urban area give explicit consideration to governmental jurisdictions and boundaries, as in the case of metropolitan statistical areas in the US which are formed from contiguous sets of counties.

In contrast, urban land has been defined through land-use and land-cover measures in keeping with the methods and concepts of this research community [ 13 , 14 , 15 , 16 ]. Numerous studies suggest that no more than 3% of global land is urban [ 17 , 18 , 19 ], and that in the near term, urban land is expected to grow twice as fast as urban population [ 20 ]. Yet, based on their key argument that not all urban land is built-up, Liu and colleagues [ 17 ] find that only 0.65% and 0.45% of the global land is built-up area, and impervious surface, respectively.

Both analytic perspectives have emphasized the gap between the richness of their urban concepts and the limited abilities of the available data to measure these concepts [ 21 , 22 ]. Most low-income countries do not yet produce the spatially-detailed demographic data they would need to adequately differentiate urban and rural populations and monitor change. Data constraints are receding more rapidly in land classification research, which is benefitting from the increasing availability of fine-resolution, remotely-sensed data with global coverage and spanning several decades, enabling scientists to take advantage of advances in classification and analytical tools for change detection. Land can be designated as urban on the basis of different types of low-to-high-intensity development at varying population densities or even (as in the 2010 US Census definition) no population at all. There may exist good reasons for measures of urban people and urban land to differ in coverage [ 23 ], but to date these differences have not attracted systematic research attention.

This study is informed by more than a decade of innovative, interdisciplinary work that has combined remote-sensing data with socio-demographic or ecological information to detect and map urban areas and change [ 19 , 20 , 23 , 24 , 25 , 26 , 27 ]. In these studies, the remote-sensing derived data–whether of the land use/land cover type or night-time lights [ 28 ]–have provided proxies for urban spatial features or boundaries. These studies have not directly compared satellite with census data, in part because they lacked the spatially detailed census data available to us here. Using the US as a test case, we explore the potential of remotely-sensed measures of built-up land to serve as proxies for official census measures of urbanization.

Of course, no remotely-sensed measures can fully substitute for socioeconomically-informed classifications of richly detailed census data, but even in the US such remotely-sensed measures can provide helpful complementary information. More importantly, they have the potential to add significant value in countries that are data-poor or where the ability of current urban-rural definitions to adequately classify settlement patterns is in doubt. Our analysis provides a systematic means of distinguishing peri-urban areas from other types of urban development, which may be useful in future research on finer scale urban development as well as in forecasts of urban expansion. By identifying strengths and weaknesses in the GHSL-derived proxies in a well-documented context, our study contributes to ongoing efforts to develop unified statistics-based definitions of urban from globally consistent time-series [ 29 , 30 ] that could be applied widely throughout the social and natural sciences.

This study has two main goals. First, we assess the agreement between GHSL-based measures of built-up land and census-based urban classifications, aiming to establish whether the former (GHSL) can be used as a suitable proxy for the latter (census data) where the latter are absent. Second, using this unique combination of data layers, we also aim to learn about U.S.-specific trends and processes that influence urban form (e.g., density, sub- and peri-urbanization). We draw out the implications of our finding for using GHSL to fill in for census data (for example, in inter-censal periods) in other settings, including in many data-poor countries and discuss the potential for using such data and methods in spatial forecasts of urban settings. These goals are further articulated in the sections below.

Approach, materials & methods

In order to explore what satellite-based proxy measures can contribute to defining urban places in a consistent manner, with possible application to data-poor countries, we use the high-quality demographic data of the US Census Bureau to (1) evaluate the ability of one newly available remotely-sensed settlement data product, the Global Human Settlement Layer (GHSL [ 31 ]), to approximate census-based measures of urban land and people, and (2) learn about the urban classification system that results from a spatial integration of the two data sources. GHSL classifies built-up land layers using Landsat imagery covering the period from 1975 to 2014. Otherwise similar global data products suffer from limitations either in terms of coarse spatial resolution (e.g., Global Rural Urban Mapping Project; GRUMP [ 32 ]), or limited temporal coverage (e.g., GlobCover [ 33 ]), Global Urban Footprint [ 34 ], GHSL Sentinel [ 35 ]). The GHSL dataset, therefore, has the potential not only for understanding and modelling urban patterns and change in ways that other data sets cannot, but may also offer new opportunities for refining population projections and research in field such as disaster management and risk assessment [ 36 ]. In the remainder of this section, we describe these underlying data in greater detail, as well as the conceptual framework and data integration methods used to generate our results.

Table 1 provides an overview of the data sources used in this study: census data at the block level for three different census years, 1990, 2000 and 2010 and the aggregate version of GHSL for similar points in time (1990, 2000 and 2014). The table summarizes our integrative effort to identify both “Urban People” (in keeping with demographic perspectives) and “Urban Land” (land-science perspectives): The GHSL data indicate the presence or absence of built structures at fine spatial granularity and are aggregated to express the density of structures (i.e., built-up area proportions) at moderate spatial resolution; the census classification of urban areas is based on population criteria at the census block level that varies in spatial resolution. Whereas the GHSL measures of built-up land are relatively consistent over time, the census-based urban definitions changed considerably over the period we study, underscoring one of the fundamental problems in deriving reliable estimates of urban change. Each data set is described further below. All data used herein are publicly available.

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https://doi.org/10.1371/journal.pone.0208487.t001

What is the census meaning of urban?

At the core of this analysis is the finest-grained spatial unit of analysis for the United States census—the census block, for which there is complete geographic coverage over the last three decennial censuses (1990, 2000, and 2010). Census blocks are delineated by both man-made and physical characteristics of the landscape, such as roads and rivers; in densely populated urban areas they typically comprise actual city blocks. Unlike larger census units such as tracts, blocks can vary widely in total population, which ranges from zero in many instances to several hundred or even thousands of persons in the case of densely populated city blocks. In response to changes in development and population density, the total number of blocks has risen substantially over the past three censuses, increasing from just over 7 million blocks in 1990 to over 11 million blocks in the 2010 census.

At each census, these blocks and the population contained within them are defined as urban or rural according to criteria that have changed over time (see Table 1 ). The dominant methodological trend over the three censuses has been the gradual elimination of municipal and place boundaries from the Census Bureau’s statistical definitions [ 3 ]. At the time of the 1990 census, urban/rural status was based on both total population and density criteria. Cities (which refer to census incorporated or designated places ) of greater than 50,000 persons (urbanized areas, or UAs), and all blocks contained in them, were classified as urban. Additionally, any census blocks adjacent to such qualifying territory with a population density over 1,000/mi 2 were included in the larger urbanized area. Outside urbanized areas, blocks were defined as urban if they were part of census designated places with a population greater than 2,500. For the 2000 census, the use of census incorporated/designated places (cities) as a starting point for constructing UAs was dropped and census blocks became the primary building blocks. By definition, to qualify as a UA a contiguous set of blocks demonstrating population density > 1,000/mi 2 needed a total population of > 50,000. The count-oriented definition for smaller (<50,000) urban places was also dropped in favor of a new measure known as urban clusters (UCs) which were defined using the same population density criteria as UAs (>1,000/mi 2 ). Urban clusters were defined as a core set of contiguous census blocks with a density greater than 1,000/mi 2 and a total population of 2,500–49,999. Any blocks within UAs and UCs were thus defined as urban, as were any census blocks adjacent (within 2.5 miles) to UAs and UCs provided that their population density exceeded 500/mi 2 . For 2010, the 2000 urban classification scheme was further amended to include some categories of land in industrial and commercial use: non-residential blocks mainly covered by impervious surfaces (pavement, parking lots, and airports) in close proximity (within 0.25 miles) to populated urban blocks within UAs and UCs. In an innovative combination of the demographic and land cover research perspectives, the Census Bureau drew its impervious surface measures from Landsat imagery prepared by the National Land Cover Database [ 3 ]. Additional intricacies of the census data definitions can be found on-line .

What is the meaning of GHSL-derived built-up land?

The Global Human Settlement Layer (GHSL), produced by the Joint Research Center (JRC) of the European Commission, represents a new generation of global built-up land data products, encompassing 40 years of historic change (1975, 1990, 2000, and 2014) at fine spatial resolution (approximately 38 meters, aggregated to 304 meters). More than 40,000 Landsat scenes have been processed in a consistent manner across countries and over time, drawing upon state-of-the-art built-up land extraction methods using advanced machine learning algorithms [ 35 , 37 ]. In their original resolution, the data are binary, indicating either the presence or absence of a built structure in each 38m grid cell [ 31 , 38 ]. The 304m data are constructed from the 38m cells; they record the percentage of the 304m cell that is built-up. (A revised release of these data has been issued at a resolution of 250m, but as these have been resampled from 304m version, we use the 304m data here to avoid any bias incurred during the resampling procedure.) A recent validation study has generally confirmed the accuracy of the GHSL algorithms except perhaps in very sparsely-settled rural regions; for details, see Leyk et al. [ 39 ].

Method: Conceptual framework and data integration

The analytical challenge we face here is how to derive such GHSL-based proxies and critically assess their relationship to census-based depictions of urban land and urban population. Focusing on the US census years of 1990, 2000, and 2010, the aim of our study is to assess how alternative GHSL-based measures of built-up land relate to and agree with census-based classifications of urban areas, concentrating on the spatially most detailed census-block level.

The conceptual framework guiding this research is shown in the Venn diagram below ( Fig 1 ), combining census measures of urban population with those of GHSL indicating built-up land exceeding a given density threshold. This spatial overlay of a binary land-cover measure (GHSL) atop a binary census-block classification identifies four classes of land: officially urban and built-up, which we term a class of “urban agreement” ( UAg ); officially urban but not sufficiently built-up (i.e., not meeting the given GHSL threshold), designated as “urban people only” ( UPO ); sufficiently built-up but not officially urban, entitled “built-up land only” ( BULO ); and lastly, residual land that is neither officially urban nor sufficiently built-up, which we describe somewhat informally as “rural extents” ( RE ). This residual class is comprised of the portions of official rural-designated census blocks that also fall below the GHSL built-up threshold. Maps showing these different layers are presented in S1 Fig , and the steps executed to produce them are illustrated in S2 Fig . The combination of the two types of spatial data, and the areas of agreement and disagreement between them, generate an instructively detailed and differentiated picture of urban environments. The three mutually-exclusive classes UAg , UPO , and BULO together make up what we term an “urban inclusive” ( UI ) layer. This summary class extends the official urban totals to include any people and land in the BULO zone, that is, built-up area that is not officially urban but which exceeds the given built-up threshold.

https://doi.org/10.1371/journal.pone.0208487.g001

Census blocks , each classified as urban or rural by the Census Bureau, provide complete coverage of all national territorial units. Restricting our analysis to the continental, lower 48 states, we overlay the GHSL built-up raster data (304m grid cells) on these census blocks (as detailed in the S1 Fig and S1 Text ). The Census Bureau designation of urban is binary—either wholly urban or wholly rural—at the census-block level. In a similar fashion, we adopt a binary coding for the density of structures in each raster cell that intersects a census block, which is coded in relation to a specified built-up percentage threshold. We consider three such thresholds in this analysis– 25, 40, and 50 percent built-up. The most commonly-cited threshold for defining urban land from GHSL is 50 percent [ 30 ], but we also use more inclusive thresholds to assess the sensitivity of our results to threshold levels. The EC-standard measure is to date the only formalized threshold in use and therefore we adopt this value as the starting point in our analysis.

Having classified land as conceptualized in Fig 1 and shown in S1 Fig , we proceed to calculate the population living in each of the four classes. One difficulty is that where units of land are concerned, the high resolution of the GHSL often identifies variation in the density of structures within a census block, especially outside city cores where there can be multiple GHSL cells per block. In order to link classes of land to classes of people, we have assumed that the population of the block is uniformly distributed across its geographic area, even in cases where GHSL shows that only a portion of that block is built-up. Specifically, we overlay the four-class spatial distribution described above with the block-based census population data and extract population totals for each layer within each block using a proportional allocation algorithm (by land area, hence applying areal weighting techniques [ 40 , 41 ]). S2 Fig outlines the data-processing algorithm in more detail.

Comparisons of officially-designated urban land and population with the built-up proxy measures are undertaken for 1990, 2000, and 2010. Our analysis is applied to all census blocks in the continental US as a first analytical step to shed light on urban structure and composition at the finest level of spatial granularity, and over time. The results will guide subsequent experiments in future research (currently underway, and presented elsewhere) in which we undertake an analysis of blocks within US Metropolitan Statistical Areas, often used as proxies for cities, and apply these methods to other countries to characterize spatial and temporal variability in the evolution of urban systems.

We organize our results in four parts. First, we describe our estimates of population and land area for each of the classes we’ve constructed–urban agreement ( UAg ), built-up land only ( BULO ), urban people only ( UPO ), and the residual rural extents ( RE )–for each of the three census years, using different GHSL built-up percentage thresholds. Second, we elaborate on how built-up area and officially urban estimates compare in order to evaluate how well one type of data can serve as a proxy for the other. Third, derived from this we describe the built-up levels of our different urban classes in order to better understand urban form as well as urban measurement. Fourth, we focus on the transitions in classes over the three censuses to explore the potential for forecasting urban change.

What do urban classes tell us about measures of urbanization?

In 1990, the population of the continental United States was 247 million; by 2010 the total had risen to 307 million. Official statistics put the urban population of 2010 at 80.7 percent of the total population, up from 75.1 percent in 1990 [ 42 ]. For land area (using the Census definition), the urban percentages run from 2.9 percent (in 1990) to 3.6 percent (in 2010) of all continental land (see Table 2 ). To what extent does adoption of an urban-inclusive ( UI ) perspective alter such percentages? Any additions to the official urban totals would come from the inclusion of what we have termed built-up land only ( BULO ), but as we find, neither the population nor the land area of this class is very substantial. The population inhabiting such land amounted to only 1.2 percent of the continental US population in 1990 (1.5 percent of the urban-inclusive population), declining further to 0.5 percent in 2010 (0.6 percent of the urban-inclusive population). This suggests that the officially designated urban areas include most of the population residing on built-up land portions. (Results for GHSL thresholds of 25 percent and 40 percent built-up are presented in S1 and S2 Tables.) The urban inclusive ( UI ) land area by our estimate has increased from just over 3 percent in 1990 to 3.7 percent in 2010, neither figure being significantly higher than what one would obtain from official urban-designated totals.

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Just over two-thirds of the urban-inclusive population is found in the built-up areas (using the 50 percent built-up criterion) that are officially designated as urban (i.e., the urban agreement class, UAg ). These are areas that might conventionally be regarded as “core urban” [ 43 ], and is where population densities are highest, at 1,561 persons per square kilometer or more in all three census years. However, the UAg class accounts for a much smaller share of urban-inclusive land than it does for people, accounting for only about one-third of the UI land area in 1990 and 38 percent in 2010. The areas classified as urban people only ( UPO ) contain somewhat less than one-third of the urban inclusive population, exhibiting population densities from nearly 370–456 persons per km 2 over the period, well below the densities of UAg areas. Such officially urban but lower density land accounts for well over half of all urban land area. UPO areas are typically found on the outskirts of urban areas, as would be consistent with suburban and peri-urban settlement (see S3 Fig ). From 1990 to 2010, this segment of the urban population grew the most in relative terms (by 7 percent in relation to the 1990 benchmark). While core urban and peri-urban are terms that are widely used to characterize aspects of urban form, our classes help to map, contextualize and quantify these aspects in a systematic way. As also shown in Table 2 , population densities in non-urban built-up land ( BULO ) are low—not as low as rural densities, to be sure—but well below the population densities of the other two classes of urban land.

Importantly, these characterizations are dependent on which built-up threshold one applies. We have taken 50 percent built-up to be the default threshold, in part because it is the one being used elsewhere [ 30 ] as an urban proxy. S1 and S2 Tables present estimates for two more inclusive GHSL thresholds. If the 25 percent built-up criterion were to be adopted, there would be little change in the total urban-inclusive population, but a significant redistribution of this population among the urban classes would take place. A greater share of the urban-inclusive population would belong to the UAg category (built-up and officially urban)—81 percent in 2010 as compared to 68 percent in Table 2 —and correspondingly the population found in the officially urban but not built-up ( UPO ) class would be 13 percentage points smaller (slightly more than half its size as estimated in Table 2 ). The population densities of all urban classes would decline with the lower threshold, as would be expected given the correlation between the density of structures and the density of population. The choice of threshold also affects the estimates of land area in each class, but even at the 25 percent threshold, less than 4 percent of total land area would fall in the urban-inclusive category.

How do built-up and officially urban compare?

Table 3 summarizes the performance of our density-of-structures proxy for officially-designated urban population and land area. Here we compare the more restrictive GHSL threshold of 50 percent built-up to a more forgiving 25 percent threshold. In areas that are built-up by these criteria, the official urban percentage of the population exceeds 98 percent (at the more restrictive threshold) and 85 percent (at the more inclusive one). Similarly, between 87–91 percent of built-up land (50 percent threshold) is officially classified as urban (with about 10 percent less at the more inclusive threshold). In summary, almost all residents of built-up land are officially urban, and a very high percentage of all built-up land is also officially urban. For both population and land, being built-up very nearly implies being officially urban.

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But does officially urban imply being built-up? Here, the performance of the GHSL proxy is less impressive but nevertheless instructive. Of all areas officially designated as urban, between 69 to 84 percent of the population (depending on the GHSL threshold) lives on built-up land. Specifically, at the 50 percent threshold, GHSL misses about 30 percent of the official urban population; with the more inclusive threshold this number reduces to about 16 percent of the official urban population, Similarly, of the total land area of official urban census blocks, only 35 to 39 percent is built-up at the 50 percent threshold, by between 50 to 57 percent is built-up at the more inclusive threshold. Being officially urban clearly does not imply being built-up. The two measures are simply not equivalent.

To put this finding in its proper context, it should be recalled that the official urban designations are framed with two population density thresholds, at least one distance threshold (lower densities being allowed for areas contiguous to higher-density areas), and further include consideration of total population size. They can be regarded as a rich, multi-parameter statistical specification of urban-ness. In contrast, the built-up urban proxy that we have constructed here is simple: it has only a single threshold parameter. Therefore, it is not entirely surprising that this proxy fails to capture all the features of complex urban landscapes. Having established the potential of the built-up proxy measures, we see opportunities to improve their performance by devising more flexible parametric specifications, which might mimic the Census Bureau's treatment of population, contiguity, and density, and also to revise estimates of potentially non-urban land and people within officially urban block units. We revisit this point in Discussion to draw out the implications for future research. To further assist with that objective, we next examine variation in built-up levels across the different urban classes.

How built-up are urban classes?

As shown in Table 4 we examine the mean GHSL built-up percentages by class and threshold to better understand the built-up levels of different types of urban environments. By definition, the means for the urban agreement ( UAg ) and built-up land only ( BULO ) classes must exceed the chosen threshold; what is of interest here is the degree to which they do so. For the built-up thresholds of 25, 40, and 50 percent, the UAg means of built-up percentages are respectively 66, 75, and 80 percent. Likewise, the urban-people-only ( UPO ) class must by definition exhibit built-up percentages that fall short of the GHSL threshold: the means are in fact far below those thresholds, at 5.5–7 percent, 10–12 percent, and 13–16 percent, respectively. This suggests that a much lower built-up threshold could be applied, either by itself or in combination with rules of proximity (to UAg areas), to detect suburban and peri-urban areas within the US. As for the rural residual ( RE ) class, its average GHSL values are below 1 percent irrespective of the threshold, although as mentioned earlier there is reason to believe that these rural results may be based on underestimated built-up percentages in rural settings.

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As Table 4 shows, the average GHSL value for the urban inclusive ( UI ) class exhibits a modest increase over time for each GHSL threshold. Yet, when examined by classes within urban-inclusive, it can be seen that UPO is the only class in which the mean built-up percentages are increasing. There are several possible explanations for this: 1) areas that transition into the UAg and BULO classes are likely to be places that have a lower percent of built-up land (and closer to the thresholds used to construct these classes) than the areas that were previously classified as UAg and BULO , thus causing the average built-up values to decrease. 2) In UPO areas, while some increase in built-up-ness is expected, their average values remain below the respective thresholds consistent with suburban and peri-urban development. 3) Block boundaries change substantially over time as a function of the census-based definition of urban. These changes affect which blocks, or parts of blocks, are included in any given class, contributing to the decreases or increases of average GHSL class values.

Do patterns of change describe urban transitions?

We now examine the changes in the urban status taking place over the course of one to two decades. The levels of built-up density ( Table 4 ) and the sheer durability of structures, make it unlikely that built-up areas will lose enough structures to fall below a given threshold in the space of a few decades. Transitions from below to above the density thresholds are more likely, at least in cases of rapid development of sparsely settled land. The main changes of interest, therefore, are in those areas that involve the sparsely built-up but urban places in the UPO category and the built-up but non-urban places ( BULO ) whose status might be revised by the Census Bureau.

Table 5 documents these transitions; it can be read in conjunction with the illustrations in Fig 2 , which shows, as an example, the dynamics of change for much of the New York City metro area (officially, the Metropolitan Statistical Area, MSA). Examining the transitions of UI land, Table 5 shows that in both 1990–2000 and 2000–2010, areas in any given class at the start of a decade are likely to remain in that class at its end. This is especially true for areas of UAg : 96 percent of the land area in this status in 1990 is again so classified in 2000; over the 2000–2010 period, 99 percent remained UAg . The relatively little area exiting this class almost always enters the class of built-up but not officially urban ( BULO ) a decade later; this occurs mostly on the edges of an urban area, as in Long Island (see Panel A of Fig 2 ). Of land area that is classified as BULO in 1990, 39 percent transitioned to the UAg class, thus retaining its built-up status but being newly designated as officially urban by the census. As seen in Fig 2 , these are areas that can be described as either in-fill within larger urban areas, or localities on the edge of the existing area of UAg . From 2000–2010, more than half of the BULO area underwent this transformation. As with UAg areas, no land area classified as BULO was reclassified as RE in subsequent decades. Overall, therefore, the dynamics exhibited by the BULO areas suggest that built-up but not-yet-officially-urban areas may have significant potential to gain an official urban designation over the course of a decade, thus serving as a leading indicator of urbanizing population.

Change in classifications, New York city MSA, 2000–2010: changes from 2000 all urban classes in panel A and from 2000 rural extents in panel B. NB: As indicated in Table 5 , no UAg or BULO area transitions to UPO (Panel A), but some RE area transitions to UPO (Panel B).

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Among the less densely built-up urban localities ( UPO ), a significant fraction of the land area loses its official urban status to become low-density rural ( RE ) over a decade (33 percent make this transition from 1990–2000 and 15 percent over 2000–2010), with transitions to other classes being much less common. Inspecting these cases, we find that the areas which were reclassified as rural were initially somewhat more distant (1.4 and 2 km, in 2000 and 2010, respectively) from the nearest area of UAg than were those that remained UPO (which were about 0.5 km away). Additionally, the bulk of the area reclassified as RE was also subject to census block boundary changes between census years, suggesting that blocks are being reorganized to more cleanly separate their urban and rural components. Despite the sizable reclassification to RE , the majority of UPO land remains UPO land in the subsequent decade.

Table 5 and Panel B of Fig 2 also allow us to compare these transitions with those of land initially classified as rural. While only small fractions of RE land transition to any urban class over the course of a decade, the total amount of land in these classes is decidedly non-trivial. Most RE land that is reclassified transitions to the UPO class–some 60,000 km 2 and 50,000 km 2 respectively, in 2000 and 2010; when combined with transitions of UPO land to RE, this results in only a modest net gain of UPO land between 1990 and 2000 (≈12,000 km 2 ), and a larger gain between 2000 and 2010 (≈29,000 km 2 ). A decomposition of these transitions suggests that two distinct processes are at work: It appears that most of the UPO to RE transitions result from boundary changes whereby larger urban blocks with geographically condensed urban populations are split into urban and rural components. Conversely, RE to UPO transitions are associated with population growth. Significantly less RE land makes a transition to areas of UAg or BULO . However, the land that does transition to BULO –almost 3,000 km 2 in each decade–comprises between 20 to 30 percent of land in the BULO class, 13,740 km 2 and 10,865 km 2 , respectively, in 2000 and 2010. Further research will be required to delineate the full transition history of each land parcel (e.g., from RE to UPO or BULO to UAg ), but it is clear that the transition of rural areas into urban ones is an important feature of the full continuum of the urbanization process, and is likely to be of considerable importance in non-US settings. For example, in China Zhu [ 44 ] has termed this process in situ urbanization. Finally, as with the transformations within urban classes, there is a pattern evident in the transformation of RE areas to urban ones. Panel B of Fig 2 shows that they tend to be on the outlying areas of existing urban areas, although dispersed transformations to both BULO and UPO areas are also notable.

In this analysis, the incorporation of remote sensing-derived, built-up land layer–such as those derived from GHSL–has enabled us to pursue three objectives. First, by combining census-defined urban areas with GHSL and in this way integrating demographic with land-cover research perspectives, we aimed to learn in a more holistic way about urban structure. The Census Bureau has taken steps in this direction as well, by including the impervious surface land cover class in its urban definitions for the 2010 census. Our results suggest that this blended approach is well worth pursuing. Second, the study aimed to identify the potential of using GHSL-built-up measures as a spatially and temporally consistent proxy for urban classes, which could be applied to urban land or urban people in regions and countries in which no (or quite limited) census-related urban classes exist. Third, we were interested in understanding the potential to use GHSL for creating urban indicators and in spatial forecasting of urban land. Our results will be discussed below in the light of these three objectives.

Combined data layers reveal patterns of urban structure and urbanization

This study has created a new way of combining satellite-derived settlement layers with census data to model different urban classes, including areas where urban people live in urban-designated land (areas of urban agreement, UAg ), built-up land only ( BULO ; in which the population is considered to be rural by the census) and where officially-designated urban people live on non-urban land–that is, on land that is not very built-up ( UPO ). These distinct, thematically refined classes provide a unique picture of the distribution of urban people and urban land and give insight into the variety of urban patterns across the whole nation–allowing us to characterize urban expansion and in-filling as well as urban densification over three censuses.

An overwhelming share of the urban population of the US (nearly 70 percent) live in areas of urban agreement, consistently throughout the 20 year period we study. Of all people living in built-up areas, very small percentages–only 1.5 percent in 1990, declining to 0.3 percent in 2010 –are not classified as urban by the census. Our analysis reveals that different classes have very different built-up levels. Areas of UAg are overwhelmingly built-up, being between 5–10 times more built-up than areas of UPO (depending on the decade and GHSL threshold considered). All urban classes have far greater built-up percentages than rural extents, reflecting in some part an imbalanced classification accuracy of GHSL [ 39 ], as well as the fact that officially rural areas include uninhabited areas (such as national parks).

However, the full extent of urban living is not captured by built-up density alone: Using a 50 percent threshold, we find that around 30 percent of the officially urban population does not live in built-up areas. Several factors combine to produce this gap between the official urban population and the GHSL-based proxy for it. First, as we have shown, the degree to which the official urban population is captured by GHSL is sensitive to the choice of the built-up threshold. Second, the gap may be partly explained by the use of proximity and connectivity measures to urban cores in Census Bureau’s urban definitions, which are meant to include people who live an “urban lifestyle”. A third contributing factor is the expansive peri-urban and ex-urban development that is found commonly in the US, but which is much less prevalent in other countries [ 30 ]. Our analysis reveals that these uncaptured areas have built-up levels that are much lower than 50 percent. It is worth noting that in related studies, it has been found that GHSL (and Landscan SL, a similar product) outperform other measure of built-up land in detecting more peripheral urban development, while there is general agreement across products in more heavily built-up core urban areas [ 45 ]. This suggests that, by any measure, urban people tend to live well beyond the bounds of heavily built-up land (see S2 Text and S1 and S2 Tables for analysis at alternative GHSL thresholds). Finally, the gap may also have something to do with our assumption that population is uniformly distributed within census blocks. We discuss alternatives to the uniformity assumption below.

Despite this shortcoming, the combination of block-level census classifications with GHSL enables us to describe urban patterns and their change over time in ways that cannot be done with more conventional, binary geographies, such as those of the temporally-changing MSA or CBSA classifications of metropolitan areas for the US. The fine-grained GHSL data offer additional information in the form of built-up proportion (which has an intuitive meaning related to the intensity of development), and is thus relevant for the identification and differentiation of urban land within any administratively-identified urban area. Other studies [ 4 , 11 ] have also grappled with this issue, but so far, as we are aware, ours is the first attempt to combine census urban classifications with remote-sensing derived data for the purpose of evaluating the degree of correspondence between such disparate data sources. GHSL data in and of themselves, and our combined classification scheme, both hold promise for use as urban extent proxies over time in settings where administrative data that include urban definitions are missing or badly out-of-date, or as in the case of MSA-type classifications, are too complex and time-variant to easily interpret.

Can GHSL be used to derive consistent urban proxies in data-poor regions?

As we have shown, GHSL has very good potential in the measurement of urban processes not only across space but also across time. Although US census definitions have changed over time, it has been demonstrated that GHSL reflects urban areas and urban populations, consistently, in the sense that the degree of agreement between census-based classifications and GHSL-derived footprints remains essentially constant. These are encouraging results for the use of GHSL in measuring urban extents and change in non-U.S settings, enabling the analyst to maintain consistency in definitional criteria across countries and over time. However, it must also be cautioned that GHSL does not reflect the fullness of more complex urban definitions, as we see in the US. Nevertheless, it seems to capture the majority of population (our urban agreement class) that would be considered urban in other settings as well. Lower built-up thresholds capture a greater share of the urban population in the US, pointing to even greater potential in using GHSL at varying thresholds of 50 and less percent built-up.

Can GHSL-based urban indicators be used in forecasting?

We found that most land area classified as urban agreement ( UAg ) in one decade remains in that class in the subsequent decades. The other two classes ( BULO and UPO ) both undergo some transition to areas of urban agreement, which is especially probable for areas of BULO . Understanding these transitions in full is part of a larger research agenda, but it seems that BULO and UPO areas might be viewed as leading indicators of change, and might therefore aid planners and analysts in preparing for urban transitions. In the present analysis, we find that although the population of BULO areas is quite small, over time these areas are increasingly likely (40 percent from 1990–2000 and 50 percent from 2000–2010) to become classified by the census as being urban. Only a small portion of the UPO land area transitions to UAg , roughly 5 percent in each of the two decades. Importantly, on the rural side of the urban-rural continuum, there are sizable transitions in both directions between UPO land and rural extents ( RE ), such that on net, the fraction of total population in UPO areas grows modestly over time. We cannot comment here on land that might have transitioned multiple times, as our analysis did not track the evolution of specific land parcels across all three census years. However the spatial distribution of transitions illustrated in Fig 2 indicate, for example, that transitions from RE to UPO often occur in close proximity to transitions from either UPO or BULO to UAg , suggesting a possible stepwise transition from RE up the urban hierarchy in some cases. While often thought of as a very American form of urbanization, this process may be consistent with that observed in a non-spatial analysis of European cities whereby total urban land expands while population growth, and in some instances even population totals, decline [ 46 , 47 ]. These observations suggest much potential for using GHSL to be predictive of densification as well as changes in urban structure over the relatively near term.

Limitations of the study

There are several small but notable limitations of this study. First, we assume a uniform distribution of population across the territory of a census block, even when GHSL identifies only part of that block as built-up. Uniformity is a simplifying but flawed assumption: the true proportion of population within the built-up portion of such a block is likely to be higher. In adopting a uniform assumption, we are likely to have underestimated the population found in areas of UAg and BULO and over-estimated that of UPO . However, in this study we prioritized scalability to the whole U.S. over over the use of more complex dasymetric models to redistribute population to different parts within a block, which tends to be difficult to validate [ 48 , 49 ].

A second and closely related challenge has to do with boundary changes: census block geometries change across census years. The methodology applied in this study treated boundary changes at the block level as processes that may be part and parcel of urbanization (such as, administrative reclassification) and thus assumed comparability of block parts over time. The impact of this assumption in comparison to other methodological approaches is not yet known. (Future research will examine the role of block-level changes on urbanization.)

Third, the accuracy of GHSL in rural areas is known to be lower than in urban settings [ 39 ]. Because the BULO and RE classes are comprised only of areas that are officially rural, our estimates of BULO land area and population, and our estimates of GHSL values for both BULO and RE classes, may well be too low. As a consequence, the detection of urban area that does not meet the built-up proportion criterion is open to question.

Fourth, satellite data are inherently limited in that they cannot reveal anything about the key socioeconomic features of population composition—the domains of age, gender, education, race/ethnicity, and poverty—whereas census data do bring these critical dimensions to light, allowing them to be mapped together with population density and size. The vertical dimension of settlement is also clearly important, but to date no global data product—satellite-derived or social-science data—measures building volume (see [ 50 , 51 ] for examples of volumetric urban change for particular cities).

Future research directions

Future research will explore more complex rules for built-up thresholds, perhaps by emulating the Census Bureau criteria on proximity, connectivity and density by allowing lower density built-up areas to be defined as proxy-urban if the area is within a certain distance of higher density areas. Such efforts would benefit from a comparative perspective to test the implications of built-up density specifications against a range of urban definitions and development patterns [ 1 , 6 ], so as to further advance efforts toward a global classification schema [ 30 ]. These advanced measures combined with higher-resolution satellites such as Sentinel in future GHSL releases [ 35 ] will further improve capturing built-up areas in more suburban, peri-urban and rural locations and thus help to reduce the discrepancy between areas that are only designated as urban by the census ( UPO ) and satellite-based urban measures.

It is important to undertake comparative work. We are currently conducting analysis comparing the results shown here with those for India and Mexico. Yet, the presented general approach can be adopted widely wherever there is access to fine-grained census (or even survey) data in which urban classifications are indicated. Further, since country-specific definitions of urban population have meaning, insight from a broad range of countries combined with in-depth knowledge of local understanding of urban forms will help to determine the potential limits of using satellite proxies.

Other ancillary data–such as night-time lights [ 52 , 53 ], historic housing and property data [ 54 ] and high-resolution satellite data that can differentiate among types of settlements–will be integrated in future research to examine how they can be used to refine estimates of the socioeconomic or demographic characteristics of place.

Methodologically, we will also make use of the distinct advantage of GHSL and similar high-resolution data to spatially refine census enumeration units, thus overcoming some of the persistent limitations in demographic analysis that typically assumes areal units to be internally uniform [ 55 , 56 ] and compares units that are inconsistent between censuses. This will directly benefit the application of areal interpolation methods [ 57 , 58 ] to create spatially refined and temporally consistent target units within which estimates can be compared more reliably.

Finally, future research will more formally investigate multi-temporal processes within the urban system. While the best methods to do this remain to be determined [ 16 , 59 ], in light of the fact that there are no existing spatial forecasts of city-growth or urbanization in this urban era, this is a promising new direction for scientific inquiry.

Conclusions

This study has revealed important new aspects of the structure and composition of urban settings, showing how structure and composition are reflected in census data alone and satellite data alone, but are more fully revealed in their combination. Breaking from convention, we replace dichotomous indicators of urban-rural with graduated classes of land and population. We have found strong agreement between the US census urban classifications and the GHSL measures of built-up area, with the associations being constant over time and across GHSL thresholds. Areas of urban agreement–which meet both the census urban classification and GHSL threshold–are overwhelmingly built-up in comparison with areas that meet the census urban definition only. These are encouraging findings. Nevertheless, we would caution against the uncritical use of a single built-up GHSL threshold, which has the potential to misclassify the urban population. For the US case, we have shown that the adoption of a 50 percent built-up threshold (and a simple proportional allocation rule), fails to identify some 30 percent of the official urban population. Lower built-up thresholds capture more of the official urban population, and therefore merit careful consideration, as do richer specifications of the built-up surface that take contiguity and proximity into account. Combined census and satellite data can be further analyzed for even more detailed and nuanced characterizations of urban form and systematic evaluation of urban development patterns.

GHSL also holds promise for predictions of future urbanization and urban spatial patterns. This is a welcomed and long overdue advance in data and methods in both research and policy circles that have been dominated by use of simple aspatial trend interpolations/extrapolations of population estimates [ 1 ]. In the US, non-urban but substantially built-up places show a non-trivial likelihood of being classified as census-urban a decade later, suggesting that areas built-up but still officially classified as rural may represent a leading edge of urban change. Such in situ urban transitions may be taking place in other countries as well. The combination of built-up density data with ancillary data on roads and similar measures of connectivity [ 60 ] would seem to have excellent potential for forecasting urban change. Our research points to the need for focused and critical evaluation, especially of fringe areas and areas in transition.

Supporting information

S1 text. additional methodological detail..

https://doi.org/10.1371/journal.pone.0208487.s001

S2 Text. Sensitivity tests using alternative GHSL thresholds: Estimates of population and land area.

https://doi.org/10.1371/journal.pone.0208487.s002

Constructing urban layers for the New York City MSA, including (a) urban blocks, (b) all urban land (GHSL 50% threshold), (c) urban inclusive area (UI), (d) urban agreement (UAg), (e) urban people only (UPO), (f) built-up land only (BULO), and (g) the entire urban hierarchy (including RE, UAg, UPO and BULO). Green background indicates rural extents (RE) in all maps.

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S2 Fig. Workflow for allocating land and population across urban classification scheme.

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S3 Fig. Urban classifications, 1990 (upper) and 2010 (lower), with year-2000 MSA boundary, for selected pairs of large and small MSAs.

https://doi.org/10.1371/journal.pone.0208487.s005

S1 Table. Population and land area by urban classification, 25% GHSL threshold.

https://doi.org/10.1371/journal.pone.0208487.s006

S2 Table. Population and land area by urban classification, 40% GHSL threshold.

https://doi.org/10.1371/journal.pone.0208487.s007

Acknowledgments

We thank Martino Pesaresi, director of the GHSL program at the Joint Research Center, European Commission, Ispra, Italy for advice and Brian O’Neill at NCAR for comments on an early draft. We thank Elizabeth Major, Denys Dukhovnov, and Natalie Lin for research assistance.

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The politics of inequality: Why are governance systems not more responsive to the unequal distribution of income and wealth?

The politics of inequality

June 4, 2024

Starting from a theoretical framework that conceptualizes policy outcomes as the result of complex interactions between actors, institutions and discourses, the paper synthesizes global research on the politics of (re)distribution within democratic governance systems.

Four questions are used to structure the surveyed material:  What factors shape preference formation with respect to distribution across different actors? What factors enable or constrain collective action aimed at generating demand for inequality reduction? How do actors with an interest in preserving inequality leverage influence differentials to capture the policy process?  How do institutions and discourses constrain the policy arena to limit the range of possible policy outcomes?

As a synthesis of global research of politics of distribution, the paper is expected to serve as a conceptual springboard for context-specific analysis aimed at generating relevant governance reform agendas. In addition, the paper could be used in a more prospective way in the context of political transitions. It could, for instance, be used as the starting point of risk informed analysis aimed at identifying factors that may prevent democratic openings from delivering hoped-for social and economic justice results.

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Knowledge mapping analysis of pro-environmental behaviors: research hotspots, trends and frontiers

• Published: 08 June 2024

Cite this article

• Lingyun Mi 1   na1 ,
• Wenfeng Zhang 1   na1 ,
• Haimiao Yu 1 ,
• Yuguo Zhang 2 ,
• Ting Xu 1 &
• Lijie Qiao 2  

As a critical approach to addressing environmental issues, including global climate change, pro-environmental behavior (PEB) has garnered extensive attention in the environment research field. However, a systematic understanding of the evolution process and emerging trends of PEB research has not been provided. Employing CiteSpace as a bibliometric analysis tool, this study systematically assesses the development status from the macro, meso, and micro levels, as well as summarizes the hotspots, trends, and frontiers based on 4032 PEB articles from the Web of Science database. The contributions of this study are as follows: Firstly, it presents a clear depiction of the publication trends, knowledge flow characteristics, disciplinary evolution, journal citation patterns, research collaboration status, and thematic focus of PEB research. Secondly, it identifies the evolving hot topics in PEB research over time, progressing from waste management, energy conservation, and green tourism to employee green behavior, sustainable consumption, and spillover of environmental behavior. Thirdly, it uncovers the research trends in PEB, including the expansion of the research object from focusing on single-domain PEB to cross-domain PEB, and the expansion of the influencing factors from focusing on psychological level drive to multiple contextual reinforcements. Lastly, the paper delineates future research agendas from the perspectives of research scope, methodologies, and content. This paper provides an opportunity to comprehensively grasp the developmental trajectory and future directions of PEB research, offering guidance to researchers and policymakers on how to effectively promote sustainable societal development.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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This work was supported by the Fundamental Research Funds for the Central Universities (No. 2024ZDPYSK02).

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Perspectives on urban transformation research: transformations in , of , and by cities

• Katharina Hölscher   ORCID: orcid.org/0000-0002-4504-3368 1 &
• Niki Frantzeskaki 2  

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The narrative of ‘urban transformations’ epitomises the hope that cities provide rich opportunities for contributing to local and global sustainability and resilience. Urban transformation research is developing a rich yet consistent research agenda, offering opportunities for integrating multiple perspectives and disciplines concerned with radical change towards desirable urban systems. We outline three perspectives on urban transformations in , of and by cities as a structuring approach for integrating knowledge about urban transformations. We illustrate how each perspective helps detangle different questions about urban transformations while also raising awareness about their limitations. Each perspective brings distinct insights about urban transformations to ultimately support research and practice on transformations for sustainability and resilience. Future research should endeavour to bridge across the three perspectives to address their respective limitations.

Science highlights

We outline three perspectives on urban transformations for explaining, structuring and integrating the emerging urban transformations research field.

Transformation in cities focuses on unravelling the diverse factors, processes and dynamics driving place-based transformations in cities. This perspective represents research that aims to examine and explain why transformations occur and are supported in some places and not others.

Transformation of cities examines the outcomes of transformative changes in urban (sub-)systems. It serves to understand and evaluate the emergence of new urban functions, new interactions and their implications for sustainability and resilience.

Transformation by cities looks at the changes taking place on global and regional levels as a result of urbanisation and urban development approaches. The perspective emphasises the agency of cities on a global scale and how transformation concepts travel between places.

Future research should aim to bridge across the perspectives to address their respective limitations, for example by bringing in place-based knowledge (‘in’) into global discussions (‘by’) to facilitate cross-city learning.

Policy and practice recommendations

Experimental, collaborative and place-based governance approaches facilitate the integration of local knowledge, the development of inspiring narratives that boost sense of place and empower local communities to boost transformations in cities.

To assess and coordinate urban transformations, transformations, policy and practice actors need to employ systemic concepts and visions that advance solutions with multiple benefits for synergies and  minimal trade-offs.

Multi-level partnerships and (transnational) networks for policy knowledge exchange between cities help mobilising the potential of cities as agents of change for sustainability at a global scale.

Introduction

The notion of ‘urban transformation’ has been gaining ground in science and policy debates. Urban transformations to sustainability and resilience are enshrined in the 2030 United Nations Sustainable Development Goals (SDGs) (UN 2016 ) and the New Urban Agenda (UN-Habitat 2016a ). A rich research field around questions of urban transformations has started to emerge, combining multiple scientific disciplines, ontologies and methods (Elmqvist et al. 2018 , 2019 ; Wolfram et al. 2017 ; Vojnovic 2014 ). Key to these debates is the aim to put cities on a central stage for accelerating change towards local and global sustainability and resilience.

Urban transformation narratives have been driven by the recognition of the need and opportunity for radical change towards sustainable and resilient cities. Cities constantly experience changes, but contemporary urban change processes are unparalleled. Cities grapple with a variety of interrelated challenges, including pollution, poverty and inequality, ageing infrastructure and climate change (Haase et al. 2018 ; UN-Habitat 2016b ; Seto et al. 2017 ). Urbanisation in its current form causes significant changes in land use, energy demand, biodiversity and lifestyles and raises questions about the contribution of cities to global environmental change (Haase et al. 2018 ; Alberti et al. 2018 ; Elmqvist et al. 2013 ; Seto et al. 2017 ). At the same time, cities concentrate the conditions and resources for realising the fundamental changes in energy, transportation, water use, land use, housing, consumption and lifestyles that are needed to ensure liveability, wellbeing and sustainability of our (urban) future (Romero-Lankao et al. 2018 ; Koch et al. 2016 ; Elmqvist et al. 2018 ). The potential and momentum in cities is visible in for example the ‘climate emergency’ declarations of local governments that call for accelerated climate action in view of international stalemate.

The notion of urban transformation guides and formulates a better understanding of urban change. On the one hand, ‘transformation’ serves as an analytical lens to describe and understand the continuous, complex and contested processes and dynamics manifesting in cities, as well as how these dynamics alter urban functions, local needs and interactions between cities and their surroundings (McCormick et al. 2013 ; Iwaniec et al. 2019 ). On the other hand, the transformation perspective provides a normative orientation that emphasises the need for radical and systemic change in order to overcome persistent social, environmental and economic problems and to purposefully move towards sustainable and resilient cities in the long-term (Hölscher et al. 2019 ; Kabisch et al. 2018 ). Accordingly, sustainability and resilience are complementary concepts to asses and orient urban transformation processes (Elmqvist et al. 2019 ; Pickett et al. 2014 ; Simon et al. 2018 ).

In this paper, we distinguish three perspectives on urban transformations to structure and guide research and practice on urban transformations. Urban transformation research is an emergent, loosely connected interdisciplinary field combining urban studies and complex system studies. Various research fields and disciplines converge in urban transformation research; the multitude of disciplines has been systematically reviewed in Wolfram et al. ( 2017 ) and Wolfram and Frantzeskaki ( 2016 ). This diversity engenders multiple entry points and provides complementary concepts, theories and insights. However, the diversity causes ambiguities in ontologies, use of concepts and fragmented knowledge about how urban transformations unfold and can be supported.

Urban transformation research would benefit from “gradual interconnection, and the articulation of a certain range of research perspectives” (Wolfram and Frantzeskaki 2016 : 2). To facilitate this, we distinguish and describe three perspectives on urban transformations that provide areas of convergence across diverse research approaches. Each perspective provides distinct starting points to generate, structure and integrate knowledge along certain questions. Ultimately, the perspectives outline an agenda for advancing theory and practice on urban transformations for sustainability and resilience: they generate implications for urban policy and practice and a way forward to bridge across the perspectives to address the respective limitations.

Perspectives on transformations in, of and by cities

We distinguish between perspectives on urban transformations in , of and by cities. The perspectives provide entry points for formulating and structuring research questions on urban transformations, integrating research approaches and knowledge, and deriving implications for practice.

The three perspectives start from similar assumptions about cities and urban transformations. They focus on urban transformations as complex processes of radical, systemic change across multiple dimensions (e.g. social, institutional, cultural, political, economic, technological, ecological) (Hölscher et al. 2018 ; Frantzeskaki et al. 2018a ; McCormick et al. 2013 ). Cities are understood as complex, adaptive and open systems (Alberti et al. 2018 ; McPhearson and Wijsman, 2017 ; Ernstson et al., 2010 ; Collier et al. 2013 ). This implies that urban transformations are not spatially limited, and driven by and driving cross-scale and cross-sectoral dynamics: cities are “local nodes within multiple overlapping social, economic, ecological, political and physical networks, continuously shaping and shaped by flows of people, matter and information across scales” (Wolfram and Frantzeskaki 2016 : 143; see also Hansen and Coenen 2015 ; Chelleri et al. 2015 ). To describe, explain and evaluate urban transformations, cities are increasingly approached as social-ecological-technical systems (SETS), including (1) socio-economic, political and institutional dimensions (social); (2) natural resource flows and physical phenomena (ecological); (3) as well as the manmade surroundings (technological) (McPhearson 2020 ; Alberti et al. 2018 ; Bai et al. 2017 ). Actors have a central position within urban systems, influencing how cities are organised and resources are produced and consumed. Given the open character of urban systems, actors are diverse and include household members, local governments, and entrepreneurs also regional and national governments, international bodies and multinational companies, amongst others (Glaas et al. 2019 ; Webb et al. 2018 ).

Urban transformations can be desirable or undesirable (Elmqvist et al. 2019 ; Hölscher 2019 ). A shared aim across urban transformation research perspectives and approaches is to generate actionable knowledge to intervene in urban transformation processes and support radical change towards sustainable and resilient urban systems (cf. Wittmayer and Hölscher 2017 ).

Despite these shared starting points and aims, the perspectives ask distinct questions about transformations vis-à-vis urban systems. They look at systemic change dynamics taking place in cities (“in”), the outcomes of systemic change of cities (“of”), or systemic change on global and regional levels driven by cities (“by”). These entry points and corresponding questions manifest in differences along key descriptors of urban transformations (cf. Hölscher et al. 2018 ). The differences are not contradictory: they generate complementary insights for understanding and supporting urban transformations given the different level of aggregation, analysis and understanding of system dynamics and points of intervention (Table 1 ). 

The main aim of the perspectives is to facilitate structuring of urban transformation research along shared themes and questions. Specifically, in articulating these, we show the actionable knowledge generated through each perspective to support urban transformations for sustainability and resilience. We also show that the perspectives offer bridges across knowledge to strengthen research and practice.

Transformation in cities: cities as places of transformations

Transformation in cities focuses on unravelling the diverse, local, regional and global factors, processes and interactions that converge in cities as places of transformations, thus driving or constraining place-based transformations.

The perspective zooms in on cities as spaces and places. Cities are geolocated in an objective, abstracted point, i.e. space, which is for example demarcated by geographical and administrative boundaries. Cities as places are defined by the physical (i.e. urban form) and philosophical (i.e. imagination and representation) relationships between people and place (Roche, 2016 ; Knox 2005 ). Thus, cities as places are both “a centre of meaning and the external context of people’s actions” (Knox 2005 : 2). As spaces and places of transformations, cities harbour specific potentials, driving forces and barriers (Hansen and Coenen 2015 ).

Place-based transformations are the result of the social construction by people responding to the opportunities and constraints of their particular locality (Fratini and Jensen 2017 ; Späth and Rohracher 2014 ). Endogenous conditions and developments include geographic location, climate, local economic structure, population dynamics and the built environment. For example, urban segregation and inequality result from and are reinforced by interactions between residential choices, personal preferences, job markets, land and real estate markets and public policies (Alberti et al. 2018 ). The construction of place-based transformations does not take place independently of societal norms and representations of the world. Economic and cultural globalisation and the resulting ‘network society’ becomes manifest in cities and shape place-based transformation dynamics (Roche, 2016 ). Scholars seeking to understand the ‘geography in transitions’ emphasise that cities are positioned within cross-scale spatial and institutional contexts that influence local change dynamics (Hansen and Coenen 2015 ; Truffer et al. 2015 ; Coenen et al. 2012 ; Hodson et al. 2017 ; McLean et al. 2016 ). Along similar lines, Loorbach et al. ( 2020 ) show the translocal character of social innovations that are locally rooted but globally connected.

This perspective positions transformative agency as deeply embedded in socio-spatial contexts. A central research focus is on urban niches that experiment with and scale new solutions (McLean et al. 2016 ; Ehnert et al. 2018 ), governance arrangements (Wolfram 2019 ; Hölscher et al. 2019a ) and ways of relating and knowing (Frantzeskaki and Rok 2018 ). Urban experimentation or real-world laboratories have become process tools to facilitate co-creative and innovative solution finding processes that empower actors to deal with urban problems, for example related to mobility, regeneration, community resilience or green job creation (Bulkeley et al. 2019 ; von Wirth et al. 2019 ; Hölscher et al. 2019c ). Such approaches represent situated manners of  place-making to co-develop inspiring ‘narratives of place’, empower local communities and foster urban transformative capacities (Wolfram 2019 ; Jensen et al. 2016 ; Ziervogel, 2019 ; Castán Broto et al. 2019 ). The idea of place-specificity recognises the particular role of ‘sense of place’ and ‘place attachment’, which can be an outcome of experimentation and in turn drive transformative change (Frantzeskaki et al., 2016 ; di Masso et al. 2019 ; Brink and Wamsler 2019 ). Ryan ( 2013 ) describes how multiple small ‘eco-acupuncture’ interventions can shift the community’s ideas of what is permissible, desirable and possible.

A key value of this perspective lies on its embedded research inquiry into the ‘black box’ of a city, including social, economic and ecological situated and contextual knowledge. A main implication for urban policy and planning practice is to facilitate place-based innovation by going beyond sectoral infrastructuring and top-down masterplanning towards situated and cross-sectoral place-making. Experimental and co-creative governance approaches help recognise and mobilise place-specific capacities. The need for place-based innovation further calls for higher-level policies to be centred on the local dimension. For example, the current European Union Cohesion Policy puts a place-based approach into practice that recognises place variety (Solly 2016 ) and further extends it to a governance capacity building programme that engages with cities on the ground through the URBACT program ( www.urbact.eu ).

A limitation of this perspective is that knowledge about and actions instigating transformations in a specific city context are very entrenched in context dynamics. This can  limit transferability or scaling other than ‘scaling deep’ pathway (Moore et al.  2015 ; Lam et al. 2020 ) if not connected with mechanisms for global and transnational learning and knowledge transfer (Section 2.3). In (Moore et al. 2015 ; Lam et al. 2020 ) addition, neighbourhood-level interventions need to be connected to knowledge about city-level outcomes. This calls for critical evaluations of systemic outcomes in urban systems (Section 2.2).

Transformation of cities: outcomes of transformation dynamics in urban systems

Transformation of cities examines and evaluates the outcomes of transformation dynamics in urban (sub-)systems in terms of new urban functions, local needs and interactions and implications for sustainability and resilience.

This perspective focuses on urban (sub-)systems defined by specific functions (e.g. economy, energy, transport, food, healthcare, housing). Compared to the other perspectives, it most explicitly applies socio-technical and social-ecological, and increasingly SETS, frameworks to describe urban (sub-)systems. Urban transformations are the outcome of radical changes of dominant structures (e.g. infrastructures, regulations), cultures (e.g. values) and practices (e.g. mobility behaviours) of such urban (sub-)systems. As a result of these changes, what kind of and how system functions are delivered is fundamentally altered (Ernst et al. 2016 ).

The main aim of this perspective is to explain and evaluate how transformation dynamics affect urban systems’ functions. Frameworks and models to investigate how transformation dynamics influence urban (sub-)systems pay attention to the complex processes and feedback loops within, across and beyond urban systems and the accumulated effects on the urban system level. For example, studying social-ecological-technical infrastructure systems in cities advances understanding of urban structure-function relationships between green space availability, wellbeing, biodiversity and climate adaptation (McPhearson 2020 ). Similarly, urban metabolism analysis and ecosystem studies seek to understand energy and material flows (Bai 2016 ; Dalla Fontana and Boas 2019 ). An emerging perspective on cities as ‘multi-regime’ configurations investigates dynamics across different functional systems (e.g. energy, water, mobility, food) (Grin et al. 2017 ; Irvine and Bai 2019 ). This provides opportunities to unveil interactions across multiple urban systems and scales. For instance, rapid changes in electricity systems can have knock-on effects for urban mobility or heat systems (Chen and Chen 2016 ; Chelleri et al. 2015 ). The relational geography perspective puts forth a differentiated view of urban systems: it zooms in on different boroughs, districts or neighbourhoods and raises questions such as how innovation and change in one location affects neighbouring locations (Wachsmuth et al. 2016 ).

This perspective most explicitly addresses prescriptive, ‘goal’-driven and recently mission-driven orientations for reinventing cities to be more sustainable, resilient, inclusive, attractive, prosperous, safe and environmentally healthy (Elmqvist et al. 2018 ; Kabisch et al. 2018 ; Rudd et al. 2018 ). Researchers and urban practitioners and planners employ concepts like ‘sustainability’ and ‘resilience’ as frames to evaluate the state of urban systems and to inform urban planning and regeneration programmes (Elmqvist et al. 2019 ). The systemic focus and application of such concepts also helps to identify synergies and trade-offs across urban systems and goals. For example, the sustainability paradigm of maximising efficiency in mobility or energy systems might result in vulnerability to natural disasters when systems lack parallel or redundant back-up systems (ibid.). Similarly, scholars point to the risks of green gentrification: while urban greening interventions have multiple benefits for the environment and climate adaptation, if not planned and governed inclusively, they can create unintended dynamics of exclusion, polarisation and segregation (Anguelovski et al. 2019 ; Haase et al. 2017 ).

This perspective takes a meta-level view on the agency and governance in cities, highlighting strategic partnerships and interventions based on desired system-level outcomes. From this perspective, cities may act as coherent strategic entities based on systemic understandings of city-specific and long-term effects to pursue managed transitions of their large-scale (sub-)systems (Jensen et al. 2016 ; Hodson et al., 2017 ). Urban transformation governance needs to facilitate alignment, foresight and reflexive learning to recognise, anticipate and shape transformation dynamics and leverage points (Hölscher et al. 2019b ). Key starting points are shared definitions of what ‘desirability’ means in specific contexts. Orchestration can align priorities and connect emerging alternatives, ideas, people and solutions (ibid.; Hodson et al., 2017 ). Shared and long-term visions re-orient short-term decisions and interventions that create synergies across multiple priorities. For example, Galvin and Maassen ( 2020 ) analyse Medellín’s (Columbia) mobility transformation that also contributed to inclusiveness and public safety. Transition management is a practice-oriented framework to co-develop shared visions, pathways and experiments in an ongoing learning-by-doing and doing-by-learning way (Frantzeskaki et al. 2018b ; Loorbach et al. 2015 ).

In summary, this perspective provides a view on interpreting transformation dynamics and developing orientations and practical guidance for intervention. It becomes visible in urban planning and policy practice through the development of systemic urban concepts as ‘anchor points’ or attractors for urban transformations such as ‘sharing cities’, ‘circular cities’, or ‘renaturing cities’. Cities like Rotterdam in the Netherlands and New York City in the USA are using such concepts to formulate long-term climate, sustainability and resilience agendas and establish cross-cutting city-level partnerships for their implementation (Hölscher et al. 2019a ). A main implication of this perspective is about the need to institutionalise and prioritise such long-term agendas into policy and planning across sectors and scales (ibid.).

A limitation of this perspective is that it overlooks place-specific implications and can nuance or be agnostic to politics and contestations at local sub-system level. Strategically linking place-based initiatives (Section 2.1) with systemic urban concepts and visions provides a powerful tool to align the multitude of activities taking place in cities and to coordinate urban transformations on (sub-)system scale. Additionally, this perspective requires explicit attention to the relationships between urban systems and their hinterlands or other distant territories, which affect and are affected by urban system’s functioning (Section 2.3).

Transformation by cities: cities as agents of change at global scale

The third perspective on transformation by cities draws attention to the changes taking place on global and regional levels as a result of urbanisation and urban development.

The main emphasis is here placed on cities as “agents of change at global scale” (Acuto 2016 ). As open systems, cities are not just influenced by developments outside their spatial boundaries (see Section 2.1). Urban transformations also have implications on global resources, environmental conditions, commodities and governance.

On the one hand, cities – including their social-ecological-technological configurations and the diversity of actors influencing them – can be viewed as culprits driving global high emissions, resource depletion and unsustainability. This raises critical questions about the relationship between current and unprecedented urbanisation and global sustainability (Seto et al. 2017 ; Haase et al. 2018 ). For example, the expansion of cities will triple land cover by 2030, compared to 2000, with severe implications on biodiversity (Alberti et al. 2018 ; Elmqvist et al. 2013 ). Different frameworks and concepts are employed to describe and assess the linkages between cities and their hinterland and other distant territories, including ‘urban land teleconnections’ (Seto et al. 2012 ), ‘regenerative cities’ (Girardet 2016 ) and ‘urban ecological footprint’ (Folke et al. 1997 ; Hoornweg et al. 2016 ; Rees and Wackernagel 2008 ).

On the other hand, cities have become key loci for trialling sustainable approaches and solutions that inform the global sustainability agenda (UN-Habitat 2016b ; Seto et al. 2017 ; Bai et al. 2018 ). Cities – especially local governments – play key roles in shaping global sustainability programmes and discourses and in developing and sharing knowledge and best practices. Local governments have also become celebrated for taking action when the national government is not (van der Heijden 2018 ; Acuto 2016 ). Governance strategies such as experimentation, best practices or imaginaries have been taken up globally (Haarstad 2016 ; McCann 2011 ; van der Heijden 2016 ). This raises questions about how the experiences and best practices showcased in cities become knowledge to be diffused and shared, as well as how transformations travel between places and across scales (Lam et al. 2020 ).

This perspective supports a polycentric and multi-level approach to global environmental governance. Global environmental governance is becoming increasingly decentralised and polycentric, which is visible for example in climate governance (Ostrom 2014 ; Jordan et al. 2018 ; Hölscher and Frantzeskaki 2020 ) and the urban SDG (UN 2016 ). The recent ‘city charters’ of global organisations such as the IPCC Cities and Climate Change, the Convention on Biological Diversity and Cities and Future Earth Urban Knowledge Network, showcase the recognition of ‘cities’ as key players on a global level. While urban sustainability governance has often proliferated without leadership at national levels, the nestedness of local governance in legal and institutional frameworks at regional, national and international levels requires alignment of priorities and legislation across governance levels (Hughes et al. 2017 ; Keskitalo et al. 2016 ).

In summary, this perspective creates knowledge about the role of cities in contributing to global change and what it means for governance, policy and planning at global, national, metropolitan and regional levels. It provides and requires big data from cities and their resource footprints, flows and dynamics so as to draw on patterns and pathways for change that can inform and reinforce global agendas for action. A key mechanism for urban practitioners is to strengthen policy knowledge exchange across frontrunning cities (Hölscher et al. 2019a ). Transnational city networks such as the International Council for Local Environmental Initiatives (ICLEI), C40 and 100 Resilient Cities facilitate knowledge exchange and inter-city learning, foster the creation of collective goals, lobby for international attention, and enable the transplantation of innovative, sustainable and resilient policy and planning approaches (Acuto et al. 2017 ; Lee 2018 ; Mejía-Dugand et al. 2016 ; Frantzeskaki et al. 2019 ; Davidson et al. 2019 ).

A danger of this perspective is that this global discourse is mainly focused on ‘global cities’. Medium-sized and middle-income cities are leaders in terms of actual sustainability performance and need to be actively acknowledged and considered (Vojnovic 2014 ). Florida ( 2017 ) criticises how “winner-take-all cities” reinforce inequality, while many cities stagnate and middle-class neighbourhoods disappear. This requires more research into how resources and opportunities are distributed and made accessible across different cities, for example ‘global’ cities, metropolitan cities and developing countries’ cities (Coenen et al. 2012 ; Gavin et al. 2013 ). Additionally, cities are not necessarily a united front: priorities and interpretations differ across cities (Growe and Freytag 2019 ). To address these issues, this perspective would benefit from a more critical and contextual research approach on place-based transformations (Section 2.1), questioning why transformations occur and are supported in some places and not others. Comparative analyses into the factors and dynamics influencing place-based transformations can facilitate transnational knowledge transfer and upscaling of place-based initiatives.

Conclusions

We offer three perspectives on urban transformations research as a means to cherish and celebrate, but also structure the diversity of the growing urban transformations research field. Our paper is a first attempt to distinguish these perspectives, by discussing key questions, entry points, practical implications and limitations. We show that the perspectives help converge research approaches and clarify how different perspectives provide evidence for urban policy and planning.

The perspectives are not merely conceptual devices: they show up in cities’ agendas, programmes and approaches and give guidance to practitioners. The ‘transformation in cities’ perspective asks practitioners to experiment with collaborative place-making approaches like urban living labs to integrate local knowledge and strengthen a sense of place and empowerment. The ‘transformation of cities’ perspective appears as underlying integrative systems’ approach for core urban strategies such as climate change and biodiversity strategies. The ‘transformation by cities’ perspective highlights the need to invest in policy knowledge exchange between cities, for example through transnational city networks.

The three perspectives on urban transformation do not exist in isolation from one another. We have shown how the perspectives can feed into and complement each other to address respective research gaps and practical challenges. The main future research direction we put forth is to bridge across the perspectives to address their respective limitations and generate comprehensive actionable knowledge. This means to formulate integrative research questions bridging across perspectives: How do place-making initiatives in a specific neighbourhood affect urban systems’ functioning? How can place-based transformation knowledge be transferred to other city contexts? How can place-based experiments and transformation initiatives or projects inform policy at city and city-network level? What are the conditions for downscaling strategic initiatives from global level – for example, post-Aichi biodiversity targets – considering capacities of urban sub-systems?

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

US Department of Housing and Urban Development

International Council for Local Environmental Initiatives

International Panel on Climate Change

Sustainable Development Goal

Social-ecological-technological system

United NationsMeerow, S

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2024 Environmental Performance Index: A Surprise Top Ranking, Global Biodiversity Commitment Tested

The Baltic nation of Estonia is No. 1 in the 2024 rankings, while Denmark, one of the top ranked countries in the 2022 EPI dropped to 10 th place, highlighting the challenges of reducing emissions in hard-to-decarbonize industries. Meanwhile, “paper parks” are proving a global challenge to international biodiversity commitments.

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In 2022, at the UN Biodiversity Conference, COP 15, in Montreal over 190 countries made what has been called “the biggest conservation commitment the world has ever seen.”  The Kunming-Montreal Global Biodiversity Framework called for the effective protection and management of 30% of the world’s terrestrial, inland water, and coastal and marine areas by the year 2030 — commonly known as the 30x30 target. While there has been progress toward reaching this ambitious goal of protecting 30% of land and seas on paper, just ahead of World Environment Day, the 2024 Environmental Performance Index (EPI) , an analysis by Yale researchers that provides a data-driven summary of the state of sustainability around the world, shows that in many cases such protections have failed to halt ecosystem loss or curtail environmentally destructive practices.

A new metric that assesses how well countries are protecting important ecosystems indicated that while nations have made progress in protecting land and seas, many of these areas are “paper parks” where commercial activities such as mining and trawling continue to occur — sometimes at a higher rate than in non-protected areas. The EPI analyses show that in 23 countries, more than 10% of the land protected is covered by croplands and buildings, and in 35 countries there is more fishing activity inside marine protected areas than outside. 

“Protected areas are failing to achieve their goals in different ways,” said Sebastián Block Munguía, a postdoctoral associate with the Yale Center for Environmental Law and Policy (YCELP) and the lead author of the report. “In Europe, destructive fishing is allowed inside marine protected areas, and a large fraction of the area protected in land is covered by croplands, not natural ecosystems. In many developing countries, even when destructive activities are not allowed in protected areas, shortages of funding and personnel make it difficult to enforce rules.”

The 2024 EPI, published by the Yale Center for Environmental Law and Policy and Columbia University’s Center for International Earth Science Information Network ranks 180 countries based on 58 performance indicators to track progress on mitigating climate change, promoting environmental health, and safeguarding ecosystem vitality. The data evaluates efforts by the nations to reach U.N. sustainability goals, the 2015 Paris Climate Change Agreement, as well as the Kunming-Montreal Global Biodiversity Framework. The data for the index underlying different indicators come from a variety of academic institutions and international organizations and cover different periods. Protected area coverage indicators are based on data from March 2024, while greenhouse emissions data are from 2022.

Estonia has decreased its GHG emissions by 59% compared to 1990. The energy sector will be the biggest contributor in reducing emissions in the coming years as we have an aim to produce 100% of our electricity consumption from renewables by 2030.”

The index found that many countries that were leading in sustainability goals have fallen behind or stalled, illustrating the challenges of reducing emissions in hard-to-decarbonize industries and resistant sectors such as agriculture. In several countries, recent drops in agricultural greenhouse gas emissions (GHG) have been the result of external circumstances, not policy. For example, in Albania, supply chain disruptions led to more expensive animal feed that resulted in a sharp reduction in cows and, consequentially, nitrous oxide and methane emissions.

Estonia leads this year’s rankings with a 40% drop in GHG emissions over the last decade, largely attributed to replacing dirty oil shale power plants with cleaner energy sources. The country is drafting a proposal to achieve by 2040 a CO2 neutral energy sector and a CO2 neutral public transport network in bigger cities.

“Estonia has decreased its GHG emissions by 59% compared to 1990. The energy sector will be the biggest contributor in reducing emissions in the coming years as we have an aim to produce 100% of our electricity consumption from renewables by 2030,” said Kristi Klaas, Estonia’s vice-minister for Green Transition. Klaas discussed some of the policies that led to the country's success as well as ongoing challenges, such as reducing emissions in the agriculture sector, at a webinar hosted by YCELP on June 3.  Dr. Abdullah Ali Abdullah Al-Amri, chairman of the Environment Authority of Oman, also joined the webinar to discuss efforts aimed at protecting the county's multiple ecosystems with rare biodiversity and endangered species, such as the Arabian oryx, and subspecies, such as the Arabian leopard. 

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 Denmark, the top ranked country in the 2022 EPI dropped to 10th place, as its pace of decarbonization slowed, highlighting that those early gains from implementing “low-hanging-fruit policies, such as switching to electricity generation from coal to natural gas and expanding renewable power generation are themselves insufficient,” the index notes. Emissions in the world’s largest economies such as the U.S. (which is ranked 34th) are falling too slowly or still rising — such as in China, Russia, and India, which is ranked 176th.

Over the last decade only five countries — Estonia, Finland, Greece, Timor-Leste, and the United Kingdom — have cut their GHG emissions over the last decade at the rate needed to reach net zero by 2050. Vietnam and other developing countries in Southeast and Southern Asia — such as Pakistan, Laos, Myanmar, and Bangladesh — are ranked the lowest, indicating the urgency of international cooperation to help provide a path for struggling nations to achieve sustainability.

“The 2024 Environmental Performance Index highlights a range of critical sustainability challenges from climate change to biodiversity loss and beyond — and reveals trends suggesting that countries across the world need to redouble their efforts to protect critical ecosystems and the vitality of our planet,” said Daniel Esty, Hillhouse Professor of Environmental Law and Policy and director of YCELP.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Simple Behavioral Analysis (SimBA) is an open-source machine learning platform designed for the automated detection of animal behaviors. It leverages markerless key-point pose tracking from open-source programs like SLEAP and DeepLabCut to transform tracking data into features that describe relationships between body parts over time. Researchers can train classifiers within SimBA to detect specific behaviors based on these features. A key feature of SimBA is its integration with Shapley Additive Explanations (SHAP) which provides users with quantitative insights into which features are critical to their behavioral predictions. The explainability of the outcome facilitates the standardization and sharing of behavioral definitions across labs, making complex behavioral analysis accessible to non-specialists. Further, by computing and sharing these explainability metrics, behavioral classifiers can be reconceptualized as shareable reagents akin to the commonly used Research Reagent Identifiers (RRIDs) system for wet lab reagents, enhancing reproducibility and interpretability between research groups.

Goodwin, Nastacia L; Choong, Jia J; Hwang, Sophia; Pitts, Kayla; Bloom, Liana; Islam, Aasiya; Zhang, Yizhe Y; Szelenyi, Eric R; Tong, Xiaoyu; Newman, Emily L; Miczek, Klaus; Wright, Hayden R; McLaughlin, Ryan J; Norville, Zane C; Eshel, Neir; Heshmati, Mitra; Nilsson, Simon R O; Golden, Sam A

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Acknowledgements

The authors thank the thousands of scientists whose data and papers have made this Review possible. This Review is a joint effort of the working group sUrbio2050 kindly supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118).

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Robert I. McDonald & Katie Crossman

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Andressa V. Mansur, Andrea Pacheco, Henrique M. Pereira & Alexandra S. Werner

CIBIO/InBio, Universidade do Porto, Porto, Portugal

Fernando Ascensão & Henrique M. Pereira

Department of Conservation Biology, Estación Biológica de Doñana, Seville, Spain

Fernando Ascensão

The Nature of Cities, New York, NY, USA

M’lisa Colbert & David Maddox

Stockholm Resilience Center, Stockholm University, Stockholm, Sweden

Thomas Elmqvist

Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Quebec, Canada

Andrew Gonzalez

Department of Geography, Texas A&M University, College Station, TX, USA

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Secretariat of the Convention on Biological Diversity, Montreal, Quebec, Canada

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Yale School of Forestry & Environmental Studies, Yale University, New Haven, CT, USA

Kangning Huang, Karen C. Seto & Rohan Simkin

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Authors co-designed the literature review during a working group meeting. A.V.M. led the literature review, which all authors contributed to. R.I.M. wrote the initial version of this manuscript, with significant feedback and guidance from H.M.P. and A.V.M. All authors made substantial contributions to the intellectual content, analysis and interpretation of the literature review, and editing of the manuscript.

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Supplementary information

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Supplementary Methods.

Supplementary Table 1

Table S1. Forecasted urban-caused natural habitat loss, by country or other administrative unit (2000–2030). Results are sorted in descending order of the percentage of the total land area on which natural habitat was forecast to be lost to urban growth, from greatest to least urban impact. Small administrative units or other units with no data (for example, Antarctica) are not shown in this table.

Supplementary Table 2

Table S2. Forecasted urban-caused natural habitat loss, by biome and country-level income group (2000–2030). Results are sorted in descending order of the percentage of the total land area on which natural habitat was forecast to be lost to urban growth, from greatest to least urban impact.

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McDonald, R.I., Mansur, A.V., Ascensão, F. et al. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat Sustain 3 , 16–24 (2020). https://doi.org/10.1038/s41893-019-0436-6

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Received : 19 April 2019

Accepted : 30 October 2019

Published : 09 December 2019

Issue Date : January 2020

DOI : https://doi.org/10.1038/s41893-019-0436-6

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