Urban Estimates and Projections at the United Nations: The Strengths, Weaknesses, and Underpinnings of the World Urbanization Prospects

Buettner, Thomas

doi:10.1007/s40980-015-0004-2

Cited by 66 publications

(57 citation statements)

References 16 publications

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“…Urban population data are collected and distributed by the United Nations Department for Economic and Social Affairs (UNDESA) which issues periodical updates of the World Urbanization Prospects (WUP) based on national reporting. Despite national data being compiled on the basis of the heterogeneous national definitions of urban areas and cities [57][58][59][60] (based on administrative, functional designations and/or population size and population density thresholds)-thus determining inconsistencies across spatial units and epochs due to a shortage of alternative sources-the UNDESA WUP has become the main source for urbanisation analyses and urbanisation studies [61]. Advanced georeferenced approaches to grid population data [62,63] offer alternative local and spatial information to map populated places on the planet.…”

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confidence: 99%

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

et al. 2019

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Sustainable Development Goal (SDG) 11 aspires to “Make cities and human settlements inclusive, safe, resilient and sustainable”, and the introduction of an explicit urban goal testifies to the importance of urbanisation. The understanding of the process of urbanisation and the capacity to monitor the SDGs require a wealth of open, reliable, locally yet globally comparable data, and a fully-fledged data revolution. In this framework, the European Commission–Joint Research Centre has developed a suite of (open and free) data and tools named Global Human Settlement Layer (GHSL) which maps the human presence on Earth (built-up areas, population distribution and settlement typologies) between 1975 and 2015. The GHSL supplies information on the progressive expansion of built-up areas on Earth and population dynamics in human settlements, with both sources of information serving as baseline data to quantify land use efficiency (LUE), listed as a Tier II indicator for SDG 11 (11.3.1). In this paper, we present the profile of the LUE across several territorial scales between 1990 and 2015, highlighting diverse development trajectories and the land take efficiency of different human settlements. Our results show that (i) the GHSL framework allows us to estimate LUE for the entire planet at several territorial scales, opening the opportunity of lifting the LUE indicator from its Tier II classification; (ii) the current formulation of the LUE is substantially subject to path dependency; and (iii) it requires additional spatially-explicit metrics for its interpretation. We propose the Achieved Population Density in Expansion Areas and the Marginal Land Consumption per New Inhabitant metrics for this purpose. The study is planetary and multi-temporal in coverage, demonstrating the value of well-designed, open and free, fine-scale geospatial information on human settlements in supporting policy and monitoring progress made towards meeting the SDGs.

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confidence: 99%

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

et al. 2019

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“…The URPAS data are based on observed changes in the proportion of the population living in urban areas by sex and five‐year age groups. Each country uses its own definition of urban, making cross‐country comparisons somewhat inexact (Buettner ). The population estimates are taken from censuses and population registers.…”

Section: Methodsmentioning

confidence: 99%

Rural/Urban Population Age and Sex Composition in sub‐Saharan Africa 1980–2015

Menashe‐Oren¹,

Stecklov²

2017

Population & Development Rev

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“…At present, United Nations Population Division data is widely used to track urbanization across the continent. But UN urban growth projections often over-emphasize primary cities and have been shown to be previously incorrect [22,23,[25][26][27]. UN data is only provided at the national-level and for select municipalities with populations greater than 300,000 residents [1], even though the UN estimated that in 2015 49% of Africans live in urban settlements with fewer than 300,000 people.…”

Section: Introductionmentioning

confidence: 99%

Variability in urban population distributions across Africa

Tuholske

Caylor

Avery

2019

Environ. Res. Lett.

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Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa's urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa's urban settlements distributions fit power law distributions exhibited by Zipf's law. Our results reveal that 77%-85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa's urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small-and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa's ever-growing urban settlements.

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Urban Estimates and Projections at the United Nations: The Strengths, Weaknesses, and Underpinnings of the World Urbanization Prospects

Cited by 66 publications

References 16 publications

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

Rural/Urban Population Age and Sex Composition in sub‐Saharan Africa 1980–2015

Variability in urban population distributions across Africa

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