Examining the correlates and drivers of human population distributions across low- and middle-income countries

Nieves, Jeremiah J.; Stevens, Forrest R.; Gaughan, Andrea A.E.; Linard, Catherine; Sorichetta, Alessandro; Hornby, Graeme; Patel, Nirav; Tatem, Andrew J.

doi:10.1098/rsif.2017.0401

Cited by 64 publications

(74 citation statements)

References 72 publications

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“…The methodology for SDG 11.3.1 is established and referenced in the SDG indicator Metadata Repository managed by UNDESA (https://unstats.un.org/sdgs/metadata). LUE monitors the "ratio of land consumption rate to population growth rate" and aims to quantify the use of land as a consequence of urban expansion pressures (demographic and economic) [54,[69][70][71][72][73] with a dimensionless number. To estimate LUE, it is first necessary to quantify the rate of land consumption (LCR) and the population growth rate (PGR) in a given spatial unit and time span (∆t).…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Furthermore, there are other "intangibles" such as local, regional, and national land use or development policies, which almost certainly shape BS growth, but are typically not in an accessible format, if available at all. The value of using population data to predict growth of settlement, shown here in a semi-independent model framework, and the value of using urban/settlement feature data sets to predict population distribution (108), raises the question of whether within a strictly predictive modelling framework (109,110), i.e. no inference on causality and measuring effect, if it is worthwhile or proper to try to fully separate population and settlement given their reciprocal causal links via economics, i.e.…”

Section: Discussionmentioning

confidence: 99%

Modelling Built-Settlements between Remotely-Sensed Observations

Nieves¹,

Sorichetta²,

Linard³

et al. 2019

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Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual builtsettlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category "built" in each year. This modelling framework shows strong promise for filling gaps in crosssectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.As a result, many studies have turned to a definition based upon the remotely-sensed physical features of urban areas, i.e. the built-environment. However, even reducing the definitional scope of urban to its physical dimension, the form of built-environment can widely vary across space and time due to materials used, differences in urban morphology, and the surrounding environmental context (18,31,32). Initially, remotely sensed urban definitions were optically-based thematic classifications of land cover, typically captured the "built-environment," including buildings, roads, runways, and, sometimes erroneously, bare soil (18,(33)(34)(35). Later improvements using supporting information about the surrounding environment and vegetation during post-processing helped discern the true built-environment from the surrounding land covers (18). Other notable advances include the use of high resolution orthographic imagery to detect subtle short-term built-environment change in China (36) and the use of Landsat imagery to create multi-temporal thematic representations of the built environment across the globe (37).Coinciding with advances in imagery, statistical methods, and computational resource availability, high-resolution datasets with global extent have been created either through combining multi-source optical imagery with contrast detection methods (38,39) or utilizing Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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“…However, while no assumptions are placed on the linearity or interactions present in relating ancillary data to population density (a feature of random forest modeling), we assume that the process resulting in those estimated associations at an aggregate level are, on the whole, representative of the process relating covariates to population density at the finer, gridded scale. In the absence of data on population densities at the finer scale of interest, of which we have none to estimate the model with or validate against across time, output based on this assumption has consistently shown to perform better than less complex or less informed disaggregation techniques (Stevens et al 2015;Gaughan et al 2016;Nieves, et al 2017). Despite the "ecological fallacy" inherent to this change-of-support (Gelfland, et al 2001;Holt, et al 1996) and likely biased outcome at the pixel level, the approach still manages to achieve comparable results to bottomup modeling using fine-scale model estimates (e.g., Engstrom, et al 2019).…”

Section: Gridded Population Intensity Estimatesmentioning

confidence: 99%

Missing millions: undercounting urbanization in India

et al. 2019

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The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the population is urban. We show that this is an artifact of the definition of urbanity and an underestimate of the level of urbanization in India. We use a random forest-based model to create a high-resolution (~100 m) population grid from district-level data available from the Indian Census for 2001 and 2011, a novel application of such methods to create temporally consistent population grids. We then apply a community-detection clustering algorithm to construct urban agglomerations for the entire country. Compared with the 2011 official statistics, we estimate 12% more of urban population, but find fewer mid-size cities. We also identify urban agglomerations that span jurisdictional boundaries across large portions of Kerala and the Gangetic Plain.

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Examining the correlates and drivers of human population distributions across low- and middle-income countries

Cited by 64 publications

References 72 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

Modelling Built-Settlements between Remotely-Sensed Observations

Missing millions: undercounting urbanization in India

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