The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

Leyk, Stefan; Gaughan, Andrea E.; Adamo, Susana B.; Sherbinin, Alex de; Balk, Deborah; Freire, Sérgio; Rose, Amy; Stevens, Forrest R.; Blankespoor, Brian; Frye, Charlie; Comenetz, Joshua; Sorichetta, Alessandro; MacManus, Kytt; Pistolesi, Linda; Levy, Marion J.; Tatem, Andrew J.; Pesaresi, Martino

doi:10.5194/essd-11-1385-2019

Cited by 252 publications

(297 citation statements)

References 117 publications

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“…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. This is generally achieved with a combination of EO-derived information and census data [64].…”

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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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“…Gridded data types involve overlaying a regular square grid on the study area, where each grid cell is an analytical unit. This reference unit enables the combination of a wide range of spatial (i.e., vector and raster) and nonspatial (i.e., tabular) datasets that can be accessed efficiently and consistently [93]. The analytical output is also in a gridded format, where the allocation of a "deprivation index" value to a grid cell does not involve assigning a label to a specific administrative unit but an areal unit, thus preventing neighborhood stigmatization.…”

Section: The Potential Of High-resolution Gridded Datasets To Map Depmentioning

confidence: 99%

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

et al. 2020

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Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

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“…The dataset is an estimate of population per 30 arc second pixel (which is at a higher resolution than 1 square kilometer) and updated annually [45]. LandScan data are regarded as an important data resource for global population distribution and accuracy and are regularly validated [46].…”

Section: Parcel Population Density Analysismentioning

confidence: 99%

Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration

et al. 2019

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Due to rapid, sprawling urban and industrial development, urbanization in China has led to serious environmental pollution with subsequent risks to human well-being. Landscapes comprised of intermingled residential and industrial areas are common across China, which is a large challenge for effective urban planning and environmental protection. Being able to identify industrial land across the urban landscape is critical for understanding patterns of urban design and subsequent consequences for the environment. Here, we describe a method to quickly identify industrial parcels using points of interest (POIs) and large-scale spatial data. We used the Beijing–Tianjin–Hebei urban agglomeration as a case study and identified 8325 square kilometers of industrial land, accounting for 30.7% of the total built land. Based on ground-truth randomly-sampled sites, the accuracy, precision, and recall of identified industrial areas were 87.1%, 66.4%, and 68.1%, respectively. Furthermore, we found that over 350 km2 of the industrial parcels were high human settlement risks and mainly were distributed in Tianjin and Tangshan city. Over 28.8% of the identified industrial land parcels might be at the risk of potential soil contamination. The results can be helpful in future urban planning and for identifying urban areas that are targets for implementing environmental risk management and remediation.

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The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

Cited by 252 publications

References 117 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

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration

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