The effects of population aggregation in geospatial electrification planning

Khavari, Babak; Sahlberg, Andreas; Usher, Will; Korkovelos, Alexandros; Nerini, Francesco Fuso

doi:10.1016/j.esr.2021.100752

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…A continuous gridded population dataset can offer more spatially detailed information and allows for analysis of the unevenly changing relationship between humans and nature at a pixel scale over time. It was recognized as essential data source for various applications, such as epidemiology, urban planning, environmental management, assessment of risks to vulnerable population, energy crises, global inequities, and assessment of progress toward the Sustainable Development Goals (SDGs) 3 – 10 .…”

Section: Background and Summarymentioning

confidence: 99%

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

Liu,

Cao,

et al. 2024

Sci Data

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Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.

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Section: Background and Summarymentioning

confidence: 99%

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

Liu,

Cao,

et al. 2024

Sci Data

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“…A sensitivity analysis is performed using the five demand Tiers (Table 3) to understand how the new modules impact the technology mix at different levels of demand. Demand has been shown to often be the most impactful factor for technology choice from previous geospatial electrification studies [12], [38], [42]. The demographic and techno-economic parameters used in the analysis, as well as the demand estimations for the base scenario, are found in Appendix B.…”

Section: Full Detail X X Xmentioning

confidence: 99%

“…E.g. Sahlberg et al developed a scenario-discovery approach using OnSSET, based on 1,944 simulations [38], and Khavari et al developed a global sensitivity analysis from 2,080 simulation which was replicated for three countries for a total of 6,240 simulations [42]. While such experiments can still be performed using the detailed model presented in this paper, the computational resources or time allotted need to be vastly increased as compared to the light version of the OnSSET model.…”

Section: Computational Time and Modelling Detailmentioning

confidence: 99%

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Attention to detail – exploring effects on technology selection in geospatial electrification modelling

Sahlberg,

Korkovelos,

Kabongo

et al. 2023

Preprint

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As of 2021, 675 million people globally lack access to electricity. Geospatial electrification tools can be used to identify the mix of grid-extension, mini-grids and stand-alone technologies that can supply currently unelectrified areas at the lowest cost. Several such tools have been developed, at different levels of modelling detail and complexity. In this paper, we improve the Open Source Spatial Electrification Tool (OnSSET) to develop a flexible geospatial electrification tool that can still run lighter rapid assessments for a first estimate of the technology split, but now also more detailed analysis with higher spatial and temporal resolution used for grid routing, distribution network design and optimization of hybrid mini-grid generation introduced through new algorithms. We compare the existing light and new detailed versions of the tool through a case study of the north-western parts of the Democratic Republic of the Congo. We find that the new grid routing algorithm lead to more off-grid technologies, and that the detailed design of distribution networks lead to a reduction in stand-alone technologies. The detailed optimization of hybrid mini-grids display varying effects at different demand levels. Given the increased computational effort that is observed with higher modelling detail, we discuss the implications for scenario design and selection of geospatial electrification tool for future analyses aiming to support the achievement of SDG 7.

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“…Spatial autocorrelation issues can arise due to artificial statistical boundaries [94], [95], therefore in such spatial statistical problems it is wise to generate bespoke area boundaries which best represent the problem being modeled. This is a common issue for strategic assessment of telecom and energy network options [96], [97]. Consequently, a set of 'modeling regions' are defined which consist of merging local areas to ensure each has a least one major settlement to route data traffic to.…”

Section: B Spatial Processingmentioning

confidence: 99%

Engineering-Economic Evaluation of Diffractive NLOS Backhaul (e3nb): A Techno-economic Model for 3D Wireless Backhaul Assessment

2022

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The effects of population aggregation in geospatial electrification planning

Cited by 10 publications

References 28 publications

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

Attention to detail – exploring effects on technology selection in geospatial electrification modelling

Engineering-Economic Evaluation of Diffractive NLOS Backhaul (e3nb): A Techno-economic Model for 3D Wireless Backhaul Assessment

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