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Sep 1, 2022 · Disaggregating spatial data on large spatial domains and fine spatial resolutions is intensive. • We present a scalable method to estimate the parameters of a disaggregation model. • The proposed method is iterative, so we provide a proof of convergence. • We illustrate its application with a simulation and a real example. •
Sep 1, 2024 · Methods relevant for spatial disaggregation of climate action plans are reviewed. • Key methods: proxy data, machine learning, and geostatistical model-based approaches. • Appropriate method depends on domain knowledge, avaibility of local-level data, etc. • Combining different spatial disaggregation methods can enhance accuracy. •
3 days ago · The spatial resolution of our maps is comparably high, relative to existing spatial disaggregation approaches (You et al. (2014), Lamboni et al. (2016), Jackson et al. (2019)). Satellite remote sensing-based studies have classified crop types at continental or national scale with even higher spatial resolution ( d'Andrimont et al., 2021 ; Preidl et al., 2020 ).
Jan 5, 2022 · This paper describes the Disaggregator tool, developed for the aggregation and disaggregation of spatial data. There exist several types of point data that need to be aggregated for more effective visualization.
Jan 23, 2022 · This paper summarizes the research advances of population disaggregation in terms of methodology, ancillary data, and products and discusses the role of spatial disaggregation of population statistical data in monitoring and evaluating SDG indicators.
Jul 26, 2019 · This article reports on experiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetric mapping and pycnophylactic interpolation, using modern machine learning methods to combine different types of ancillary variables, in order to disaggregate historical census data into a 200 m resolution grid.
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What is spatial disaggregation?
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Nov 22, 2022 · Spatial disaggregation methods aim to compute these fine-grained estimates, often using regression algorithms that employ ancillary data to re-distribute the aggregated information.
