Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral...Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.展开更多
Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource ...Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.展开更多
Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relativel...Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects： （1） Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. （2） Historical maps and pictures offer visual and spatial quantitative land-cover information. （3） Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. （4） Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. （5） Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.展开更多
基金funded by the NASA Disasters Program grant#NH18ZDA001N001N.
文摘Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.
基金This work was supported by the Bill and Melinda Gates Foundation[OPP1134076,OPP1106427,OPP1032350,OPP1094793]National Institute of Allergy and Infectious Diseases[U19AI089674]Wellcome Trust[106866/Z/15/Z].
文摘Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.
基金National Natural Science Foundation of China, No.41271416 CAS ＂Strategic Priority Research Program＂, No.XDA05090310
文摘Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects： （1） Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. （2） Historical maps and pictures offer visual and spatial quantitative land-cover information. （3） Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. （4） Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. （5） Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.
