Spatially referenced synthetic households for high-resolution population modeling

This paper presents a methodology for generating spatially referenced synthetic households by combining population synthesis techniques for small area estimation with dasymetric population modeling. The methodology results in an output vector layer of households represented as points with unique str...

पूर्ण विवरण

ग्रंथसूची विवरण
मुख्य लेखकों: Rubinyi, S, Hall, JW, Gussenbauer, J
स्वरूप: Working paper
भाषा:English
प्रकाशित: Environmental Change Institute, University of Oxford 2021
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author Rubinyi, S
Hall, JW
Gussenbauer, J
author_facet Rubinyi, S
Hall, JW
Gussenbauer, J
author_sort Rubinyi, S
collection OXFORD
description This paper presents a methodology for generating spatially referenced synthetic households by combining population synthesis techniques for small area estimation with dasymetric population modeling. The methodology results in an output vector layer of households represented as points with unique structural and socioeconomic attributes which can be applied towards a better understanding of population dynamics, particularly those characteristics which are geographically influenced and may not conform to administrative boundaries linked to aggregated census statistics. To demonstrate the effectiveness of the methodology, the paper provides a case study in Dumuria Upazila, Bangladesh, where an independent household survey is used to validate the location of the synthetic households and associated socioeconomic attributes. The study found that 98 percent of all surveyed households were within 100 meters of at least one synthetic household and the median distance observed was only 18 meters. The study also found that the age distribution of synthetic populations was within five percent of the surveyed population for each of the five age bands considered.
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spelling oxford-uuid:6df15a85-0f15-4826-a5ad-f51c655950ee2022-03-26T19:21:03ZSpatially referenced synthetic households for high-resolution population modelingWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:6df15a85-0f15-4826-a5ad-f51c655950eeEnglishSymplectic ElementsEnvironmental Change Institute, University of Oxford 2021Rubinyi, SHall, JWGussenbauer, JThis paper presents a methodology for generating spatially referenced synthetic households by combining population synthesis techniques for small area estimation with dasymetric population modeling. The methodology results in an output vector layer of households represented as points with unique structural and socioeconomic attributes which can be applied towards a better understanding of population dynamics, particularly those characteristics which are geographically influenced and may not conform to administrative boundaries linked to aggregated census statistics. To demonstrate the effectiveness of the methodology, the paper provides a case study in Dumuria Upazila, Bangladesh, where an independent household survey is used to validate the location of the synthetic households and associated socioeconomic attributes. The study found that 98 percent of all surveyed households were within 100 meters of at least one synthetic household and the median distance observed was only 18 meters. The study also found that the age distribution of synthetic populations was within five percent of the surveyed population for each of the five age bands considered.
spellingShingle Rubinyi, S
Hall, JW
Gussenbauer, J
Spatially referenced synthetic households for high-resolution population modeling
title Spatially referenced synthetic households for high-resolution population modeling
title_full Spatially referenced synthetic households for high-resolution population modeling
title_fullStr Spatially referenced synthetic households for high-resolution population modeling
title_full_unstemmed Spatially referenced synthetic households for high-resolution population modeling
title_short Spatially referenced synthetic households for high-resolution population modeling
title_sort spatially referenced synthetic households for high resolution population modeling
work_keys_str_mv AT rubinyis spatiallyreferencedsynthetichouseholdsforhighresolutionpopulationmodeling
AT halljw spatiallyreferencedsynthetichouseholdsforhighresolutionpopulationmodeling
AT gussenbauerj spatiallyreferencedsynthetichouseholdsforhighresolutionpopulationmodeling