Uncertainty in geospatial health: challenges and opportunities ahead.
Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.Meth...
| Publicado en: | Annals of Epidemiology Vol. 65; pp. 15 - 31 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jan2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154504679&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154504679 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10472797 J8R jtl: Annals of Epidemiology issn: 10472797 maglogo: N pubinfo: dt: Jan2022 vid: 65 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 154504679 154504679 NLM34656750 154504679 10.1016/j.annepidem.2021.10.002 NLM34656750 154504679 ppf: 15 ppct: 16 formats: tig: atl: Uncertainty in geospatial health: challenges and opportunities ahead. aug: au: Delmelle, Eric M. Desjardins, Michael R. Jung, Paul Owusu, Claudio Lan, Yu Hohl, Alexander Dony, Coline affil: Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, Finland sug: subj: Geographic Information Systems Uncertainty Statistics Methods Cluster Analysis Human ab: Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.Methods: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.Results: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health.Conclusions: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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