Roteiro metodológico para análise espacial da covid-19 em áreas urbanas não metropolitanas brasileiras by João Pedro Pereira Caetano de Lima, Raul Borges Guimarães

“…Zonal implementation with choropleth maps with some examples of epidemiological rates and bivariate choropleth maps; III. …”
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Guide méthodologique pour l'analyse spatiale du covid-19 dans les zones urbaines non métropolitaines du Brésil by João Pedro Pereira Caetano de Lima, Raul Borges Guimarães

“…Zonal implementation with choropleth maps with some examples of epidemiological rates and bivariate choropleth maps; III. …”
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Relationship between hospitalization from cannabis usage and pulmonary tuberculosis in Thailand from 2017 to 2022. by Kemmapon Chumchuen, Wit Wichaidit, Virasakdi Chongsuvivatwong

“…Temporal and spatial patterns were visualized using line plots and choropleth maps, respectively. A matched cohort analysis was conducted to compare the incidence density rate of subsequent tuberculosis admission and the hazard ratio. …”
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Predictors of county-level diabetes-related mortality risks in Florida, USA: a retrospective ecological study by Nirmalendu Deb Nath, Agricola Odoi

“…Geographic distribution of DRMR were displayed in choropleth maps and ordinary least squares (OLS) regression model was used to identify county-level predictors of DRMR. …”
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A socioeconomic disadvantage index for informing policy, systems, and environmental change interventions for senior nutrition programs by Shadi Maleki, Cassandra M. Johnson, Francis A. Méndez Mediavilla, Ramalingam Shanmugam, Lesli Biediger-Friedman

“…An unweighted sum was used to create the OASDI, where a higher score indicated greater socioeconomic disadvantage. Choropleth maps were used to visualize the OASDI and persistent poverty for all census tracts within the study area. …”
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Spatial analysis and associated risk factors of HIV prevalence in Botswana: insights from the 2021 Botswana AIDS Impact Survey (BAIS V) by Sethunya R. Simela, Matlhogonolo Kelepile, Thabo I. Sebobi

“…HIV prevalence estimates were visualized through choropleth maps, and spatial autocorrelation was analyzed using Anselin Local Moran’s I in ArcGIS 10.8.2 software. …”
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Investigation of geographic disparities and temporal changes of non-gestational diabetes-related emergency department visits in Florida: a retrospective ecological study by Md Marufuzzaman Khan, Agricola Odoi

“…County-level non-gestational diabetes-related ED visit rates per 100,000 person-years were calculated and their geographic distributions were visualized using choropleth maps. Clusters of counties with high non-gestational diabetes-related ED visit rates were identified using Kulldorff’s circular and Tango’s flexible spatial scan statistics. …”
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