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    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/32546

    標題: Choropleth map legend design for visualizing the most influential areas in article citation disparities A bibliometric study
    作者: Tsair-Wei Chien(錢才瑋)
    Hsien-Yi Wang(王憲奕)
    Hsu, Chen-Fang
    Kuo, Shu-Chun
    貢獻者: Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management, Chi Mei Med Ctr, Med Res Dept
    Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management, Dept Sport Management
    Chi Mei Med Ctr, Nephrol Dept
    Chi Mei Med Ctr, Dept Partiatr
    Chung Hwa Univ Med Technol, Dept Optometry
    Chi Mei Med Ctr, Dept Ophthalmol
    關鍵字: choropleth map
    Gini coefficient
    Google Maps
    legend design
    Pubmed Central
    日期: 2019-10
    上傳時間: 2020-07-29 13:49:20 (UTC+8)
    摘要: Background: Disparities in health outcomes across countries/areas are a central concern in public health and epidemiology. However, few authors have discussed legends that can be complemental to choropleth maps (CMs) and merely linked differences in outcomes to other factors like density in areas. Thus, whether health outcome rates on CMs showing the geographical distribution can be applied to publication citations in bibliometric analyses requires further study. The legends for visualizing the most influential areas in article citation disparities should have sophisticated designs. This paper illustrates the use of cumulative frequency (CF) map legends along with Lorenz curves and Gini coefficients (GC) to characterize the disparity of article citations in areas on CMs, based on the quantile classification method for classes. Methods: By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 7042 articles published from 1945 to 2016. A total number of 41,628 articles were cited in Pubmed Central (PMC). The publication outputs based on the author's x-index were applied to plot CM about research contributions. The approach uses two methods (i.e., quantiles and equal total values for each class) with CF legends, in order to highlight the difference in x-indices across geographical areas on CMs. GC was applied to observe the x-index disparities in areas. Microsoft Excel Visual Basic for Application (VBA) was used for creating the CMs. Results: Results showed that the most productive and cited countries in Medicine (Baltimore) were China and the US. The mostcited states and cities were Maryland (the US) and Beijing (China). Taiwan (x-index=24.38) ranked behind Maryland (25.97), but ahead of Beijing (16.9). China earned lower disparity (0.42) than the US (0.49) and the rest of the world (0.53) when the GCs were applied. Conclusion: CF legends, particularly using the quantile classification for classes, can be useful to complement CMs. They also contain more information than those in standard CM legends that are commonly used with other classification methods. The steps of creating CM legends are described and introduced. Bibliometric analysts on CM can be replicated in the future.
    關聯: Medicine, v.98, n.41, e17527
    Appears in Collections:[運動管理系] 期刊論文

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