为基于gis的人口疾病发病率可视化选择数据聚类工具

IF 0.2 Q4 MEDICINE, GENERAL & INTERNAL
Roman V. Buzinov, Vladimir N. Fedorov, Aleksandr A. Kovshov, Yuliya A. Novikova, Nadezhda A. Tikhonova, Maxim S. Petrov, Ksenia V. Krutskaya
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引用次数: 0

摘要

目的:为基于gis的疾病发病率空间分布分析提供最佳聚类工具。材料和方法:我们使用了俄罗斯联邦北极地区组成实体阿尔汉格尔斯克州8个行政区人口中恶性肿瘤、慢性酒精中毒和哮喘发病率的原始数据。疾病发病率为2016年至2020年5年期间的平均值。我们评估了利用ArcMap地理信息系统工具可视化空间参考指标分布的方法。结果-研究产生了ArcMap中空间参考数据自动聚类结果的差异,这取决于单个样本分布的正态性和指标值的传播,这在最终的地图上直观地反映出来。样本中的参数值直接影响数据聚类的特征。因此,考虑这个问题对于确保正确选择适当的分析工具是很重要的。结论-我们的研究表明,在使用工具对空间参考发生率数据在ArcGIS中的可视化进行自动聚类时,有必要考虑直接影响其呈现准确性的因素。我们认为使用基于几何区间方法的聚类工具是最合适的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Choosing Data Clustering Tools For GIS-Based Visualization Of Disease Incidence In The Population
Objective — To substantiate the choice of optimal tools for clustering spatially referenced data on disease incidence for GIS-based analysis of their spatial distribution. Material and Methods — We used primary data on the incidence of malignant neoplasms, chronic alcoholism, and asthma in the population of eight administrative areas in Arkhangelsk Oblast as a constituent entity of the Arctic Zone of the Russian Federation. Disease incidence was averaged over a 5-year period from 2016 to 2020. We assessed the methods for visualizing the distribution of spatially referenced indicators using the ArcMap geoinformation system tools. Results — The study yielded differences in the outcomes of automated clustering of spatially referenced data in ArcMap, depending on the normality of the distribution in individual samples and the spread of indicator values, which was visually reflected on the resulting map. The parameter values in the samples directly affected the features of data clustering. Hence, this issue is important to consider for ensuring the correct choice of the appropriate analytical tool. Conclusion — Our study demonstrated that when using tools for automated clustering of spatially referenced incidence data in terms of their visualization in ArcGIS, it is necessary to consider the factors that directly affect the accuracy of their presentation. We consider it most appropriate to use a clustering tool based on the geometric interval method.
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来源期刊
Russian Open Medical Journal
Russian Open Medical Journal MEDICINE, GENERAL & INTERNAL-
CiteScore
0.90
自引率
0.00%
发文量
39
期刊介绍: Russian Open Medical Journal (RusOMJ) (ISSN 2304-3415) is an international peer reviewed open access e-journal. The website is updated quarterly with the RusOMJ’s latest original research, clinical studies, case reports, reviews, news, and comment articles. This Journal devoted to all field of medicine. All the RusOMJ’s articles are published in full on www.romj.org with open access and no limits on word counts. Our mission is to lead the debate on health and to engage, inform, and stimulate doctors, researchers, and other health professionals in ways that will improve outcomes for patients. The RusOMJ team is based mainly in Saratov (Russia), although we also have editors elsewhere in Russian and in other countries.
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