{"title":"2008-2020年美国伊利诺伊州心脏病死亡率时空格局可视化:一种交互式动画制图方法","authors":"Shunfu Hu, Hannah Kolker","doi":"10.1080/23754931.2023.2179892","DOIUrl":null,"url":null,"abstract":"Abstract The Illinois Department of Public Health has collected massive mortality data for the state from 2008 to 2020. Those data sets, however, are stored in numeric-text formats. Therefore, it is very difficult for the public and even health professionals to understand the spatial and temporal patterns of mortality across the state. In this article, the authors present the development of an interactive and animated cartographic approach to visualizing the spatiotemporal patterns of the death rates of heart disease in Illinois from 2008 to 2020. The interactive map was developed using the combination of ArcGIS Online and ArcGIS API for JavaScript. The map animation was created in Microsoft PowerPoint, was saved in MP4 video, and was uploaded to YouTube for public viewing. Both the interactive online map and map animation provide excellent tools for the visualization of spatiotemporal patterns of heart disease death rates in Illinois from 2008 to 2020. In addition, three sparkline graphs were generated in Microsoft Excel to reveal the temporal trend of the death rates: one for the state total, one for sex, and one for race ethnicity. It is expected that this approach can be used to visualize the spatiotemporal patterns of other public health information.","PeriodicalId":36897,"journal":{"name":"Papers in Applied Geography","volume":"35 1","pages":"125 - 135"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visualizing the Spatiotemporal Patterns of the Heart Disease Death Rates of Illinois, USA, 2008–2020: An Interactive and Animated Cartographic Approach\",\"authors\":\"Shunfu Hu, Hannah Kolker\",\"doi\":\"10.1080/23754931.2023.2179892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The Illinois Department of Public Health has collected massive mortality data for the state from 2008 to 2020. Those data sets, however, are stored in numeric-text formats. Therefore, it is very difficult for the public and even health professionals to understand the spatial and temporal patterns of mortality across the state. In this article, the authors present the development of an interactive and animated cartographic approach to visualizing the spatiotemporal patterns of the death rates of heart disease in Illinois from 2008 to 2020. The interactive map was developed using the combination of ArcGIS Online and ArcGIS API for JavaScript. The map animation was created in Microsoft PowerPoint, was saved in MP4 video, and was uploaded to YouTube for public viewing. Both the interactive online map and map animation provide excellent tools for the visualization of spatiotemporal patterns of heart disease death rates in Illinois from 2008 to 2020. In addition, three sparkline graphs were generated in Microsoft Excel to reveal the temporal trend of the death rates: one for the state total, one for sex, and one for race ethnicity. It is expected that this approach can be used to visualize the spatiotemporal patterns of other public health information.\",\"PeriodicalId\":36897,\"journal\":{\"name\":\"Papers in Applied Geography\",\"volume\":\"35 1\",\"pages\":\"125 - 135\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Papers in Applied Geography\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23754931.2023.2179892\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Papers in Applied Geography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23754931.2023.2179892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0
摘要
伊利诺伊州公共卫生部收集了该州2008年至2020年的大量死亡率数据。然而,这些数据集是以数字-文本格式存储的。因此,公众甚至卫生专业人员都很难了解全州死亡率的时空格局。在这篇文章中,作者介绍了一种交互式和动画制图方法的发展,以可视化伊利诺伊州2008年至2020年心脏病死亡率的时空模式。交互式地图是结合ArcGIS Online和ArcGIS API for JavaScript开发的。地图动画是在微软PowerPoint中制作的,保存为MP4视频,并上传到YouTube供公众观看。交互式在线地图和地图动画都为伊利诺伊州2008年至2020年心脏病死亡率的时空模式可视化提供了优秀的工具。此外,在Microsoft Excel中生成了三个火花图,以显示死亡率的时间趋势:一个是州总数,一个是性别,一个是种族和民族。预计这种方法可用于可视化其他公共卫生信息的时空格局。
Visualizing the Spatiotemporal Patterns of the Heart Disease Death Rates of Illinois, USA, 2008–2020: An Interactive and Animated Cartographic Approach
Abstract The Illinois Department of Public Health has collected massive mortality data for the state from 2008 to 2020. Those data sets, however, are stored in numeric-text formats. Therefore, it is very difficult for the public and even health professionals to understand the spatial and temporal patterns of mortality across the state. In this article, the authors present the development of an interactive and animated cartographic approach to visualizing the spatiotemporal patterns of the death rates of heart disease in Illinois from 2008 to 2020. The interactive map was developed using the combination of ArcGIS Online and ArcGIS API for JavaScript. The map animation was created in Microsoft PowerPoint, was saved in MP4 video, and was uploaded to YouTube for public viewing. Both the interactive online map and map animation provide excellent tools for the visualization of spatiotemporal patterns of heart disease death rates in Illinois from 2008 to 2020. In addition, three sparkline graphs were generated in Microsoft Excel to reveal the temporal trend of the death rates: one for the state total, one for sex, and one for race ethnicity. It is expected that this approach can be used to visualize the spatiotemporal patterns of other public health information.