{"title":"揭开空间相关性的神秘面纱：解读局部空间自相关性的互动可视化方法","authors":"Lee Mason, Blanaid Hicks, Jonas Almeida","doi":"arxiv-2408.02418","DOIUrl":null,"url":null,"abstract":"The Local Moran's I statistic is a valuable tool for identifying localized\npatterns of spatial autocorrelation. Understanding these patterns is crucial in\nspatial analysis, but interpreting the statistic can be difficult. To simplify\nthis process, we introduce three novel visualizations that enhance the\ninterpretation of Local Moran's I results. These visualizations can be\ninteractively linked to one another, and to established visualizations, to\noffer a more holistic exploration of the results. We provide a JavaScript\nlibrary with implementations of these new visual elements, along with a web\ndashboard that demonstrates their integrated use.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"46 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation\",\"authors\":\"Lee Mason, Blanaid Hicks, Jonas Almeida\",\"doi\":\"arxiv-2408.02418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Local Moran's I statistic is a valuable tool for identifying localized\\npatterns of spatial autocorrelation. Understanding these patterns is crucial in\\nspatial analysis, but interpreting the statistic can be difficult. To simplify\\nthis process, we introduce three novel visualizations that enhance the\\ninterpretation of Local Moran's I results. These visualizations can be\\ninteractively linked to one another, and to established visualizations, to\\noffer a more holistic exploration of the results. We provide a JavaScript\\nlibrary with implementations of these new visual elements, along with a web\\ndashboard that demonstrates their integrated use.\",\"PeriodicalId\":501174,\"journal\":{\"name\":\"arXiv - CS - Graphics\",\"volume\":\"46 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Graphics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.02418\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.02418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
局部莫兰 I 统计量是识别局部空间自相关模式的重要工具。理解这些模式对空间分析至关重要，但解释统计量却很困难。为了简化这一过程，我们引入了三种新颖的可视化方法，以加强对局部莫兰 I 结果的解释。这些可视化效果可以相互交互链接，也可以与已有的可视化效果链接，从而对结果进行更全面的探索。我们提供了一个 JavaScript 库，其中包含这些新的可视化元素的实现方法，同时还提供了一个网络仪表板来演示它们的集成使用。
Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation
The Local Moran's I statistic is a valuable tool for identifying localized
patterns of spatial autocorrelation. Understanding these patterns is crucial in
spatial analysis, but interpreting the statistic can be difficult. To simplify
this process, we introduce three novel visualizations that enhance the
interpretation of Local Moran's I results. These visualizations can be
interactively linked to one another, and to established visualizations, to
offer a more holistic exploration of the results. We provide a JavaScript
library with implementations of these new visual elements, along with a web
dashboard that demonstrates their integrated use.
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