A spatial randomness test based on the box-counting dimension

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY Asta-Advances in Statistical Analysis Pub Date : 2022-01-05 DOI:10.1007/s10182-021-00434-4
Yolanda Caballero, Ramón Giraldo, Jorge Mateu
{"title":"A spatial randomness test based on the box-counting dimension","authors":"Yolanda Caballero,&nbsp;Ramón Giraldo,&nbsp;Jorge Mateu","doi":"10.1007/s10182-021-00434-4","DOIUrl":null,"url":null,"abstract":"<div><p>Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log–log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 3","pages":"499 - 524"},"PeriodicalIF":1.4000,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00434-4.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asta-Advances in Statistical Analysis","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10182-021-00434-4","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1

Abstract

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log–log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于盒计数维度的空间随机性检验
空间点模式的统计建模通常从检验空间随机性假设开始。经典测试基于样方计数和基于距离的方法。或者,我们提出了一种新的基于分形维数的空间随机性统计检验,通过盒计数法计算,提供了一种与该方法更经常描述性使用相反的推断视角。我们还开发了一个基于对数-对数图的图形测试来计算盒计数维度。我们通过进行模拟研究和分析COVID-19数据集来评估我们方法的性能。结果强化了该方法的良好性能,该方法作为更经典的基于距离的策略的替代方案而出现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Asta-Advances in Statistical Analysis
Asta-Advances in Statistical Analysis 数学-统计学与概率论
CiteScore
2.20
自引率
14.30%
发文量
39
审稿时长
>12 weeks
期刊介绍: AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.
期刊最新文献
A note on dynamic spatiotemporal ARCH models: small- and large-sample results Advances in spatial econometrics and geostatistics: methods, theory, and applications Fuzzy C-modes clustering with spatial regularization and noise cluster Editorial Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1