An Extended K Function Method for Analyzing Distributions of Polygons with GIS

IF 3.3 3区 地球科学 Q1 GEOGRAPHY Geographical Analysis Pub Date : 2022-04-08 DOI:10.1111/gean.12326
Atsuyuki Okabe, Kayo Okabe
{"title":"An Extended K Function Method for Analyzing Distributions of Polygons with GIS","authors":"Atsuyuki Okabe,&nbsp;Kayo Okabe","doi":"10.1111/gean.12326","DOIUrl":null,"url":null,"abstract":"<p>The objective of this paper is to develop a <i>K</i> function method for analyzing distributions of polygon-like entities in the real world by extending Ripley’s <i>K</i> function method. Many empirical studies using the <i>K</i> function method assume that entities are represented by points. If entities are small enough in comparison with a study area, this approximation may be acceptable. If not, polygon-like entities may not be approximated by points. To deal with polygon-like entities, this paper develops a <i>K</i> function method for analyzing distributions of polygons. First, the paper shows a method for extending the local <i>K</i> function of points to that of polygons. Second, the paper compares the result obtained from the <i>K</i> function of polygons with that of the points representing the polygons and shows a distinctive difference. Third, the paper formulates the cross <i>K</i> function method of polygons to analyze the relationship between two distributions of polygons of different kinds. Fourth, the paper implements the methods in GIS. Last, the paper applies the cross <i>K</i> function method of polygons to actual distributions of buildings of different uses in Aoyama, Tokyo.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geographical Analysis","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/gean.12326","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0

Abstract

The objective of this paper is to develop a K function method for analyzing distributions of polygon-like entities in the real world by extending Ripley’s K function method. Many empirical studies using the K function method assume that entities are represented by points. If entities are small enough in comparison with a study area, this approximation may be acceptable. If not, polygon-like entities may not be approximated by points. To deal with polygon-like entities, this paper develops a K function method for analyzing distributions of polygons. First, the paper shows a method for extending the local K function of points to that of polygons. Second, the paper compares the result obtained from the K function of polygons with that of the points representing the polygons and shows a distinctive difference. Third, the paper formulates the cross K function method of polygons to analyze the relationship between two distributions of polygons of different kinds. Fourth, the paper implements the methods in GIS. Last, the paper applies the cross K function method of polygons to actual distributions of buildings of different uses in Aoyama, Tokyo.

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
用GIS分析多边形分布的扩展K函数方法
本文的目的是通过扩展Ripley的K函数方法,建立一种分析现实世界中类多边形实体分布的K函数方法。许多使用K函数方法的实证研究假设实体由点表示。如果实体与研究区域相比足够小,这种近似是可以接受的。如果没有,类多边形实体可能不会被点近似。为了处理类多边形实体,本文提出了一种分析多边形分布的K函数方法。首先,给出了一种将点的局部K函数推广到多边形的局部K函数的方法。其次,将多边形的K函数的结果与代表多边形的点的结果进行了比较,得出了明显的差异。第三,提出了多边形的交叉K函数方法,分析了不同种类多边形的两种分布之间的关系。第四,在GIS中实现了这些方法。最后,将多边形交叉K函数方法应用于东京青山不同用途建筑的实际分布。
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
CiteScore
8.70
自引率
5.60%
发文量
40
期刊介绍: First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.
期刊最新文献
Issue Information The Multiple Gradual Maximal Covering Location Problem Correction to “A hybrid approach for mass valuation of residential properties through geographic information systems and machine learning integration” Plausible Reasoning and Spatial‐Statistical Theory: A Critique of Recent Writings on “Spatial Confounding” The Regionalization and Aggregation of In‐App Location Data to Maximize Information and Minimize Data Disclosure
×
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:481959085
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