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- 作者： kaiwu
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听了得到app，吴军.科技科技史纲60讲 23 | 牛顿“牛”在哪？
https://www.dedao.cn/course/article?id=WqavDm012GolV7pa0JxPjEy8zdk73Q
这里我们先说说牛顿在科学上的主要贡献，我将它们总结为这样几方面：
- 在数学上发现二项式定理，与莱布尼茨分别独立发明微积分；
- 在物理学上，奠定了经典力学的基础，定义了许多物理量，提出了力学三定律和万有引力定律；
- 在光学上，提出了光的粒子说，发现了光谱，发明了牛顿望远镜；
- 在天文学上，利用经典力学和微积分，构建了当时最准确的天体运动模型；
- 在化学上，通过对炼金术的研究，提出了原子论的原型，以及朴素的物质不灭定律的构想。
当然，牛顿最了不起的不在于发现了那么多的知识点，而在于构建起很多庞大的学科体系。我们在介绍牛津大学第一任校长格罗斯泰斯特的贡献时讲过，这位杰出的教育家对大学贡献的关键在于他让高等教育变得具有系统性了。因此，能够建立起系统性学科的人当然非常牛。
在物理学上，牛顿之前的人类虽然也掌握了很多力学和光学知识，但都是零星的知识点，经验的总结，不成体系，有些结论甚至和非科学的没有什么区别，是牛顿完成了它们科学化的过程。建立一个学科体系，首要的任务则是定义清楚各种基本的概念。
在牛顿之前，那些最基本的物理学概念，包括质量和力，都没有清晰的定义，甚至是相混淆的，比如人们搞不清楚力、惯性和动能的区别，质量和重量的区别，速度和加速度的区别。这些概念可能你在中学时也区分了很久才搞清楚，更不要说几百年前的人了。
牛顿定义了经典物理学中的这些最基本的概念，然后在此基础上，才提出了力学三定律，进而搭建起整个物理学的大厦。牛顿的工作重现了当年欧几里得构建公理化几何学的过程，再次向世人展示了构建一个学科体系的方法。在牛顿之后，各门自然科学都开始从知识点向体系化发展了。
在牛顿之前，几乎所有的科学发现都需要先观察到现象，才能发现规律，在牛顿之后，很多发现则是先通过理论的推导，预测可能观察到的结果，然后再通过实验证实。
牛顿的贡献还不止于此，他在思想领域最大的成就是将数学、物理学和天文学三个原本孤立的知识体系，通过物质的机械运动统一起来。因此，牛顿和当时其他科学家们一起，确立了一种新的世界观，就是机械论。
诗人亚历山大·波普在拜谒牛顿墓时写下了这样一句著名的诗句，“自然和自然律隐没在黑暗中；神说，让牛顿去吧！万物遂成光明”。这其实就反映出人类在牛顿之前和之后对世界态度的变化，在那之后，人类不再觉得自己身处不可知的黑暗了。
从历史的必然性来看，牛顿的出现不是偶然的，与他同时代的英国出现了一大批顶级科学家，包括胡克（弹性定律的提出者）、哈雷、波义耳和惠更斯（生活在英国的荷兰人，牛顿选定的继承人）等人。哈雷和胡克等人其实也注意到了行星围绕太阳运动需要一种向心力，即来自太阳的引力，只是这些人没有能力完成理论的建立罢了。不过，如果没有牛顿，可能用不了多久，也会有科学家发现万有引力定律。事实上，哈雷参与了牛顿《原理》一书的出版，并且是该书第一版的出资人。这些事实说明了科技发展的必然性。
从历史的偶然性来看，牛顿非常幸运，用法国大数学家拉格朗日的话讲，“牛顿是那么地幸运，因为发现并建立一个宇宙系统的机会只能有一次”。因此牛顿可以讲是生逢其时。在牛顿之后，世界上还有很多伟大的科学家出现，但是以一己之力构建多门学科大厦的机会不会再有了。
以下是本站补充的图片及网址连接。
艾萨克·牛顿（Isaac Newton）
https://baike.baidu.com/item/%E8%89%BE%E8%90%A8%E5%85%8B%C2%B7%E7%89%9B%E9%A1%BF
https://plato.stanford.edu/entries/newton/
https://www.britannica.com/summary/Isaac-Newton
https://www.history.com/topics/inventions/isaac-newton
https://thefactfile.org/isaac-newton-facts/
图片来自https://bkimg.cdn.bcebos.com/pic/e61190ef76c6a7ef52c37f61f3faaf51f2de6694
https://www.ideaedu.org/idea-notes-on-learning/gaining-a-basic-understanding-of-the-subject/
The foundations of any discipline are its definition, knowledge base, terminology, structure, methodology, and epistemology. As we move from basic knowledge to the complex organization and hierarchies of information in the disciplines, we parallel the levels of Bloom’s cognitive taxonomy (1): knowledge, comprehension, application, analysis, synthesis, and evaluation.
- Frohlich, M. (2001). Spiritual Discipline, Discipline of Spirituality: Revisiting Questions of Definition and Method. Spiritus: A Journal of Christian Spirituality, 1(1), 65–78.
- Heggart, K. (2016, May 1). How Important is Subject Matter Knowledge for a Teacher? Edutopia. https://www.edutopia.org/discussion/how-important-subject-matter-knowledge-teacher
- Schilt, C. J. (2020). Created in Our Image: How Isaac Newton Was Fashioned as a Scientist and Forgotten as a Scholar. History of Humanities, 5(1), 75–95. https://doi.org/10.1086/707693
- Theall, M., Wager, W., & Svinicki, M. (2016, October 18). Gaining A Basic Understanding of the Subject | IDEA. https://www.ideaedu.org/idea/idea-notes-on-learning/gaining-a-basic-understanding-of-the-subject/
听了得到app，吴军.科技科技史纲60讲 24 | 现代化学：如何从炼金术演化而来
https://www.dedao.cn/course/article?id=89GEyP73eprvKB0LMJq2Mb0kRD64dl
最早从炼金术士转变为化学家的，要算德国商人波兰特了。1669年，他试图从人体的尿液中提取出黄金，这可能因为它们都是黄色的缘故，于是抱着发财的目的，用尿液做了大量实验，结果意外地发现了白磷。其他的炼金术士们听到这个消息后百般打探消息，但是波兰特的保密工作做得很好，在接下来的好几年里大家对提炼磷的细节过程毫无知晓。要知道，当时很多炼金师们为了保护自己的配方，甚至会用密码来书写。
后来德国科学家孔克尔多方打听，探知这种发磷光的物质是从尿液中提取出来的，于是他也开始做类似的实验，并且在1678年成功地提取出白磷。几乎同时，英国的科学家波义耳也用相近的方法制作出了磷。
这个提取磷的方法就传播开来了，后来，波义耳的学生通过制作白磷发了财。磷的发现，标志着从炼金术到化学的第一个转折，因为不同的人用类似的方法得到了同样的结果，从此让一个新物质的发明过程变得可以验证，这一点很重要。
在从炼金术到化学过渡的这个过程中，起了最大作用的是著名科学家安托万·拉瓦锡，他在化学界的地位堪比牛顿在物理学的地位。拉瓦锡是法国末代王朝的贵族，从来不缺钱，他做化学实验只是为了探索自然的奥秘，而不是为了赚钱。拉瓦锡一生的贡献很多，最大的贡献是以下四个：
1.确认氧是一种化学元素（oxygen一词就是拉瓦锡创造的），并且提出了氧气助燃的学说；
2.证实并确立了质量守恒定律；
3.联合其他科学家制定了今天使用的化学物质的命名法；
4.制定今天广泛使用的公制度量衡。这里面任何一项都足以让人名垂青史，相比结果，他取得每一项成就的过程，对科学的发展都更为重要。
在拉瓦锡之前，学术界普遍认为一些物质能够燃烧，是因为其中具有所谓的“燃素”，燃烧的过程就是物质释放燃素的过程。但是人们还发现，给炉子鼓风火就能烧得更旺，把油灯的罩子盖严灯就会灭，当时的人们感觉没空气不行，但并不知道是空气中的氧气在助燃。
最早发现氧气能够助燃的其实是英国科学家普利斯特里。1774年，他在加热氧化汞时，得到一种气体，这种气体不仅能使火焰燃烧得更明亮，还能帮助呼吸。遗憾的是，燃素学说在普利斯特里脑子里根深蒂固，因此他没有得出有用的结论。后来普利斯特里到了法国，向拉瓦锡介绍了自己的实验，拉瓦锡重复了他的实验，得到了相同的结果。
但是拉瓦锡不相信燃素说的解释，因为他通过定量分析和逻辑推理发现了燃素说的逻辑破绽：如果燃烧是因为物质中的燃素造成的，那么燃烧之后，灰烬的质量应该减少，而事实上，燃烧的生成物的质量是增加的，这说明一定有新的东西加入到了燃烧的产物中。
拉瓦锡在实验中有一个信条：“必须用天平进行精确测定来确定真理。”正是依靠严格测量反应物前后的质量，他才确认了在燃烧的过程中，空气中的一种气体加入了进来，而不是所谓燃素分解掉了。此外这件事再次说明，逻辑推理对于科研很重要。
拉瓦锡所有的研究工作，都遵循一种科学的方法，这种方法由笛卡尔概括成四个步骤：
1理性批判：不接受任何自己不清楚的真理。对一个命题要根据自己的判断，确定有无可疑之处，任何有可疑之处的命题都不会是真理。
2化繁为简，化整为零：对于复杂的问题，尽量分解为多个简单的小问题来研究，一个一个地分开解决。
3先易后难：在解决上述小问题时，应该按照先易后难的次序，逐步解决。
4归纳综合：解决每个小问题之后，再综合起来。看看是否彻底解决了原来的问题。
安托万-洛朗·拉瓦锡（Antoine-Laurent de Lavoisier）
https://www.britannica.com/biography/Antoine-Lavoisier
https://www.sciencehistory.org/historical-profile/antoine-laurent-lavoisier
https://www.acs.org/education/whatischemistry/landmarks/lavoisier.html
https://id.wikipedia.org/wiki/Antoine_Lavoisier
- 文章信息
- 作者： kaiwu
- 点击数：146
官方网站：https://www.r-project.org/
官网下载：https://cran.r-project.org/
Rtools： https://cran.r-project.org/bin/windows/Rtools/
中国镜像下载
| https://mirrors.tuna.tsinghua.edu.cn/CRAN/ | TUNA Team, Tsinghua University |
| https://mirrors.bfsu.edu.cn/CRAN/ | Beijing Foreign Studies University |
| https://mirrors.pku.edu.cn/CRAN/ | Peking University |
| https://mirrors.ustc.edu.cn/CRAN/ | University of Science and Technology of China |
| https://mirrors.zju.edu.cn/CRAN/ | Zhejiang University |
| https://mirror-hk.koddos.net/CRAN/ | KoDDoS in Hong Kong |
| https://mirrors.e-ducation.cn/CRAN/ | Elite Education |
| https://mirrors.qlu.edu.cn/CRAN/ | Qilu University of Technology |
| https://mirror.lzu.edu.cn/CRAN/ | Lanzhou University Open Source Society |
| https://mirrors.nju.edu.cn/CRAN/ | eScience Center, Nanjing University |
| https://mirrors.sjtug.sjtu.edu.cn/cran/ | Shanghai Jiao Tong University |
| https://mirrors.sustech.edu.cn/CRAN/ |
R官方的说法，从R4.2.0以后不在支持32位系统
Since R 4.2.0, 32-bit builds are no longer provided.
所以如果是windows 32位系统，只能安装R 4.1.3或更低的版本
R 4.1.3 windows版官网网址
https://cran.r-project.org/bin/windows/base/old/4.1.3/R-4.1.3-win.exe
R 4.1.3 windows版官网网址（兰州大学镜像）
https://mirror.lzu.edu.cn/CRAN/bin/windows/base/old/4.1.3/R-4.1.3-win.exe
R 4.1.3 windows版官网网址（中国科技大学镜像）
https://mirrors.ustc.edu.cn/CRAN/bin/windows/base/old/4.1.3/R-4.1.3-win.exe
IDE1 ：Rstudio
https://docs.posit.co/ide/news/
https://posit.co/download/rstudio-desktop/
https://www.rstudio.org/download/daily/desktop/windows/
1.1目前Rstudio支持window10、windows11，并且是64位系统，不支持32位系统
所以，如果是window10、windows11，并且是64位系统：
官方网站： https://posit.co/download/rstudio-desktop/
1.2如果是windows 7，如果是32为系统，推荐Rstudio 1.1.463或更早的版本
（1）官网下载Rstudio 1.1.463
https://www.rstudio.org/download/daily/desktop/windows/
https://s3.amazonaws.com/rstudio-dailybuilds/RStudio-1.1.463.exe
（2）国内网盘
链接：https://pan.baidu.com/s/1M9nUTCkDkPMZL9idqUjIeQ
提取码：t6jy
Rstudio与windows版本问题的参考网址：
https://community.rstudio.com/t/does-rstudio-support-windows-7-32-bit/28534
https://blog.csdn.net/weixin_36275231/article/details/113539204
https://bbs.pinggu.org/thread-10720270-1-1.html
- 文章信息
- 作者： kaiwu
- 点击数：251
1.1参考资料
英文官方参考资料 https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html
R markdown文件 https://bibliometrix.org/documents/bibliometrix_Report.zip
1.2 安装、调用文献计量分析拓展功能包 bibliometrix
Install and load bibliometrix R-package
# # 安装方法1：通过CRAN安装稳定版
# Stable version from CRAN (Comprehensive R Archive Network)
# 如果想执行下一行代码，删除 # 即可
# install.packages("bibliometrix")
# 安装方法2：通过github安装最新版
# Most updated version from GitHub
# 如果想执行下2行代码，删除 # 即可
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
# Installation of some useful packages
# 最为稳健的方法，检测是否安装，没有安装就安装
if(!isTRUE(require("bibliometrix"))){install.packages("bibliometrix")}
# 调用bibliometrix拓展功能包
library(bibliometrix)
Loading required package: bibliometrix
Please note that our software is open source and available for use, distributed under the MIT license.
When it is used in a publication, we ask that authors properly cite the following reference:
Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
Journal of Informetrics, 11(4), pp 959-975, Elsevier.
Failure to properly cite the software is considered a violation of the license.
For information and bug reports:
- Take a look at https://www.bibliometrix.org
- Send an email to info@bibliometrix.org
- Write a post on https://github.com/massimoaria/bibliometrix/issues
Help us to keep Bibliometrix and Biblioshiny free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
To start with the Biblioshiny app, please digit:
biblioshiny()
1.3 设定文件存储目录
#dataforder是文件目录，同学可以自行修改你的文件目录，注意两点
#（1）不能用汉字，必须是英文字母和数字的组合
# (2) window系统，目录用\，而这里用的跟苹果系统、linux系统一致的/，需要修改一下
# 对于一个行数很多的程序，用起来很方便。
datafolder="D:/tdata/"
1.4 文献数据检索Bibliographic Collection
web of science数据库： 关于该数据库及创始人，参见http://kaiwu.city/index.php/histcite
web of science数据库官方网址（购买了授权，校园网可以访问） http://apps.webofknowledge.com/
699 results from Social Sciences Citation Index (SSCI): big data (topic) and tourism or tourist or hospitality or hotel (topic) Refined By: Document Types: Articles or Review Articles. Languages: English.
1.5导入文献计量分析数据Data Loading and Converting
first file 1-500
# 导入bib格式的文献数据Loading txt or bib files into R environment
# 方法1：如果已经下载了文献数据，可以使用本地文件
D1<- paste0(datafolder,"wos_bigdata1.bib")
# 方法2：直接使用网盘分享连接，下载并导入
# 网盘链接1：国外网盘opendrive.com
# D1 <-"https://od.lk/s/172672726_f1pd3/wos_bigdata1.bib"
# 网盘链接2：国内网盘https://www.ctfile.com/
# D1 <-"https://drfs.ctcontents.com/file/20727931/557190835/131a5c/opendata/wos_bigdata1.bib"
# bib格式数据转换为R分析用数据格式
# Converting the loaded files into a R bibliographic dataframe
M1 <- convert2df(file = D1, dbsource = "isi", format = "bibtex")
Converting your isi collection into a bibliographic dataframe Done! Generating affiliation field tag AU_UN from C1: Done!
second file 501-699
# Loading txt or bib files into R environment
D2 <- paste0(datafolder,"wos_bigdata2.bib")
# D2<-"https://od.lk/s/172672727_ACXwn/wos_bigdata2.bib"
# D2<-"https://drfs.ctcontents.com/file/20727931/557190837/f586ff/opendata/wos_bigdata2.bib"
# Converting the loaded files into a R bibliographic dataframe
M2 <- convert2df(file = D2, dbsource = "isi", format = "bibtex")
Converting your isi collection into a bibliographic dataframe Done! Generating affiliation field tag AU_UN from C1: Done!
# 合并导入的两个文件M1和M2
# 因为列结构相同（47个变量），只是行数的合并，所以用rbind，r表示row
# 如果行数是一样的，只是增加变量，那就是列合并，cbind，c表示column
# 如果是数据库的不同表格合并，那你要用merger，join，要设置合并的主键
M699<-rbind(M1,M2)
1.6 保存数据
# 将处理好的数据文件保存，以便未来使用——不用每次都重新导入原始数据
# csv是用英文逗号(,)作为分隔符号的数据格式，可以直接用excel打开，几乎所有的数据分析软件都支持csv格式数据
# rda是r特有的数据格式
write.csv(M699, file = paste0(datafolder,'M699.csv'))
save(M699,file=paste0(datafolder,'M699.rda'))
#save(M699,file='D:/tdata/M699.rda')
清理缓存clear the memory
1.7 剔除两条重复记录
3.1 部分 NetMatrix699 <- biblioNetwork(M699, analysis = "co-citation", network = "references", sep = ";")
出错，错误信息如下
Warning message: "non-unique value when setting 'row.names': 'QIU Q, 2021, ISPRS INT J GEO-INF'" Error in .rowNamesDF<-(x, value = value): duplicate 'row.names' are not allowed Traceback:
- biblioNetwork(M699tag, analysis = "co-citation", network = "sources",
- . sep = ";")
- cocMatrix(M, Field = "CR_SO", type = "sparse", n, sep, short = short)
row.names<-(*tmp*, value = M$SR)row.names<-.data.frame(*tmp*, value = M$SR).rowNamesDF<-(x, value = value)- stop("duplicate 'row.names' are not allowed")
#剔除重复记录
M699 <- subset(M699,SR !='QIU Q, 2021, ISPRS INT J GEO-INF')
1.8 再次保存数据
# 将处理好的数据文件保存，以便未来使用——不用每次都重新导入原始数据
# csv是用英文逗号(,)作为分隔符号的数据格式，可以直接用excel打开，几乎所有的数据分析软件都支持csv格式数据
# rda是r特有的数据格式
write.csv(M699, file = paste0(datafolder,'M699.csv'))
save(M699,file=paste0(datafolder,'M699.rda'))
#save(M699,file='D:/tdata/M699.rda')
rm(list=ls())
2.描述性统计分析
以后的数据分析，可以直接从2.开始，需要再次设定一下
# 调用bibliometrix拓展功能包
library(bibliometrix)
# 设定工作文件夹
datafolder="D:/tdata/"
导入数据
load(file=paste0(datafolder,'M699.rda'))
# load(file=url("https://od.lk/s/172672725_Ma5xJ/M699.rda"))
# load(file=url("https://drfs.ctcontents.com/file/20727931/557191120/aaffe1/opendata/M699.rda"))
文献计量学（bibliometrics）主要以量化科学产出和评估科研成果影响而闻名，也可用于展示研究的动态演变。通过这种方式，文献计量学旨在描述特定学科、或研究领域的结构以及它们如何随时间演变。换言之，文献计量方法有助于绘制科学图谱（science mapping），并且在综合多个研究方面意义较大。文献计量学是建立在一套统计方法之上的一个学科，可用于定量分析科学大数据及其随时间的演变并发现信息。网络结构通常用于对作者、论文/文档/文章、参考文献、关键字等之间的交互进行建模。
Bibliometrix 是一个开源软件，用于自动化数据分析和数据可视化。在 R中转换和上传文献数据后，Bibliometrix 执行描述性分析和不同的研究结构分析。描述性分析提供了一些关于年度研究发展、前“k”位高产作者、论文、国家和最相关关键词的快照。
2.1 主要的研究发现
#options(width=160)
results699 <- biblioAnalysis(M699)
summary(results699, k=10, pause=F, width=130)
MAIN INFORMATION ABOUT DATA
Timespan 2004 : 2022
Sources (Journals, Books, etc) 196
Documents 697
Annual Growth Rate % 18.11
Document Average Age 3.96
Average citations per doc 20.26
Average citations per year per doc 3.63
References 32900
DOCUMENT TYPES
article 573
article; early access 42
article; proceedings paper 3
review 75
review; early access 4
DOCUMENT CONTENTS
Keywords Plus (ID) 1426
Author's Keywords (DE) 2490
AUTHORS
Authors 1671
Author Appearances 2280
Authors of single-authored docs 55
AUTHORS COLLABORATION
Single-authored docs 64
Documents per Author 0.417
Co-Authors per Doc 3.27
International co-authorships % 37.16
Annual Scientific Production
Year Articles
2004 1
2005 1
2007 2
2008 2
2009 1
2010 3
2011 5
2012 2
2013 4
2014 12
2015 19
2016 19
2017 44
2018 74
2019 116
2020 144
2021 182
2022 20
Annual Percentage Growth Rate 18.11
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 LAW R 18 MARIANI M 6.42
2 LIU Y 14 LAW R 4.88
3 MARIANI M 12 XU X 4.08
4 NILASHI M 12 MARIANI MM 4.00
5 LI H 11 OENDER I 3.83
6 MARIANI MM 10 BORGHI M 3.50
7 SAMAD S 10 LIU Y 3.40
8 WANG S 9 MARINE-ROIG E 3.33
9 ZHANG Y 9 JIA SS 3.00
10 BORGHI M 8 PARK E 2.92
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 GRETZEL U, 2015, ELECTRON MARK 10.1007/s12525-015-0196-8 490 54.4 4.99
2 XIANG Z, 2015, INT J HOSP MANAG 10.1016/j.ijhm.2014.10.013 394 43.8 4.01
3 WOOD SA, 2013, SCI REP 10.1038/srep02976 339 30.8 3.01
4 GUO Y, 2017, TOURISM MANAGE 10.1016/j.tourman.2016.09.009 317 45.3 5.58
5 XIANG Z, 2017, TOURISM MANAGE 10.1016/j.tourman.2016.10.001 307 43.9 5.40
6 LI J, 2018, TOURISM MANAGE 10.1016/j.tourman.2018.03.009 246 41.0 8.51
7 BUHALIS D, 2015, J DESTIN MARK MANAG 10.1016/j.jdmm.2015.04.001 191 21.2 1.94
8 CARLOS GARCIA-PALOMARES J, 2015, APPL GEOGR 10.1016/j.apgeog.2015.08.002 180 20.0 1.83
9 CHENG M, 2019, INT J HOSP MANAG 10.1016/j.ijhm.2018.04.004 168 33.6 6.99
10 MARINE-ROIG E, 2015, J DESTIN MARK MANAG 10.1016/j.jdmm.2015.06.004 167 18.6 1.70
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 CHINA 197 0.2835 137 60 0.3046
2 USA 102 0.1468 65 37 0.3627
3 UNITED KINGDOM 59 0.0849 24 35 0.5932
4 SPAIN 52 0.0748 40 12 0.2308
5 KOREA 40 0.0576 27 13 0.3250
6 ITALY 36 0.0518 26 10 0.2778
7 AUSTRALIA 22 0.0317 6 16 0.7273
8 PORTUGAL 16 0.0230 12 4 0.2500
9 INDIA 14 0.0201 13 1 0.0714
10 AUSTRIA 11 0.0158 10 1 0.0909
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 USA 3533 34.64
2 CHINA 2497 12.68
3 UNITED KINGDOM 1771 30.02
4 KOREA 1006 25.15
5 SPAIN 975 18.75
6 ITALY 789 21.92
7 AUSTRALIA 613 27.86
8 FINLAND 275 68.75
9 AUSTRIA 265 24.09
10 GERMANY 261 23.73
Most Relevant Sources
Sources Articles
1 SUSTAINABILITY 65
2 TOURISM MANAGEMENT 54
3 INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 48
4 INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT 38
5 CURRENT ISSUES IN TOURISM 27
6 TOURISM ECONOMICS 18
7 TOURISM REVIEW 18
8 JOURNAL OF DESTINATION MARKETING \\& MANAGEMENT 17
9 JOURNAL OF TRAVEL RESEARCH 17
10 JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY 16
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 BIG DATA 184 BIG DATA 211
2 TOURISM 62 TOURISM 119
3 SOCIAL MEDIA 49 HOSPITALITY 100
4 ONLINE REVIEWS 41 SOCIAL MEDIA 94
5 BIG DATA ANALYTICS 38 IMPACT 93
6 SENTIMENT ANALYSIS 35 MANAGEMENT 75
7 MACHINE LEARNING 32 MODEL 71
8 TEXT MINING 30 SATISFACTION 69
9 HOSPITALITY 26 WORD-OF-MOUTH 67
10 COVID-19 24 ANALYTICS 56
plot(x=results699, k=10, pause=F)
2.2 引用最多的参考文献 Most Cited References
CR699 <- citations(M699, field = "article", sep = ";")
# 这里列出了被引用次数最高的，前20的参考文献
# 注意doi，有了doi，可以快速导入zotero等文献管理软件，完整的文献信息
cbind(CR699$Cited[1:20])
| XIANG Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/J.IJHM.2014.10.013 | 123 |
|---|---|
| LI JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/J.TOURMAN.2018.03.009 | 116 |
| XIANG Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/J.TOURMAN.2016.10.001 | 70 |
| GUO Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/J.TOURMAN.2016.09.009 | 67 |
| MARIANI M, 2018, INT J CONTEMP HOSP M, V30, P3514, DOI 10.1108/IJCHM-07-2017-0461 | 54 |
| LIU Y, 2017, TOURISM MANAGE, V59, P554, DOI 10.1016/J.TOURMAN.2016.08.012 | 52 |
| XIANG Z, 2010, TOURISM MANAGE, V31, P179, DOI 10.1016/J.TOURMAN.2009.02.016 | 52 |
| FUCHS M, 2014, J DESTIN MARK MANAGE, V3, P198, DOI 10.1016/J.JDMM.2014.08.002 | 50 |
| MIAH SJ, 2017, INFORM MANAGE-AMSTER, V54, P771, DOI 10.1016/J.IM.2016.11.011 | 47 |
| VU HQ, 2015, TOURISM MANAGE, V46, P222, DOI 10.1016/J.TOURMAN.2014.07.003 | 47 |
| LEUNG D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919 | 45 |
| YANG X, 2015, TOURISM MANAGE, V46, P386, DOI 10.1016/J.TOURMAN.2014.07.019 | 44 |
| ZHAO YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/J.IJHM.2018.03.017 | 44 |
| BUHALIS D, 2008, TOURISM MANAGE, V29, P609, DOI 10.1016/J.TOURMAN.2008.01.005 | 42 |
| LI X, 2017, TOURISM MANAGE, V59, P57, DOI 10.1016/J.TOURMAN.2016.07.005 | 42 |
| XU X, 2016, INT J HOSP MANAG, V55, P57, DOI 10.1016/J.IJHM.2016.03.003 | 42 |
| YE Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/J.IJHM.2008.06.011 | 42 |
| BANGWAYO-SKEETE PF, 2015, TOURISM MANAGE, V46, P454, DOI 10.1016/J.TOURMAN.2014.07.014 | 39 |
| CANTALLOPS AS, 2014, INT J HOSP MANAG, V36, P41, DOI 10.1016/J.IJHM.2013.08.007 | 39 |
| CHUA A, 2016, TOURISM MANAGE, V57, P295, DOI 10.1016/J.TOURMAN.2016.06.013 | 39 |
3. 共被引用分析 Co-citation Analysis
引文分析（Citation analysis）是文献计量学（bibliometrics）的经典技术之一。它通过节点（例如作者、论文、期刊）之间的联系显示特定领域的结构，而边缘可以根据网络类型进行不同的解释，即共同被引、直接引用、书目耦合。请参阅 Aria，Cuccurullo (2017)。 Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
下面是三个例子。
首先，被引参考文献（节点）之间关系的共引网络（co-citation networks）。
其次，使用被引期刊作为分析单位的共引网络。
评论共引网络的有用维度是：（i）节点的中心性和外围性 centrality and peripherality of nodes，（ii）它们的接近度和距离their proximity and distance，（iii）联系强度strength of ties，（iv）聚类clusters，（iiv）展示研究贡献之间的关系bridging contributions。
第三，引文编年图（historiograph）是建立在直接引用的基础上的，它按照历史顺序绘制了研究成果间的联系，足以重建该领域的发展历程，呈现该领域的里程碑式的重要研究。
3.1 被引用文章共被引分析 Article (References) co-citation analysis
Plot options:
-
n = 50 (the funxtion plots the main 50 cited references)
-
type = "fruchterman" (the network layout is generated using the Fruchterman-Reingold Algorithm)
-
size.cex = TRUE (the size of the vertices is proportional to their degree)
-
size = 20 (the max size of vertices)
-
remove.multiple=FALSE (multiple edges are not removed)
-
labelsize = 0.7 (defines the size of vertex labels)
-
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
-
edges.min = 5 (plots only edges with a strength greater than or equal to 5)
-
all other arguments assume the default values
# 这个部分需要57.93045秒
# 下面两行注释语句，用于保存图片为png格式文件，感兴趣同学自己研究一下
NetMatrix699 <- biblioNetwork(M699, analysis = "co-citation", network = "references", sep = ";")
#png(paste(datafolder,'net699n50.png',sep=""),width = 1024, height = 1024,units = "px",pointsize = 12, bg = "white", res = NA, restoreConsole = TRUE)
net699n50=networkPlot(NetMatrix699, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)
#dev.off()
Descriptive analysis of Article co-citation network characteristics
#netstat699 <- networkStat(NetMatrix699)
#summary(netstat699,k=10)
# 这个部分太慢，我注释掉了
3.2 期刊共被引分析 Journal (Source) co-citation analysis
M699tag=metaTagExtraction(M699,"CR_SO",sep=";")
NetMatrix699tag <- biblioNetwork(M699tag, analysis = "co-citation", network = "sources", sep = ";")
net699n50tag=networkPlot(NetMatrix699tag, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)
期刊共被引分析结果的描述性统计分析
Descriptive analysis of Journal co-citation network characteristics
netstat699tag <- networkStat(NetMatrix699tag)
summary(netstat699tag,k=10)
Main statistics about the network Size 11891 Density 0.01 Transitivity 0.135 Diameter 4 Degree Centralization 0.773 Average path length 2.147
4.引文编年图Historiograph
4.1 构建编年图网络
# 下面两行注释语句，用于保存图片为png格式文件，感兴趣同学自己研究一下
histresults699 <- histNetwork(M699, min.citations=quantile(M699$TC,0.75), sep = ";")
WOS DB: Searching local citations (LCS) by reference items (SR) and DOIs... Analyzing 47541 reference items... Found 174 documents with no empty Local Citations (LCS)
4.2 编年图网络可视化
#options(width = 1024)
#png(paste(datafolder,'histresults699.png',sep=""),width = 1200, height = 800,units = "px",pointsize = 15, bg = "white", res = NA, restoreConsole = TRUE)
#net699hist <- histPlot(histresults699, n=20, size = 5, labelsize = 5)
#dev.off()
#net699hist
histresults699 <- histNetwork(M699, min.citations=quantile(M699$TC,0.75), sep = ";")
net699hist <- histPlot(histresults699, n=20, size = 5, labelsize = 3)
WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...
Analyzing 47541 reference items...
Found 174 documents with no empty Local Citations (LCS)
Legend
Label
1 YANG Y, 2014, J TRAVEL RES DOI 10.1177/0047287513500391
2 XIANG Z, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.10.013
3 GRETZEL U, 2015, ELECTRON MARK DOI 10.1007/S12525-015-0196-8
4 SCHUCKERT M, 2015, INT J HOSP MANAG DOI 10.1016/J.IJHM.2014.12.007
5 PHILANDER K, 2016, INT J HOSP MANAG DOI 10.1016/J.IJHM.2016.02.001
6 MARIANI MM, 2016, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2015.12.008
7 PAN B, 2017, J TRAVEL RES DOI 10.1177/0047287516669050
8 XIANG Z, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.10.001
9 LI X, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.07.005
10 LIU Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.08.012
11 GUO Y, 2017, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2016.09.009
12 TALON-BALLESTERO P, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2018.03.017
13 MARIANI MM, 2018, TOURISM MANAGE DOI 10.1016/J.TOURMAN.2017.11.006
14 ALAEI AR, 2019, J TRAVEL RES DOI 10.1177/0047287517747753
15 MARIANI M, 2019, IEEE ACCESS DOI 10.1109/ACCESS.2018.2887300
16 AHANI A, 2019, INT J HOSP MANAG DOI 10.1016/J.IJHM.2019.01.003
17 MARIANI M, 2020, TOUR REV DOI 10.1108/TR-06-2019-0259
Author_Keywords
1 TOURISM DEMAND FORECASTING; TIME SERIES; ONLINE DATA; HOTEL OCCUPANCY;; BIG DATA; WEBSITE TRAFFIC
2 BIG DATA; TEXT ANALYTICS; GUEST EXPERIENCE; SATISFACTION; HOTEL; MANAGEMENT
3 SMART TOURISM; SMART TECHNOLOGY; SMART BUSINESS ECOSYSTEMS; BUSINESS; MODELS; OPEN INNOVATION; BIG DATA; INTERNET OF THINGS
4 ONLINE RATINGS; TRAVELER DISTRIBUTION; REPUTATION; SATISFACTION; DIFFERENCE; LANGUAGE
5 <NA>
6 SOCIAL MEDIA; FACEBOOK; BIG DATA; DESTINATION MANAGEMENT ORGANIZATIONS;; ENGAGEMENT; DESTINATION MARKETING; ITALIAN REGIONS
7 TOURISM DEMAND FORECASTING; BIG DATA; WEB TRAFFIC; TIME SERIES; MARKOV; SWITCHING DYNAMIC REGRESSION MODEL; SEARCH ENGINE QUERY
8 ONLINE REVIEWS; HOTEL INDUSTRY; INFORMATION QUALITY; SOCIAL MEDIA; ANALYTICS; TEXT ANALYTICS; MACHINE LEARNING
9 TOURISM DEMAND FORECAST; BIG DATA ANALYTICS; SEARCH QUERY DATA;; GENERALIZED DYNAMIC FACTOR MODEL; COMPOSITE SEARCH INDEX
10 BIG DATA; SATISFACTION; HOTEL; ONLINE REVIEWS; USER-GENERATED REVIEW;; TRIPADVISOR
11 ONLINE REVIEWS; VISITOR SATISFACTION; DATA MINING; LATENT DIRICHLET; ANALYSIS; PERCEPTUAL MAPPING
12 BIG DATA; HOSPITALITY INDUSTRY; CUSTOMER RELATIONSHIP MANAGEMENT; CLIENT; PROFILE; BOOTSTRAP RESAMPLING; HOTEL CHAINS
13 BOOKING.COM; ONLINE REVIEWS; LONDON BIG DATA; HOTEL RATINGS; HOTEL; CLASS; SKEWNESS
14 BIG DATA; SENTIMENT ANALYSIS; SOCIAL MEDIA; LEXICON; MACHINE LEARNING
15 BIG DATA; ONLINE REVIEWS; ONLINE RATING; EXPEDIA.COM; HOTEL; CULTURAL; DIFFERENCES
16 SPA HOTELS; MARKET SEGMENTATION; TRAVELLER PREFERENCE; ONLINE REVIEW;; SOCIAL BIG DATA; TRIPADVISOR; MACHINE LEARNING
17 BIG DATA; TOURISM; HOSPITALITY; BIG DATA ANALYTICS; PLATINUM JUBILEE
KeywordsPlus
1 MODELS; TESTS
2 ONLINE REVIEWS; INFORMATION; SELECTION; QUALITY
3 INTERNET; CITIES; INFORMATION; TECHNOLOGY; MANAGEMENT; INNOVATION;; NETWORKS; PROGRESS; THINGS; CHINA
4 NATIONAL CULTURE; SERVICE QUALITY; SATISFACTION; PERCEPTIONS;; TRIPADVISOR; IMPACT
5 USER-GENERATED CONTENT; WORD-OF-MOUTH; ONLINE HOTEL REVIEWS; SOCIAL; MEDIA; BIG DATA; TOURISM; CONSUMERS; HOSPITALITY; IMPACT; INTENTIONS
6 SOCIAL MEDIA; INFORMATION-SOURCES; ONLINE TRAVEL; TOURISM; TECHNOLOGY;; IMAGE; INTERNET
7 TOURISM DEMAND; TIME-SERIES; ARRIVALS; DETERMINANTS; INDUSTRY; CYCLE; UK
8 CUSTOMER REVIEWS; BIG DATA; SATISFACTION; TRIPADVISOR; PERFORMANCE;; READABILITY; EXPERIENCE; WEBSITE; HOTELS; SEARCH
9 DYNAMIC-FACTOR MODEL; BIG DATA; NEURAL-NETWORK; ARRIVALS; ANALYTICS;; ACCURACY; REVIEWS; US
10 WORD-OF-MOUTH; SERVICE QUALITY; PERCEIVED VALUE; BEHAVIOR; ADOPTION
11 WORD-OF-MOUTH; CUSTOMER SATISFACTION; SERVICE QUALITY; HOTEL REVIEWS;; USER REVIEWS; PRODUCT; PERCEPTIONS; IMPACT; SALES; CONSUMERS
12 REPEAT VISITORS; TOURIST MOTIVATIONS; REVENUE MANAGEMENT; DESTINATION; IMAGE; HOSPITALITY; 1ST-TIME; CRM; IMPLEMENTATION; SEGMENTATION;; PERFORMANCE
13 WORD-OF-MOUTH; SCORING SYSTEM; ONLINE; REVIEWS; SATISFACTION;; EXPERIENCE; PLATFORMS; ANALYTICS; TOURISM; SEARCH
14 ONLINE REVIEWS; SOCIAL MEDIA; TRAVEL; CLASSIFICATION; DESTINATIONS;; ANALYTICS
15 WORD-OF-MOUTH; QUALITY PERCEPTIONS; HOSPITALITY; CHALLENGES; PLATFORMS;; ANALYTICS; BEHAVIOR; DRIVERS; SCIENCE; EWOM
16 ARTIFICIAL NEURAL-NETWORKS; BIG DATA ANALYTICS; K-MEANS ALGORITHM;; QUALITY-OF-LIFE; SOCIAL MEDIA; CUSTOMER SATISFACTION; RECOMMENDER; SYSTEM; DESTINATION IMAGE; CONSUMER REVIEWS; SERVICE QUALITY
17 CUSTOMER SATISFACTION; DESTINATION; SERVICES; LESSONS
DOI Year LCS GCS
1 10.1177/0047287513500391 2014 38 131
2 10.1016/j.ijhm.2014.10.013 2015 123 394
3 10.1007/s12525-015-0196-8 2015 31 490
4 10.1016/j.ijhm.2014.12.007 2015 25 81
5 10.1016/j.ijhm.2016.02.001 2016 19 82
6 10.1016/j.tourman.2015.12.008 2016 21 153
7 10.1177/0047287516669050 2017 26 84
8 10.1016/j.tourman.2016.10.001 2017 70 307
9 10.1016/j.tourman.2016.07.005 2017 42 153
10 10.1016/j.tourman.2016.08.012 2017 52 117
11 10.1016/j.tourman.2016.09.009 2017 67 317
12 10.1016/j.tourman.2018.03.017 2018 18 49
13 10.1016/j.tourman.2017.11.006 2018 22 64
14 10.1177/0047287517747753 2019 38 150
15 10.1109/ACCESS.2018.2887300 2019 17 31
16 10.1016/j.ijhm.2019.01.003 2019 21 63
17 10.1108/TR-06-2019-0259 2020 17 41
5. 概念图结构：共词分析The conceptual structure - Co-Word Analysis
共词网络（Co-word networks）显示概念结构，通过术语共现（term co-occurences）揭示概念之间的联系。概念结构通常用于理解学者所涵盖的主题（研究前沿research front），并确定哪些是最重要和最近的问题。将整个时间跨度划分为不同的时间片（timeslices），并比较概念结构，有助于分析主题随时间的演变。
Bibliometrix 能够分析关键词，也可以分析文章标题和摘要中的术语。 它使用网络分析或对应分析（correspondance analysis，CA）或多重对应分析（multiple correspondance analysis，MCA）来完成。 CA 和 MCA 在二维坐标系中可视化概念结构。
5.1 共词分析：基于关键词的共同出现
Co-word Analysis through Keyword co-occurrences
Plot options:
-
normalize = "association" (the vertex similarities are normalized using association strength)
-
n = 50 (the function plots the main 50 cited references)
-
type = "fruchterman" (the network layout is generated using the Fruchterman-Reingold Algorithm)
-
size.cex = TRUE (the size of the vertices is proportional to their degree)
-
size = 20 (the max size of the vertices)
-
remove.multiple=FALSE (multiple edges are not removed)
-
labelsize = 3 (defines the max size of vertex labels)
-
label.cex = TRUE (The vertex label sizes are proportional to their degree)
-
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
-
label.n = 30 (Labels are plotted only for the main 30 vertices)
-
edges.min = 25 (plots only edges with a strength greater than or equal to 2)
-
all other arguments assume the default values
NetMatrix699keyword <- biblioNetwork(M699, analysis = "co-occurrences", network = "keywords", sep = ";")
net699keyword=networkPlot(NetMatrix699keyword, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=3,label.cex=TRUE,label.n=30,edges.min=2)
共词网络特征的描述性分析 Descriptive analysis of keyword co-occurrences network characteristics
netstat699keyword <- networkStat(NetMatrix699keyword)
summary(netstat699keyword,k=10)
Main statistics about the network Size 1426 Density 0.014 Transitivity 0.174 Diameter 5 Degree Centralization 0.42 Average path length 2.527
5.2 基于对应分析的共词分析
Co-word Analysis through Correspondence Analysis
CS699 <- conceptualStructure(M699, method="MCA", field="ID", minDegree=10, clust=5, stemming=FALSE, labelsize=8,documents=20)
Warning message: "ggrepel: 47 unlabeled data points (too many overlaps). Consider increasing max.overlaps" Warning message: "ggrepel: 42 unlabeled data points (too many overlaps). Consider increasing max.overlaps"