{"word_count": 1366, "character_count": 15842}
I always thought that there were some kind of schools in statistics, areas (not to say universities or laboratories) where people had common interest in term of statistical methodology. Like people with strong interest in extreme values, or in Lévy Processes. I wanted to check this point so I did extract information about articles puslished in about 35 journals in statistics, probability and econometrics. I got all the information in files extracted from http://scopus.com/&#62; setwd("/home/arthur/Documents/scopus/")&#62; L=list.files()&#62; z=NULL&#62; for(i in 1:length(L)){+ B=read.csv(L[i])+ z=c(z,as.character(B$Source.title))+ }Here is the list of the publications I have used&#62; Z=sort(table(z),decreasing=TRUE)&#62; Z[1:34]                                 Computational Statistics and Data Analysis                                                                        4000                                            Journal of Multivariate Analysis                                                                        4000                                                          Econometric Theory                                                                        2631                                               Annals of Applied Probability                                                                        2051                                                              Bioinformatics                                                                        2000                                                                  Biometrika                                                                        2000                                                     Journal of Econometrics                                                                        2000                               Journal of Statistical Planning and Inference                                                                        2000                             Journal of the American Statistical Association                                                                        2000                                                         Operations Research                                                                        2000                                                         Pattern Recognition                                                                        2000                                       Probability Theory and Related Fields                                                                        2000                                                           Signal Processing                                                                        2000                                              Journal of Applied Probability                                                                        1999                                 Stochastic Processes and their Applications                                                                        1999                          Annals of the Institute of Statistical Mathematics                                                                        1985                                                        Annals of Statistics                                                                        1797                                                               Technometrics                                                                        1446                                        Journal of Machine Learning Research                                                                        1441                                                               Biostatistics                                                                        1120                                          Statistics and Probability Letters                                                                        1062                                                       Annals of Probability                                                                        1054                                                    Statistics and Computing                                                                         927                                             Advances in Applied Probability                                                                         895                                         Journal of Nonparametric Statistics                                                                         836                                                    Computational Statistics                                                                         813                                             Journal of Time Series Analysis                                                                         811                           Journal of Computational and Graphical Statistics                                                                         802      Journal of the Royal Statistical Society. Series C: Applied Statistics                                                                         794 Journal of the Royal Statistical Society. Series B: Statistical Methodology                                                                         793                                                                  Biometrics                                                                         784                                                            Machine Learning                                                                         559                                                   SIAM Journal on Computing                                                                         433                                      International Journal of Biostatistics                                                                         368The first problem is that is it difficult to extract universities and locations of contributors. When you look at what we have in the dataset, here it is&#62; B$Authors.with.affiliations[1][1] Mischler, S., CEREMADE, UMR CNRS 7534, Universit\303\251 Paris-Dauphine, Place du Mar\303\251chal de Lattre de Tassigny, Paris Cedex 16, 75775, France; Mouhot, C., DPMMS,Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom; Wennberg, B., Department of Mathematical Sciences, Chalmers University of Technology, G\303\266teborg, Sweden, Department of Mathematical Sciences, University of Gothenburg, G\303\266teborg, 41296, SwedenThe first step was to split all that sentence, based on the comma operator&#62; setwd("/home/arthur/Documents/scopus/")&#62; L=list.files()&#62; v=NULL&#62; for(i in 1:length(L)){+ B=read.csv(L[i])+ A=B$Authors.with.affiliations+ for(j in 1:length(A)){+ x1=as.character(A[j])+ x2=strsplit(x1,",")+ v=c(v,x2[[1]])}+ }I have a long  long vector here. Which contains a lot of things !&#62; V=sort(table(v),decreasing=TRUE)&#62; names(V)[1:40] [1] " United States"                            [2] " Department of Statistics"                 [3] " Department of Mathematics"                [4] " M."                                       [5] " J."                                       [6] " A."                                       [7] " S."                                       [8] " United Kingdom"                           [9] " France"                                  [10] " D."                                      [11] " P."                                      [12] " Y."                                      [13] " R."                                      [14] " China"                                   [15] " H."                                      [16] " Germany"                                 [17] " Department of Economics"                 [18] " C."                                      [19] " G."                                      [20] " L."                                      [21] " Canada"                                  [22] " T."                                      [23] " University of California"                [24] " Department of Biostatistics"             [25] " F."                                      [26] " B."                                      [27] " Department of Mathematics and Statistics"[28] " E."                                      [29] " K."                                      [30] " N."                                      [31] " Department of Computer Science"          [32] " Japan"                                   [33] " Australia"                               [34] " X."                                      [35] " Hong Kong"                               [36] " Italy"                                   [37] " W."                                      [38] " Spain"&#160;A lot of useless information, for sure, but also more valuable information. Like university names,&#62; names(V)[c(23,50,58,59,61,66,67,72,84,87,89)] [1] " University of California"     " Stanford University"          [3] " Chapel Hill"                  " University of Washington"     [5] " Stanford"                     " University of Michigan"       [7] " Carnegie Mellon University"   " Columbia University"          [9] " Cornell University"           " University of North Carolina"[11] " Duke University"or cities,&#62; names(V)[c(35,40,41,44,45,47,51,53,54,55,56,62,64,65,+ 70,71,82,92,97)] [1] " Hong Kong"    " New York"     " Berkeley"     " Cambridge"    [5] " Boston"       " Seattle"      " London"       " Pittsburgh"   [9] " Los Angeles"  " Singapore"    " Beijing"      " Philadelphia"[13] " Ann Arbor"    " Atlanta"      " Toronto"      " Baltimore"   [17] " Chicago"      " San Diego"    " Tokyo"I decided to focus on 90 locations. Each time I have a string which is the same as the name of one of my 90 cities, I keep it. So if there is a Prof. Ann Arbor, I will consider that person as a city. Here is the graph of all locations, with the number of "articles". Or contributors. If four people in San Francisco published toegher an article, the article appears four times in my dataset. I did spend some time with Cambridge, and I decided to move Cambridge, MA to Boston, MA. Just for convenience.&#62; require("geosphere")&#62; require("maps")&#62; data(world.cities)&#62; data(us.cities)&#62; data(canada.cities)&#62; LOCALIZE=Vectorize(function(v){z=findLatLon(v)$latlon;if(is.na(z)){z=c(NA,NA)};return(z)})&#62; CITIES=names(V)[city]&#62; NCITIES=substr(CITIES,2,nchar(CITIES))&#62; NCITIES[substr(NCITIES,1,5)=="Paris"]="Paris"&#62; NCITIES=unique(NCITIES)&#62; LC=matrix(unlist(LOCALIZE(NCITIES)),nrow=2)&#62; BASELOC=data.frame(CITY=NCITIES,LAT=LC[2,],LON=LC[1,])I did spend some time on some cities, such as Paris, or London, where zip code was sometimes attached to the city name. I also had to fix some problems... But after a few minuts, I was able to locate those cities.Then, I wanted to extract information about all publications. Keywords are interesting, but over 266,567 "publications", it is hard to use (sometimes it is not file, somethimes it is extremely general, or extremely specialized). So I decided to extract words from the title of the contribution.&#62; VCITY=NULL&#62; VKW=NULL&#62; VY=NULL&#62; VJ=NULL&#62; VA=NULL&#62; VW=NULL&#62; art=0&#62; for(i in 1:length(L)){+ B=read.csv(L[i])+ A=B$Authors.with.affiliations+ for(j in 1:length(A)){+ art=art+1+ x1=as.character(A[j])+ x2=strsplit(x1,",")+ listu=which(x2[[1]]%in%CITIES)+ if(length(listu)&#62;0){+ C=tolower(paste(" ",as.character(B[j,"Title"]),sep=""))+ x3=strsplit(C," ")[[1]]+ kx3=which(!x3%in%c("a","the","of","an","in","",+ "for","and","with","on","to","using","from","under"))+ x3=x3[kx3]+ J=as.character(B[j,"Source.title"])+ Y=B[j,"Year"]+ n1=length(listu)+ n2=length(x3)+ VCITY=c(VCITY,rep(x2[[1]][listu],each=n2))+ VKW=c(VKW,rep(x3,n1))+ VY=c(VY,rep(Y,n1*n2))+ VJ=c(VJ,rep(J,n1*n2))+ VA=c(VA,rep(art,n1*n2))+ VW=c(vW,rep(1/n2,n1*n2))+ }}}­&#62; BASEUNIV=data.frame(CITY=NCITIES,KEYW=VKW,YEAR=VY,JOURNAL=VJ,INDICE=VA,W=W)Here, I got a huge dataset. One line is one city and one "word". Now, let us select one word, and let us plot how important that word is, in each city,&#62; Figure=function(keyword="bayesian"){+ SBASEUNIV=BASEUNIV[BASEUNIV$KEYW==keyword,]+ SB2=tapply(SBASEUNIV$W,SBASEUNIV$CITY,sum)+ D=data.frame(CITY=names(SB2),CT=as.vector(SB2))+ BASE=merge(BASELOC,D)+ library(maps)+ library(RColorBrewer)+ CL=brewer.pal(6, "RdBu")+ Y=SB2/SB*sum(SB,na.rm=TRUE)/sum(SB2,na.rm=TRUE)+ X=cut(Y,breaks=c(0,.5,.75,1,1.333,2,10000))+ levels(X)=1:6+ library(maps)+ map("world")+ points(BASE$LON,BASE$LAT,pch=1,col=CL[as.numeric(X)],+ cex=sqrt(Y*20),lwd=4)+ }In the code above, we compare with the independent case (if cities and keywords where independent) since we normalize usingSB2/SB*sum(SB,na.rm=TRUE)/sum(SB2,na.rm=TRUE)For bayesian statistics (publication with the word bayesian in the title)For nonparametric statistics (publication with the word nonparametric in the title)For stochastic processes (publication with the word processes in the title)(the problem here is that we cannot visualize the red circles: if in a city, no one published on a given topic, it would be strong red, but tiny, or even null... so we won't see it). It decided to keep the top 250 words that appeaared in titles, I removed standard common words, such as it, the, of, etc.&#62; listewords=names(sort(table(BASEUNIV$KEYW),decreasing=TRUE)[1:250])&#62; listewords=listewords[-c(1,2,3,4,7,15,24,42,129)]&#62; idx=which(BASEUNIV$KEYW%in%listemots)&#62; T=table(as.character(BASEUNIV$KEYW[idx]),BASEUNIV$CITY[idx])&#62; MATRICE=as.matrix(T)I had a nice contingency table, with 90 cities, versus 200 words.&#62; library("FactoMineR")&#62; res.pca = PCA(t(MATRICE), scale.unit=TRUE, ncp=5, + graph=FALSE)&#62; plot.PCA(res.pca, axes=c(1, 2), choix="ind")Principal component analysis was disapointing,So I decided to extract, per city, the largest contributions to the chi-square distance&#62; K2=chisq.test(MATRICE)&#62; M2=K2$expectedOn the graph below, the green level is the theoretical counts of each word, under some independence assumption. The dark line is the observed one. For instance, in San Francisco, on top, we have words that were not used a lot (e.g. processes: given the total number of publications, it would make sense to have 6 or 7 publications with the word processes, but there were 0 publications actually), and below words that were intensively used. Intensively (such as method and structure, the last one was expected two or three times, but it appeared in 25 publications) compared with the other cities,In Boston, MA,  we gotIn New York City, NYIn Paris (France),But to be honest, I was disapointed. I mean, yes, I can see on the previous graph, for instance, that there are a lot of people working on stochastic processes, with the words Brownian and Markov. But for most cases, I can hardly get an interpretation...I tried a finaly graph, on interconnexions between authors. The first point is that it is common to have joint publications with colleagues, in the same city. The largest the point, the more joint papers,But we can add here cross publications: the thinner the line, the less joint publications between two places,We can see that I missed in the first part the Cambridge-Boston distinction, since Cambridge should now stand for Cambridge, UK. But the line is clearly too large to be explained here by collaboration betweem Cambridge, UK, and Boston, MA. But still. a lot of them can be explained, with Hong-Kong and Shanghai, or Mexico and Guanajuato.If someone has better ideas to import properly the locations (or affiliations, it might be fun to focus on universities) and perhaps the abstract (more than the title), I'd be glad to try the same study in Economic journals...