# Which Cities Produce More Excellent Papers Than Can Be Expected? A New Mapping Approach, Using Google Maps, Based on Statistical Significance Testing

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4 We advise checking the maps against the original data at a number of random places before exhibiting it on the web. The user has all statistical data available in the file ucities.dbf, which is created by topcity2.exe. In the case of systemic error, we appreciate and will respond to feedback. Statistical Procedure The z-test for two independent proportions (Sheskin, 2007, pp ) is used for evaluating the degree to which an observed number of top-cited papers for a city differs from the value that would be expected on the basis of the null hypothesis of randomness in the selection of papers for a city. If excellent papers are defined as the top 10% most-highly cited papers in a field, on the basis of this null hypothesis one could expect 10% of all papers published in each city, respectively, to belong to this category. Ten percent of the papers can be expected to belong to the 10% most-highly cited papers in a certain field. The following equation is employed to compute the test statistic: p o p e z = p(1 p)(2/n) where: n represents the number of all papers published by authors located at a city; p o = n o /n represents the proportion of papers (published by authors located in this city) that are among the top 10% most-highly cited papers (n o represents the number of observed papers in this category); p e = 0.10 is the proportion of papers expected to be among the top 10% most-highly cited papers; p = n op o + n e p e 2n where: n e = n/10 represents the expected number of the top 10% most-highly cited papers. z is positively signed if the observed number of top papers is larger than the expected number and negatively signed in the reverse case. Since the z-test for two independent proportions is a large sample procedure for evaluating a 2 2 contingency table (Sheskin, 2007, p. 637), n e should be equal to or larger than 5. An absolute value of z larger than 1.96, 2.58, or 3.29 indicates statistical significance at the 5%, 1%, or 0.1% level (p < 0.05, p < 0.01, or p < 0.001) for the difference between observed and expected numbers of topcited papers. In other words, the authors located at this city are outperformers with respect to scientific excellence in terms of these statistics. Using this statistical procedure, we designed the city circles that are visualized on the map using different colors and sizes. The radii of the circles are calculated by using: observed value expected value +1 The +1 must prevent the circles from disappearing if the observed ratio is equal to the expected one. Furthermore, the circles are colored green if the observed values are larger than the expected values. We use dark green if both the expected value is at least five (and a statistical significance test is legitimate) and z is statistically significant; light green indicates a positive, but statistically nonsignificant result. The in-between color of lime green is used if the expected value is smaller than five and a statistical significance test hence should not be calculated. One should be cautious with interpretations of results below this threshold value. In the reverse case that the observed values are smaller than the expected values the circles are red or orange, respectively. They are red if the observed value is significantly smaller than the expected value and orange-red if the difference is statistically nonsignificant. If the requirement for the test of an expected value larger than five is not fulfilled the circle is colored orange. If the expected value equals the observed value a circle is colored gray. Results In order to show the fruitfulness of the proposed method we provide three field-specific maps based on sample data. Figure 1 shows the location of authors in Europe having published highly cited papers in physics. The map is based on a sample of the top 10% of articles published in 2008 in a WoS journal set for physics. The physics sample consists of 9,950 articles (searched in WoS, v. 4.1, on February 2, 2011) categorized by Thomson Reuters into the following journal sets: PHYSICS, APPLIED (3,427 articles) PHYSICS, ATOMIC, MOLECULAR & CHEMICAL (905 articles) PHYSICS, CONDENSED MATTER (2,578 articles) PHYSICS, FLUIDS & PLASMAS (522 articles) PHYSICS, MATHEMATICAL (996 articles) PHYSICS, MULTIDISCIPLINARY (2,425 articles) PHYSICS, PARTICLES & FIELDS (772 articles) The number of 9,950 is different from the sum of the articles for the single categories since some articles are categorized in more than a single subfield. A search in WoS version 5.2 shows that this sample (n = 9,950) is nearly 10% of all (117,244) papers published in the physics category in The top 10% most-highly cited articles in the sample are those 1,071 papers that received at least eight citations each between their publication in 2008 and the date of the search. Figure 1 shows the cities of authors of the excellent papers (the circles with different colors on the map) and the deviations of the observed from the expected number of top-cited papers per location (the circle radii). If one clicks on a circle (at a frame opens showing the number of observed versus expected values for the respective city, as well as asterisks indicating whether the difference between the values is statistically significant or not at the 5% (*), 1% (**), or 0.1% (***) level. For example, the label for Kiev shows the observed versus expected numbers for this city. The city is indicated by a red circle because of a larger expected than observed value. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY October

5 FIG. 1. Cities in Europe in which highly cited papers in physics were published in The global map is available at topcity/figure1.htm. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] In the frame the large and statistically significant difference between observed (n = 1) and expected value (n = 23.5) in the sample is provided as a ratio. The largest red circle on this map is indicated for Moscow. Although Moscow has a high output of papers in physics, the number of top-cited papers is comparably small in the sample. Large green circles on the map are visible for London, Paris, Karlsruhe, Munich (and Garching), Pisa, and Rome. The largest difference between observed and expected values is for London; 35% of all published papers belong to the top 10% most-highly cited papers. Cambridge, close to London, also has a dark green circle. The observed citation counts are higher than the expected counts and this difference is statistically significant. Figure 2 shows the corresponding map for the chemistry sample data (specified above in Methods). For chemistry, one can see a similar distribution of red and green circles as for the physics sample data (see Figure 1). The more one moves to the east of Europe the fewer green circles are visible. High densities of green circles can in this case be found in southwestern Germany, the southeastern part of the UK, as well as areas in Denmark (around Copenhagen), the Netherlands, Switzerland, and Italy. As in the physics map, a large red circle is positioned at Moscow. Although in the sample six top-cited papers were published from authors located here, compared to the expected value, this observed value is low. In agreement with physics, a higher number of the observed against the expected value is indicated for Cambridge in this case. Prague also has a higher than expected contribution to the top 10% layer in the sample data for chemistry. Since physics and chemistry are both natural science fields it could be interesting to see how the European map changes in the case of psychology. Figure 3 shows the map for the authors of the top-cited papers. All in all, 1,592 top-cited papers were selected in the Social Sciences Citation Index 1958 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY October 2011

6 FIG. 2. Cities in Europe in which highly cited papers in chemistry were published in The global map is available at topcity/figure2.htm. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] (WoS, 4.1) on the basis of 15,142 articles published in This represents more than half of the entire population of (24,877) papers within this category (as searched in WoS, v. 5.2): PSYCHOLOGY (2,398) PSYCHOLOGY, APPLIED (1,400) PSYCHOLOGY, BIOLOGICAL (591) PSYCHOLOGY, CLINICAL (2,988) PSYCHOLOGY, DEVELOPMENTAL (1,954) PSYCHOLOGY, EDUCATIONAL (991) PSYCHOLOGY, EXPERIMENTAL (3,022) PSYCHOLOGY, MATHEMATICAL (444) PSYCHOLOGY, MULTIDISCIPLINARY (3,436) PSYCHOLOGY, SOCIAL (1,670) Each of the 1,592 top-cited papers in the sample received at least eight citations between 2008 and the date of research (March 2, 2011). Ten percent of 15,142 is 1,514. However, topcity2.exe includes ranks that are tied at the percentile level chosen by the researcher and thus we retrieved 1,592 records using the 10% level. In Figure 3 it is interesting to see a much smaller circle for Moscow than in both other figures. In the psychology sample data, the total output with an address in Moscow is small. The largest set in Europe (in this psychology sample) is attributed to London: 149 top-cited papers against 71.6 expected papers. Thus, London, UK outperforms the expectation to a statistically significant degree. However, with 45 papers in the top 10% (against 19.4 expected), Berlin outperforms London in this sample with an observed/expected rate of 2.32 (p < 0.001) against 2.08 (p < 0.001) despite the lower number in the sample. The comparison of London and Berlin points out the importance of considering not only the real numbers of excellent papers but also the expected numbers. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY October

7 FIG. 3. Cities in Europe in which highly cited papers in psychology were published in The global map is available at topcity/figure3.htm. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Discussion A number of works have been published on productivity rankings and centers of excellence (see, e.g., Buela-Casal, Gutiérrez-Martínez, Bermúdez-Sánchez, & Vadillo-Muñoz, 2007; Costas et al., 2009; Frenken et al., 2009; Glänzel et al., 2009). The methods presented in this paper allow for an analysis revealing centers of excellence around the world using programs that are freely available. Based on WoS data, field-specific excellence can be identified in cities where highly cited papers were published. Compared to the mapping approaches published hitherto, our approach is more analytically oriented by allowing the assessment of an observed number of excellent papers for a city against the expected number. With this feature, this approach can not only identify the top performers in output but the true jewels. These are cities in which authors are located who publish significantly more top-cited papers than can be statistically expected. As the sample datasets in this paper show for three fields, these cities do not necessarily have a high output of highly cited papers. Thus, our approach normalizes for size. Despite the advantages of our approach by mapping observed versus expected numbers, we recognize the limitations inherent to these bibliometric exercises: (1) Publications are among several types of scientific activities. (2) It is not always the case that the addresses listed on the publication reflect the locations where the reported research was conducted. (3) The handling of multiple authorships is different in scientific fields. For example, the first and last authors have a special meaning in many, but not in all fields of science JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY October 2011

9 Cardona, M., & Marx, W. (2006). Vitaly L. Ginzburg A bibliometric study. Journal of Superconductivity and Novel Magnetism, 19(3 5), Costas, R., Bordons, M., van Leeuwen, T.N., & van Raan,A.F.J. (2009). Scaling rules in the science system: Influence of field-specific citation characteristics on the impact of individual researchers. Journal of the American Society for Information Science and Technology, 60(4), Dehon, C., McCathie, A., & Verardi, V. (2010). Uncovering excellence in academic rankings: A closer look at the Shanghai ranking. Scientometrics, 83(2), Frenken, K., Hardeman, S., & Hoekman, J. (2009). Spatial scientometrics: Towards a cumulative research program. Journal of Informetrics, 3(3), Glänzel, W., Thijs, B., Schubert, A., & Debackere, K. (2009). Subfieldspecific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance. Scientometrics, 78(1), Jeremic,V., Bulajic, M., Martic, M., & Radojicic, Z. (2011).A fresh approach to evaluating the academic ranking of world universities. Scientometrics, 87(3), Lewison, G., Thornicroft, G., Szmukler, G., & Tansella, M. (2007). Fair assessment of the merits of psychiatric research. British Journal of Psychiatry, 190, Leydesdorff, L., & Persson, O. (2010). Mapping the geography of science: Distribution patterns and networks of relations among cities and institutes. Journal of the American Society for Information Science and Technology, 61(8), Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), Marx, W. (2011). Special features of historical papers from the viewpoint of bibliometrics. Journal of the American Society for Information Science and Technology, 62(3), Matthiessen, C.W., Schwarz, A.W., & Find, S. (2002). The top-level global research system, : Centres, networks and nodality. An analysis based on bibliometric indicators. Urban Studies, 39(5 6), Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), Seglen, P.O., & Aksnes, D.W. (2000). Scientific productivity and group size: a bibliometric analysis of Norwegian microbiological research. Scientometrics, 49(1), Sheskin, D. (2007). Handbook of parametric and nonparametric statistical procedures (4th ed.). Boca Raton, FL: Chapman & Hall/CRC. Tijssen, R., & van Leeuwen, T. (2006). Centres of research excellence and science indicators. Can excellence be captured in numbers? In W. Glänzel (Ed.), Ninth International Conference on Science and Technology Indicators (pp ). Leuven, Belgium: Katholieke Universiteit Leuven. Tijssen, R., Visser, M., & van Leeuwen, T. (2002). Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference? Scientometrics, 54(3), van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), Wagner, C.S. (2008). The new invisible college. Washington, DC: Brookings Press JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY October 2011

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