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GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

CANCER 2019年第15期125卷 2544-2560页

作者： Sahar, Liora Foster, Stephanie L. Sherman, Recinda L. Henry, Kevin A. Goldberg, Daniel W. Stinchcomb, David G. Bauer, Joseph E. Amer Canc Soc Stat & Evaluat Ctr Geospatial Res 250 Williams St Atlanta GA 30303 USA Ctr Dis Control & Prevent Geospatial Res Anal & Serv Program Atlanta GA USA North Amer Assoc Cent Canc Registries Data Use & Res Springfield IL USA Temple Univ Dept Geog & Urban Studies Philadelphia PA 19122 USA Fox Chase Canc Ctr Canc Prevent & Control Program 7701 Burholme Ave Philadelphia PA 19111 USA Texas A&M Univ Coll Geosci Dept Geog College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn Coll Engn College Stn TX USA Westat Corp Hlth Studies Rockville MD USA Amer Canc Soc Stat & Evaluat Ctr Atlanta GA 30329 USA

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

关键词： cancer surveillance geographic information science (GIScience) Geographic Information Systems (GIS) mapping and visualization spatial epidemiology spatial statistics

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Using full-text data to create improved term maps 16

Using full-text data to create improved term maps

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： van Eck, Nees Jan Waltman, Ludo Song, Min Jeong, Yoo Kyung Leiden Univ Ctr Sci & Technol Studies Leiden Netherlands Yonsei Univ Dept Lib & Informat Sci Seoul South Korea

A term map offers a visualization of a network of terms that co-occur in scientific publications. Term maps are usually created based on the titles and abstracts of publications. In this paper, we explore the use of full-text data for creating term maps. We create and compare a series of term maps based on the full text of publications in Journal of Informetrics. We use our results to discuss the advantages and disadvantages of different approaches for creating term maps.

关键词： Co-occurrence analysis mapping and visualization

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Funding Map for Research Project Relationships using Paragraph Vectors 16

Funding Map for Research Project Relationships using Paragra...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Kawamura, Takahiro Watanabe, Katsutaro Matsumoto, Naoya Jibu, Mari Japan Sci & Technol Agcy Tokyo Japan

Research funding based on scientific metrics is strongly required;however, navigating the funding landscape is challenging. Conventional techniques that use intercitation and co-citation cannot be utilized for ongoing projects. Therefore, to grasp research projects on grants and visualize their relationship, this paper proposes to locate projects in a multi-dimensional space using a paragraph vector technique with an entropy-based clustering method. After describing our novel method, we provide details of the experiments we conducted to show that the proposed method successfully formed meaningful project clusters from 25,607 project descriptions from the 7th Framework Programme. Finally, we identify significant relationships from the project map, e.g., projects with similarities in different subject categories and projects bridging different categories.

关键词： Knowledge discovery and data mining mapping and visualization

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Overlapping Thematic Structures Extraction with Mixed-Membership Stochastic Blockmodel 16

Overlapping Thematic Structures Extraction with Mixed-Member...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Xu, Shuo Liu, Junwan Wang, Zheng Beijing Univ Technol Beijing 100124 Peoples R China Inst Sci & Tech Informat China Beijing 100038 Peoples R China

It is increasing important to identify automatically thematic structures from massive scientific literature. The interdisciplinarity enables thematic structures without natural boundaries. In this work, the identification of thematic structures is regarded as an overlapping community detection problem from the citation-link network. Thus, the overlapping thematic structures can be detected from citation-link network with a mixed-membership stochastic blockmodel, armed with stochastic infer algorithm. Experimental results on the astro dataset indicate that it is feasible to extract overlapping thematic structures.

关键词： mapping and visualization Social network analysis

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UK ethnic minority cancer researchers: their origins, destinations and sex 16

UK ethnic minority cancer researchers: their origins, destin...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Begum, Mursheda Roe, Philip Webber, Richard Lewison, Grant Kings Coll London Dept Canc Studies London SE1 9RT England Evaluametr Ltd 157 Verulam Rd St Albans AL3 4DW England Bisham Gardens London N6 6DJ England

We identified the names of all authors of Web of Science papers on cancer in 2009-11 and in 2014-16 with a UK address, and listed those in each postcode area (the first one or two letters of UK postcodes). Their ethnicity or national origins were determined from their names with the OriginsInfo database into seven main groups;African, Asian excluding South Asian, South Asian, Western European, Eastern European, Islamic World, and the UK/Ireland. We were thus able to determine the geographical distribution within the UK of members of the six immigrant groups in terms of their fractional contributions to cancer research. The percentage of research contributions from scientists with non-British (or Irish) names rose from 37% in 2009-11 to 39.4% in 2014-16. It was higher in London, and rose from 47% to 51%. The largest ethnic contributions were from Europe: in 2014-16 the percentages were 15.4% from Western Europe and 3.7% from eastern Europe. There were 8.1% of contributions from South Asia, and 5.6% from the rest of Asia;the largest were in four areas of London, and in Cambridge and Oxford. Female participation is still less than that of males, but it is increasing by about 2% per year.

关键词： Country-level studies mapping and visualization Participation in science Science policy

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MFTACA: An Author Co-citation Analysis Method Combined with Metadata in Full Text 16

MFTACA: An Author Co-citation Analysis Method Combined with ...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Bu, Yi Wang, Binglu Huang, Win-bin Che, Shangkun Indiana Univ Bloomington IN USA Peking Univ Beijing Peoples R China

As a frequently used method of depicting scientific intellectual structures, author co-citation analysis (ACA) has been applied to many domains However, only count-based information is involved as the input of ACA, which is not sufficiently informative for knowledge representations. This article catches several metadata in full text of citing papers but not aims at content-level information, which could increase the amount of information input to ACA but not increase computational complexity a lot. By involving information including the number of mentioned times in a citing paper, the number of context words in a citing sentence, and the published year of a reference, we propose a new method called MFTACA (metadata-in-full-text-based ACA). We combine these pieces of information into traditional ACA and compare the results between ACA and MFTACA by using factor analysis, network analysis, and MDS-measurement. The result of our empirical study indicates that compared with traditional ACA, our proposed method shows a better clustering performance in visualizations and reveals more details in displaying intellectual structures.

关键词： Citation and co-citation analysis mapping and visualization Science of science

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Research Progress of Library and Information Science: Exploring Chinese Scientific Articles from 2001 to 2015 16

Research Progress of Library and Information Science: Explor...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Hao, Liu Chinese Acad Sci Chengdu Lib & Informat Ctr Scientometr & Evaluat Res Ctr Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100040 Peoples R China

Academic article can reflect the performance of science research. It is necessary to learn the research progress of library and information science (LIS) from the perspective of English articles. This paper regards the articles written by Chinese scholars published during the period from 2001 to 2015 in Web of Science Core Collection as research object, and explores the research progress of LIS from three aspects, which include an overview of Chinese and world articles published in LIS, the most influential institutions and the main research contents. The performance of Chinese scholars in LIS has been enhanced in terms of both quantity and participation in the world. The threshold of the most influential institutions is gradually increased. Besides, the most influential institutions in LIS in China have shifted from Hong Kong to Chinese Mainland. Information retrieval, digital libraries, information systems, data mining, knowledge management and etc. can be considered as classic research contents in LIS. This paper provides the research progress of library and information science in China from three aspects.

关键词： mapping and visualization Social network analysis

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Detecting social communities in Spanish Theses Defense Committee in the area of Computer Science 16

Detecting social communities in Spanish Theses Defense Commi...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Duarte-Martinez, V Lopez-Herrera, A. G. Cobo, M. J. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Cadiz Dept Comp Sci & Engn Cadiz Spain

In this contribution a bibliometric analysis of the computer science theses defended in Spain is carried out based on bibliographic networks and communities detection. Data was retrieved from Teseo database. Then, a filtering process was applied in order to get only theses data in which doctoral programs title match with keywords related with Computer Science. Then, we build a network with theses defense committee members from data extracted and we apply fundamentals of social network analysis and community detection techniques to analyze data and uncover novel characteristics from network structure.

关键词： Co-occurrence analysis Country-level studies mapping and visualization Social network analysis

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University rankings in computer science: a study and visualization of 'geo-based' impact and conference proceeding (CORE) scores 16

University rankings in computer science: a study and visuali...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Ehret, Philip Zuccala, Alesia Gipp, Bela Univ Konstanz Constance Germany Univ Copenhagen Copenhagen Denmark

This is a research-in-progress paper concerning two types of institutional rankings, the Leiden and QS World ranking, and their relationship to a list of universities' 'geo-based' impact scores, and Computing Research and Education Conference (CORE) participation scores in the field of computer science. A 'geo-based' impact measure examines the geographical distribution of incoming citations to a particular university's journal articles for a specific period of time. It takes into account both the number of citations and the geographical variability in these citations. The CORE participation score is calculated on the basis of the number of weighted proceedings papers that a university has contributed to either an A*, A, B, or C conference as ranked by the Computing Research and Education Association of Australasia. In addition to calculating the correlations between the distinct university rankings and the separate 'geo-based' versus CORE scores, we are in the process of developing a geographical visualization tool that presents the metrics so that they may be examined in an explorative way.

关键词： mapping and visualization Science communication University policy and institutional rankings

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LitStoryTeller: An Interactive System for Visual Exploration of Scientific Papers Leveraging Named entities and Comparative Sentences 16

LitStoryTeller: An Interactive System for Visual Exploration...

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16th International Conference on Scientometrics and Informetrics (ISSI)

作者： Ping, Qing Chen, Chaomei Drexel Univ Philadelphia PA 19104 USA

The present study proposes LitStoryTeller, an interactive system for visually exploring the semantic structure of a scientific article. We demonstrate how LitStoryTeller could be used to answer some of the most fundamental research questions, such as how a new method was built on top of existing methods, based on what theoretical proof and experimental evidences. More importantly, LitStoryTeller can assist users to understand the full and interesting story a scientific paper, with a concise outline and important details. The proposed system borrows a metaphor from screen play, and visualizes the storyline of a scientific paper by arranging its characters (scientific concepts or teuninologies) and scenes (paragraphs/sentences) into a progressive and interactive storyline. Such storylines help to preserve the semantic structure and logical thinking process of a scientific paper. Semantic structures, such as scientific concepts and comparative sentences, are extracted using existing named entity recognition APIs and supervised classifiers, from a scientific paper automatically. Two supplementary views, ranked entity frequency view and entity co-occurrence network view, are provided to help users identify the "main plot" of such scientific storylines. When collective documents are ready, LitStoryTeller also provides a temporal entity evolution view and entity community view for collection digestion.

关键词： Knowledge discovery and data mining mapping and visualization Methods and techniques

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