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Article | Open Access

ST-Map: an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Chenyu ZUO1,2, Yifan XU2, Lingfang DING3, Liqiu MENG2
ETH Zurich, Zurich 8050, Switzerland
Chair of Cartography and Visual Analgtics, TU Munich, Munich 80333, Germany
Norwegian University of Science and Technology, Trondheim 7491, Norway
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Abstract

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations. Many studies use bibliometric data to analyze the popularity of certain research topics, well-adopted methodologies, influential authors, and the interrelationships among research disciplines. However, the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging. This study combined a Space-Time Cube (STC) and a 3D glyph to represent the complex multivariate bibliographic data. We further implemented a visual design by developing an interactive interface. The effectiveness, understandability, and engagement of ST-Map are evaluated by seven experts in geovisualization. The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Keywords

user studyspatiotemporal analysisspace-time cubebibliographic datainteractive map

References

[1]

GROSSMAN G M, HELPMAN E. Trade, knowledge spillovers, and growth[J]. European Economic Review, 1991, 35(2-3): 517-526.
Crossref Google Scholar
[2]

DÖRING T, SCHNELLENBACH J. What do we know about geographical knowledge spillovers and regional growth: A survey of the literature[J]. Regional Studies, 2006, 40(3): 375-395.
Crossref Google Scholar
[3]

CSOMÓS G. On the challenges ahead of spatial scientometrics focusing on the city level[J]. Aslib Journal of Information Management, 2020, 72(1): 67-87.
Crossref Google Scholar
[4]

CSOMÓS G, VIDA Z V, LENGYEL B. Exploring the changing geographical pattern of international scientific collaborations through the prism of cities[J]. PLoS One, 2020, 15(11): e0242468.
Crossref Google Scholar
[5]
BORT S, OEHME M, ZOCK F. Regional networks, alliance portfolio configuration, and innovation performance[M]//Understanding the Relationship Between Networks and Technology, Creativity and Innovation. Leeds: Emerald Group Publishing Limited, 2014: 229-256.
Crossref
[6]

ORLANDO M, VERBA M, WEILER S. Universities, agglomeration, and regional innovation[J]. Review of Regional Studies, 2019, 49(3): 407-427.
Crossref Google Scholar
[7]

AUTANT-BERNARD C, LESAGE J P. Quantifying knowledge spillovers using spatial econometric models[J]. Journal of Regional Science, 2011, 51(3): 471-496. DOI: 10.1111/j.1467-9787.2010.00705.x.
Crossref Google Scholar
[8]

HOLL A, PETERS B, RAMMER C. Local knowledge spillovers and innovation persistence of firms[J]. Economics of Innovation and New Technology, 2023, 32(6): 826-850. DOI: 10.1080/10438599.2022.2036609.
Crossref Google Scholar
[9]

BUZARD K, CARLINO G A, HUNT R M, et al. Localized knowledge spillovers: evidence from the spatial clustering of R&D labs and patent citations[J]. Regional Science and Urban Economics, 2020, 81: 103490.
Crossref Google Scholar
[10]

TIBERIUS V, SCHWARZER H, ROIG-DOBÓN S. Radical innovations: between established knowledge and future research opportunities[J]. Journal of Innovation & Knowledge, 2021, 6(3): 145-153.
Crossref Google Scholar
[11]

CANCINO C A, MERIGÓ J M, CORONADO F C. A bibliometric analysis of leading universities in innovation research[J]. Journal of Innovation & Knowledge, 2017, 2(3): 106-124.
Crossref Google Scholar
[12]

J-FIGUEIREDO R, NETO J V, QUELHAS O L G, et al. Knowledge Intensive Business Services (KIBS): bibliometric analysis and their different behaviors in the scientific literature: topic 16-innovation and services[J]. RAI Revista de Administração e Inovação, 2017, 14(3): 216-225.
Crossref Google Scholar
[13]

NASCIMENTO R F, ÁVILA M F, TARANTO O P, et al. Agglomeration in fluidized bed: bibliometric analysis, a review, and future perspectives[J]. Powder Technology, 2022, 406: 117597.
Crossref Google Scholar
[14]

KLEMINSKI R, KAZIENKO P, KAJDANOWICZ T. Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification[J]. Journal of Information Science, 2022, 48(3): 349-373. DOI: 10.1177/0165551520962775.
Crossref Google Scholar
[15]

KEMENY T, STORPER M. Is specialization good for regional economic development?[J]. Regional Studies, 2015, 49(6): 1003-1018.
Crossref Google Scholar
[16]

BOTTAZZI G, GRAGNOLATI U. Cities and clusters: economy-wide and sector-specific effects in corporate location[J]. Regional Studies, 2015, 49(1): 113-129.
Crossref Google Scholar
[17]

BUENSTORF G, FRITSCH M, MEDRANO L F. Regional knowledge, organizational capabilities and the emergence of the west German laser systems industry, 1975—2005[J]. Regional Studies, 2015, 49(1): 59-75.
Crossref Google Scholar
[18]

GUI Qinchang, DU Debin, LIU Chengliang. The changing geography of scientific knowledge production: evidence from the metropolitan area level[J]. Applied Spatial Analysis and Policy, 2023, 17(1): 157-174.
Crossref Google Scholar
[19]

SUN Huaping, EDZIAH B K, KPORSU A K, et al. Energy efficiency: the role of technological innovation and knowledge spillover[J]. Technological Forecasting and Social Change, 2021, 167: 120659.
Crossref Google Scholar
[20]

ZHAO Yang, WANG Lin, ZHANG Yaming. Research thematic and emerging trends of contextual cues: a bibliometrics and visualization approach[J]. Library Hi Tech, 2021, 39(2): 462-487.
Crossref Google Scholar
[21]

NINKOV A, FRANK J R, MAGGIO L A. Bibliometrics: Methods for studying academic publishing[J]. Perspectives on Medical Education, 2022, 11(3): 173-176.
Crossref Google Scholar
[22]

DONTHU N, KUMAR S, MUKHERJEE D, et al. How to conduct a bibliometric analysis: an overview and guidelines[J]. Journal of business research, 2021, 133: 285-296.
Crossref Google Scholar
[23]
MOLONTAY R, NAGY M. Twenty years of network science: a bibliographic and co-authorship network analysis[M]//ÇAKIRTAŞ M, OZDEMIR M K. Big Data and Social Media Analytics: Trending Applications. Cham: Springer, 2021: 1-24. DOI: 10.1007/978-3-030-67044-3_1.
Crossref
[24]

HEVEY D. Network analysis: a brief overview and tutorial[J]. Health Psychology and Behavioral Medicine, 2018, 6(1): 301-328. DOI: 10.1080/21642850.2018.1521283.
Crossref Google Scholar
[25]

LOZANO S, CALZADA-INFANTE L, ADENSO-DÍAZ B, et al. Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature[J]. Scientometrics, 2019, 120(2): 609-629.
Crossref Google Scholar
[26]

LIM W M, KUMAR S. Guidelines for interpreting the results of bibliometric analysis: a sensemaking approach[J]. Global Business and Organizational Excellence, 2024, 43(2): 17-26.
Crossref Google Scholar
[27]

ZHUANG Liang, YE Chao, LIESKE S N. Intertwining globality and locality: bibliometric analysis based on the top geography annual conferences in America and China[J]. Scientometrics, 2020, 122(2): 1075-1096.
Crossref Google Scholar
[28]

IBÁÑEZ J J, BREVIK E C, CERDÀ A. Geodiversity and geoheritage: detecting scientific and geographic biases and gaps through a bibliometric study[J]. Science of The Total Environment, 2019, 659: 1032-1044.
Crossref Google Scholar
[29]
KRAKER P, SCHRAMM M, KITTEL C, et al. Openknowledgemaps/Headstart: Headstart 7[EB/OL]. (2021-11-25)[2024-01-02]. https://doi.org/10.5281/zenodo.5726914.
[30]

CHEN Chaomei, SONG Min. Visualizing a field of research: A methodology of systematic scientometric reviews[J]. PLoS One, 14(10): e0223994. DOI: 10.1371/journal.pone.0223994.
Crossref Google Scholar
[31]

CHEN Chaomei. Citespace Ⅱ: detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
Crossref Google Scholar
[32]

SEBASTIAN Y, SIEW E G, ORIMAYE S O. Emerging approaches in literature-based discovery: techniques and performance review[J]. The Knowledge Engineering Review, 2017, 32: e12.
Crossref Google Scholar
[33]

ZUO Chenyu, DING Linfang, YANG Zhuo, et al. Multiscale geovisual analysis of knowledge innovation patterns using big scholarly data[J]. Annals of GIS, 2022, 28(2): 197-212.
Crossref Google Scholar
[34]
AIGNER W, MIKSCH S, SCHUMANN H, et al. Guiding the selection of visualization techniques[M]//AIGNER W, MIKSCH S, SCHUMANN H, et al. Visualization of Time-Oriented Data. London: Springer, 2023: 193-210. DOI: 10.1007/978-1-4471-7527-8_7.
Crossref
[35]

MCNABB L, LARAMEE R S. Multivariate maps—a glyph-placement algorithm to support multivariate geospatial visualization[J]. Information, 2019, 10(10): 302.
Crossref Google Scholar
[36]

WEI Zhonghui, GU Xiaohe, SUN Qian, et al. Analysis of the spatial and temporal pattern of changes in abandoned farmland based on long time series of remote sensing data[J]. Remote Sensing, 2021, 13(13): 2549.
Crossref Google Scholar
[37]

ZHU Yingzhen, YU Jifang, WU Jiangqin. Chro-Ring: a time-oriented visual approach to represent writer's history[J]. The Visual Computer, 2016, 32(9): 1133-1149.
Crossref Google Scholar
[38]
ANDRIENKO G, ANDRIENKO N, SCHUMANN H, et al. Visualization of trajectory attributes in space-time cube and trajectory wall[M]//BUCHROITHNER M, PRECHTEL N, BURGHARDT D. Cartography from Pole to Pole. Berlin, Heidelberg: Springer, 2014: 157-163. DOI: 10.1007/978-3-642-32618-9_11.
Crossref
[39]

BU Chuan, ZHANG Quanjie, WANG Qianwen, et al. Sinestream: improving the readability of streamgraphs by minimizing sine illusion effects[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(2): 1634-1643.
Crossref Google Scholar
[40]

GRIFFIN A L, MACEACHREN A M, HARDISTY F, et al. A comparison of animated maps with static small-multiple maps for visually identifying space-time clusters[J]. Annals of the Association of American Geographers, 2006, 96(4): 740-753. DOI: 10.1111/j.1467-8306.2006.00514.x.
Crossref Google Scholar
[41]

BOYANDIN I, BERTINI E, LALANNE D. A qualitative study on the exploration of temporal changes in flow maps with animation and small-multiples[J]. Computer Graphics Forum, 2012, 31(3pt2): 1005-1014. DOI: 10.1111/j.1467-8659.2012.03093.x.
Crossref Google Scholar
[42]

KRISTENSSON P O, DAHLBACK N, ANUNDI D, et al. An evaluation of space time cube representation of spatiotemporal patterns[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(4): 696-702.
Crossref Google Scholar
[43]

HÄGERSTRAAND T. What about people in regional science?[J]. Papers in Regional Science, 1970, 24(1): 7-21.
Crossref Google Scholar
[44]

ANDRIENKO N, ANDRIENKO G, RINZIVILLO S. Exploiting spatial abstraction in predictive analytics of vehicle traffic[J]. ISPRS International Journal of Geo-Information, 2015, 4(2): 591-606.
Crossref Google Scholar
[45]
PETERS S, BETZ H D, MENG Liqiu. Visual analysis of lightning data using space-time-cube[M]//BUCHROITHNER M, PRECHTEL N, BURGHARDT D. Cartography from Pole to Pole. Berlin, Heidelberg: Springer, 2014: 165-176.
Crossref
[46]
HEWAGAMAGE K P, HIRAKAWA M, ICHIKAWA T. Interactive visualization of spatiotemporal patterns using spirals on a geographical map[C]//Proceedings of 1999 IEEE Symposium on Visual Languages. Tokyo, Japan: IEEE, 1999: 296-303.
Crossref
[47]

ZUO Chenyu, GAO Mengyao, DING Linfang, et al. Space-time cube for visual queries over metadata of heterogeneous geodata[J]. KN-Journal of Cartography and Geographic Information, 2022, 72(1): 29-39.
Crossref Google Scholar
Journal of Geodesy and Geoinformation Science
Volume 7 Issue 1,
March 2024
Pages 3-15
DOI: 10.11947/j.JGGS.2024.0102
Cite this article:
ZUO C, XU Y, DING L, et al. ST-Map: an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data. Journal of Geodesy and Geoinformation Science, 2024, 7(1): 3-15. https://doi.org/10.11947/j.JGGS.2024.0102

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Published: 20 March 2024
© 2024 Journal of Geodesy and Geoinformation Science
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