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Visual analytical tools for multivariate higher-order information for emergency management

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Abstract

Higher-order information of moving objects is of great importance for processing in-case scenarios in emergency management. Multivariate higher-order information management is a crucial key to the success of emergency management since emergency management involves developing plans with a given set of multiple resources. Past studies focus on univariate higher-order information limiting the scope of applicability and usability. This paper proposes a set of visual analytical approaches supporting multivariate higher-order information for dynamically moving disasters. We introduce a robust Voronoi-based data structure supporting multivariate datasets and dynamic disasters and propose visual analytical approaches for effective emergency management. The proposed visual analytical suite facilitates interactivity and enables users to explore in-case scenarios with multivariate datasets and dynamic disasters. A case study with real datasets is given to explain the applicability, usability and practicability of the proposed system.

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References

  • Aboagye-Sarfo P, Mai Q, Sanfilippo FM, Preen DB, Stewart LM, Fatovich DM (2015) A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. J Biomed Inf 57:62–73

    Article  Google Scholar 

  • Andrienko GL, Andrienko NV, Dykes J, Fabrikant SI, Wachowicz M (2008) Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inf Vis 7(3–4):173–180

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Burch M, Weiskopf D (2012) Visual analytics methodology for eye movement studies. IEEE Trans Vis Comput Graph 18(12):2889–2898

    Article  Google Scholar 

  • Buschmann S, Trapp M, Döllner J (2016) Animated visualization of spatial–temporal trajectory data for air-traffic analysis. Vis Comput 32(3):371–381

    Article  Google Scholar 

  • Chu D, Sheets D, Zhao Y, Wu Y, Yang J, Zheng M, Chen G (2014) Visualizing hidden themes of taxi movement with semantic transformation. In: 2014 IEEE Pacific visualization symposium (PacificVis). IEEE, pp 137–144

  • Cornel D, Konev A, Sadransky B, Horváth Z, Brambilla A, Viola I, Waser J (2016) Composite flow maps. Comput Graph Forum 35(3):461–470

    Article  Google Scholar 

  • Crnovrsanin T, Muelder C, Correa C, Ma K (2009) Proximity-based visualization of movement trace data. In: 2009 IEEE symposium on visual analytics science and technology, pp 11–18

  • Dusse F, Júnior PS, Alves AT, Novais R, Vieira V, Mendonça M (2016) Information visualization for emergency management: a systematic mapping study. Expert Syst Appl 45:424–437

    Article  Google Scholar 

  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD’96. AAAI Press, pp 226–231

  • Gonçalves P (2011) Balancing provision of relief and recovery with capacity building in humanitarian operations. Oper Manag Res 4(1–2):39–50

    Article  Google Scholar 

  • Gonçalves T, Afonso AP, Martins B (2015) Cartographic visualization of human trajectory data: overview and analysis. J Locat Based Serv 9(2):138–166

    Article  Google Scholar 

  • Haddow G, Bullock J, Coppola DP (2013) Introduction to emergency management. Butterworth-Heinemann, Oxford

    Google Scholar 

  • Hamid AHBA, Rozan MZA, Ibrahim R, Deris S, Selamat A, Yunus MNM (2015) Requirements engineering of Malaysia’s radiation and nuclear emergency plan simulator. Springer, Cham, pp 83–97

    Google Scholar 

  • Handcock RN, Swain DL, Bishop-Hurley GJ, Patison KP, Wark T, Valencia P, Corke P, O’Neill CJ (2009) Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9(5):3586–3603

    Article  Google Scholar 

  • Hashemi Beni L, Mostafavi MA, Pouliot J, Gavrilova M (2011) Toward 3d spatial dynamic field simulation within GIS using kinetic Voronoi diagram and Delaunay tetrahedralization. Int J Geograph Inf Sci 25(1):25–50

    Article  Google Scholar 

  • Heinrich J, Weiskopf D (2013) State of the art of parallel coordinates. In: STAR proceedings of Eurographics, pp 95–116

  • Huang X, Zhao Y, Ma C, Yang J, Ye X, Zhang C (2016) Trajgraph: a graph-based visual analytics approach to studying urban network centralities using taxi trajectory data. IEEE Trans Vis Comput Graph 22(1):160–169

    Article  Google Scholar 

  • Keim D, Andrienko G, Fekete J-D, Görg C, Kohlhammer J, Melançon G (2008) Visual analytics: definition, process, and challenges. Springer, Berlin

    Google Scholar 

  • Lee I, Lee K (2009) A generic triangle-based data structure of the complete set of higher order Voronoi diagrams for emergency management. Comput Environ Urban Syst 33(2):90–99

    Article  Google Scholar 

  • Lee I, Pershouse R, Phillips P, Christensen C (2007) What-if emergency management system: a generalized Voronoi diagram approach. In: Pacific-Asia workshop on intelligence and security informatics. Springer, pp 58–69

  • Lee I, Torpelund-Bruin C, Lee K (2012) Map segmentation for geospatial data mining through generalized higher-order Voronoi diagrams with sequential scan algorithms. Expert Syst Appl 39(12):11135–11148

    Article  Google Scholar 

  • Lin L, Lu Y, Pan Y, Chen X (2012) Integrating graph partitioning and matching for trajectory analysis in video surveillance. IEEE Trans Image Process 21(12):4844–4857

    Article  MathSciNet  Google Scholar 

  • Morris BT, Trivedi MM (2008) A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans Circuits Syst Video Technol 18(8):1114–1127

    Article  Google Scholar 

  • Okabe A, Boots B, Sugihara K (1992) Spatial tessellations: concepts and applications of Voronoi diagrams. Wiley, New York

    MATH  Google Scholar 

  • Okabe A, Boots B, Sugihara K, Chiu SN (2009) Spatial tessellations: concepts and applications of Voronoi diagrams, vol 501. Wiley, New York

    MATH  Google Scholar 

  • Palmer JD (2006) Using line and texture to visualize higher-order voronoi diagrams. In: 3rd international symposium on Voronoi diagrams in science and engineering, ISVD 2006, Banff, Alberta, Canada, July 2–5, 2006, pp 166–172

  • Palmieri F, Ficco M, Pardi S, Castiglione A (2016) A cloud-based architecture for emergency management and first responders localization in smart city environments. Comput Electr Eng 56:810–830

    Article  Google Scholar 

  • Popa MC, Rothkrantz LJ, Shan C, Gritti T, Wiggers P (2013) Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. Pattern Recognit Lett 34(7):809–819

    Article  Google Scholar 

  • Saoud MS, Boubetra A, Attia S (2017) A multi-agent based modeling and simulation data management and analysis system for the hospital emergency department. IJHISI 12(3):21–36

    Google Scholar 

  • Shalabi LA, Shaaban Z (2006) Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: 2006 international conference on dependability of computer systems (DepCoS-RELCOMEX 2006), 24–28 May 2006. Szklarska Poreba, Poland, pp 207–214

  • Shan S, Shi J, Yan Q (2017) Blog recommendation and management implications in an emergency context: an information entropy perspective. Asia-Pac J Oper Res 34(01):1740007

    Article  MathSciNet  Google Scholar 

  • Sun G-D, Wu Y-C, Liang R-H, Liu S-X (2013) A survey of visual analytics techniques and applications: state-of-the-art research and future challenges. J Comput Sci Technol 28(5):852–867

    Article  Google Scholar 

  • Telea A, van Wijk JJ (2001) Visualization of generalized Voronoi diagrams. In: Proceedings of the 2001 Joint Eurographics and IEEE TCVG symposium on visualization, VisSym 2001, Ascona, Switzerland, May 28–30, 2001, pp 165–174

  • Wang Y, Lee I (2014) Visual analytics of higher order information for trajectory datasets. World Acad Sci Eng Technol Int J Environ Chem Ecol Geol Geophys Eng 7(12):966–971

    Google Scholar 

  • Wang Y, Lee K, Lee I (2014a) Directional higher order information for spatio-temporal trajectory dataset. In: 2014 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 35–42

  • Wang Y, Lee K, Lee I (2014b) Visual analytics of topological higher order information for emergency management based on tourism trajectory datasets. Proc Comput Sci 29:683–691

    Article  Google Scholar 

  • Wang Y, Lee K, Lee I (2016a) Multivariate higher order information for emergency management based on tourism trajectory datasets. In: Trends in applied knowledge-based systems and data science—29th international conference on industrial engineering and other applications of applied intelligent systems, IEA/AIE 2016, Morioka, Japan, August 2–4, 2016, proceedings, pp 725–736

  • Wang Y, Lee K, Lee I (2016b) Visual analytical tool for higher order k-means clustering for trajectory data mining. In: Australasian joint conference on artificial intelligence. Springer, pp 507–518

  • Wang D, Guo D, Zhang H (2017) Spatial temporal data visualization in emergency management: a view from data-driven decision. In: Proceedings of the 3rd ACM SIGSPATIAL workshop on emergency management using, EM-GIS’17, New York, NY, USA. ACM, pp 8:1–8:7

  • Xiong X, Mokbel MF, Aref WG (2005) Sea-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: 21st international conference on data engineering (ICDE’05). IEEE, pp 643–654

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Authors and Affiliations

  1. Discipline of Computer Science and Information Technology, College of Science and Engineering, James Cook University, PO Box 6811, Cairns, QLD, 4870, Australia

    Ye Wang, Kyungmi Lee & Ickjai Lee

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  1. Ye Wang
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  2. Kyungmi Lee
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  3. Ickjai Lee
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Correspondence to Ickjai Lee.

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Wang, Y., Lee, K. & Lee, I. Visual analytical tools for multivariate higher-order information for emergency management. J Vis 23, 721–743 (2020). https://doi.org/10.1007/s12650-020-00645-y

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