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Visual analytics for spatiotemporal events

  • Ricardo Almeida Silva
  • João Moura Pires
  • Nuno DatiaEmail author
  • Maribel Yasmina Santos
  • Bruno Martins
  • Fernando Birra
Article
  • 46 Downloads

Abstract

Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.

Keywords

Data visualization Spatiotemporal patterns Multiple levels of detail Visual analytics 

Notes

Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e Tecnologia MCTES, UID/CEC/04516/2013 (NOVA LINCS), UID/CEC/00319/2019 (ALGORITMI), and UID/CEC/50021/2019 (INESC-ID).

References

  1. 1.
    Aigner W, Miksch S, Schumann H, Tominski C (2011) Visualization of time-oriented data. Springer Science & Business MediaGoogle Scholar
  2. 2.
    Andrienko G, Andrienko N, Bak P, Keim D, Kisilevich S, Wrobel S (2011) A conceptual framework and taxonomy of techniques for analyzing movement. J Vis Lang Comput 22(3):213–232CrossRefGoogle Scholar
  3. 3.
    Andrienko G, Andrienko N, Bosch H, Ertl T, Fuchs G, Jankowski P, Thom D (2013) Thematic patterns in georeferenced tweets through space-time visual analytics. Comput Sci Eng 15(3):72–82CrossRefGoogle Scholar
  4. 4.
    Andrienko N, Andrienko G (2004) Interactive visual tools to explore spatio-temporal variation. In: Proceedings of the working conference on advanced visual interfaces. ACM, pp 417–420Google Scholar
  5. 5.
    Andrienko N, Andrienko G (2006) Exploratory analysis of spatial and temporal data: a systematic approach. Springer, BerlinzbMATHGoogle Scholar
  6. 6.
    Bédard Y, Rivest S, Proulx MJ (2007) Spatial. online analytical. processing (solap): concepts, architectures, and solutions. Data warehouses and OLAP: concepts, architectures, and solutions, Idea Group Inc, pp 298–319Google Scholar
  7. 7.
    Bertin J, Berg WJ, Wainer H (1983) Semiology of graphics: diagrams, networks, maps, vol 1. University of Wisconsin press MadisonGoogle Scholar
  8. 8.
    Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control, Wiley, New YorkGoogle Scholar
  9. 9.
    Cardoso D, Alves R, Pires JM, Birra F, Silva R (2017) Gisplay-extensible web api for thematic maps with webgl. In: International conference on computational science and its applications. Springer, pp 674–689Google Scholar
  10. 10.
    Chae J, Thom D, Bosch H, Jang Y, Maciejewski R, Ebert DS, Ertl T (2012) Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: 2012 IEEE conference on visual analytics science and technology (VAST). IEEE, pp 143–152Google Scholar
  11. 11.
    Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: a general framework and some examples. Computer 37(4):50–56CrossRefGoogle Scholar
  12. 12.
    Cho I, Dou W, Wang DX, Sauda E, Ribarsky W (2016) Vairoma: a visual analytics system for making sense of places, times, and events in roman history. IEEE Trans Vis Comput Graph 22(1):210–219CrossRefGoogle Scholar
  13. 13.
    Dykes J, MacEachren A, Kraak M (2005) Exploring geovisualization. No vol 1 in International Cartographic Association. Elsevier, AmsterdamGoogle Scholar
  14. 14.
    Ebdon D (1985) Statistics in geography. Blackwell, OxfordGoogle Scholar
  15. 15.
    Ferreira N, Poco J, Vo HT, Freire J, Silva CT (2013) Visual exploration of big spatio-temporal urban data: a study of new york city taxi trips. IEEE Trans Vis Comput Graph 19(12):2149–2158CrossRefGoogle Scholar
  16. 16.
    Forlines C, Wittenburg K (2010) Wakame: sense making of multi-dimensional spatial-temporal data. In: Proceedings of the international conference on advanced visual interfaces. ACM, pp 33–40Google Scholar
  17. 17.
    Fuchs G, Schumann H (2004) Visualizing abstract data on maps. In: 2004 Proceedings. Eighth international conference on information visualisation, 2004. IV. IEEE, pp 139–144Google Scholar
  18. 18.
    Gabriel E (2014) Estimating second-order characteristics of inhomogeneous spatio-temporal point processes. Methodol Comput Appl Probab 16(2):411–431MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Gabriel E, Rowlingson B, Diggle P (2013) stpp: an r package for plotting, simulating and analyzing spatio-temporal point patterns. J Stat Softw 53(2):1–29CrossRefGoogle Scholar
  20. 20.
    Gao Y, Wang S, Padmanabhan A, Yin J, Cao G (2018) Mapping spatiotemporal patterns of events using social media: a case study of influenza trends. Int J Geogr Inf Sci 32(3):425–449CrossRefGoogle Scholar
  21. 21.
    Gatalsky P, Andrienko N, Andrienko G (2004) Interactive analysis of event data using space-time cube. In: 2004 Proceedings. Eighth international conference on information visualisation, 2004. IV. IEEE, pp 145–152Google Scholar
  22. 22.
    Getis A (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24(3):189–206CrossRefGoogle Scholar
  23. 23.
    Goodwin S, Dykes J, Slingsby A, Turkay C (2016) Visualizing multiple variables across scale and geography. IEEE Trans Vis Comput Graph 22(1):599–608CrossRefGoogle Scholar
  24. 24.
    Guo D, Chen J, MacEachren AM, Liao K (2006) A visualization system for space-time and multivariate patterns (vis-stamp). IEEE Trans Vis Comput Graph 12(6):1461–1474CrossRefGoogle Scholar
  25. 25.
    Hadlak S, Tominski C, Schulz HJ, Schumann H (2010) Visualization of attributed hierarchical structures in a spatiotemporal context. Int J Geogr Inf Sci 24 (10):1497–1513CrossRefGoogle Scholar
  26. 26.
    Hering AS, Bell CL, Genton MG (2009) Modeling spatio-temporal wildfire ignition point patterns. Environ Ecol Stat 16(2):225–250MathSciNetCrossRefGoogle Scholar
  27. 27.
    Jacquez GM (1996) A k nearest neighbour test for space–time interaction. Stat Med 15(18):1935–1949CrossRefGoogle Scholar
  28. 28.
    Kapler T, Wright W (2005) Geotime information visualization. Inf Vis 4 (2):136–146CrossRefGoogle Scholar
  29. 29.
    Keim D, Andrienko G, Fekete JD, Gȯrg C, Kohlhammer J, Melançon G (2008) Visual analytics: definition, process, and challenges. In: Kerren A, Stasko J, Fekete JD, North C (eds) Information visualization, lecture notes in computer science, vol 4950. Springer, Berlin, pp 154–175Google Scholar
  30. 30.
    Kisilevich S, Krstajic M, Keim D, Andrienko N, Andrienko G (2010) Event-based analysis of people’s activities and behavior using flickr and panoramio geotagged photo collections. In: 2010 14th international conference information visualisation (IV). IEEE, pp 289–296Google Scholar
  31. 31.
    Knox EG, Bartlett MS (1964) The detection of space-time interactions. Appl Stat 13:25–30CrossRefGoogle Scholar
  32. 32.
    Kraak MJ, Ormeling F (2003) Cartography: visualisation of geospatial data. Essex: Pearson Education LimitedGoogle Scholar
  33. 33.
    Lahouari K, Jean-Yves B, Paule-Annick D, Hélène M, Cécile SM (2014) Représenter les dynamiques des territoires : un état des lieux, de nouveaux enjeux. http://www.map.cnrs.fr/jyb/puca/
  34. 34.
    Leipnik MR, Albert DP (2003) GIS in law enforcement: implementation issues and case studies. CRC Press, LondonGoogle Scholar
  35. 35.
    Li H, Zhang J, Sun J (2016a) A visual analytics approach for deterioration risk analysis of ancient frescoes. J Vis 19(3):529–542CrossRefGoogle Scholar
  36. 36.
    Li M, Bao Z, Sellis T, Yan S, Zhang R (2018) Homeseeker: a visual analytics system of real estate data. J Vis Lang Comput 45:1–16CrossRefGoogle Scholar
  37. 37.
    Li S, Dragicevic S, Castro FA, Sester M, Winter S, Coltekin A, Pettit C, Jiang B, Haworth J, Stein A et al (2016b) Geospatial big data handling theory and methods: a review and research challenges. ISPRS J Photogramm Remote Sens 115:119–133CrossRefGoogle Scholar
  38. 38.
    Lins L, Klosowski JT, Scheidegger C (2013) Nanocubes for real-time exploration of spatiotemporal datasets. IEEE Trans Vis Comput Graph 19(12):2456–2465CrossRefGoogle Scholar
  39. 39.
    MacEachren AM, Jaiswal A, Robinson AC, Pezanowski S, Savelyev A, Mitra P, Zhang X, Blanford J (2011) Senseplace2: Geotwitter analytics support for situational awareness. In: 2011 IEEE conference on visual analytics science and technology (VAST). IEEE, pp 181–190Google Scholar
  40. 40.
    Maciejewski R, Rudolph S, Hafen R, Abusalah A, Yakout M, Ouzzani M, Cleveland WS, Grannis SJ, Ebert DS (2010) A visual analytics approach to understanding spatiotemporal hotspots. IEEE Trans Vis Comput Graph 16(2):205–220CrossRefGoogle Scholar
  41. 41.
    Malik A, Maciejewski R, Collins TF, Ebert DS (2010) Visual analytics law enforcement toolkit. In: 2010 IEEE international conference on technologies for homeland security (HST). IEEE, pp 222–228Google Scholar
  42. 42.
    Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2 Part 1):209–220Google Scholar
  43. 43.
    Miller HJ, Han J (2009) Geographic data mining and knowledge discovery. Chapman & Hall/CRC data mining and knowledge discovery series. CRC Press, LondonGoogle Scholar
  44. 44.
    Møller J, Ghorbani M (2010) Second-order analysis of structured inhomogeneous spatio-temporal point processes. Tech rep., Department of Mathematical Sciences, Aalborg UniversityGoogle Scholar
  45. 45.
    Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23MathSciNetzbMATHCrossRefGoogle Scholar
  46. 46.
    Nelson JK, Brewer CA (2017) Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartogr Geogr Inf Sci 44(1):35–50CrossRefGoogle Scholar
  47. 47.
    Openshaw S (1984) The modifiable areal unit problem. Concepts and techniques in modern geographyGoogle Scholar
  48. 48.
    Ostfeld RS, Glass GE, Keesing F (2005) Spatial epidemiology: an emerging (or re-emerging) discipline. Trends Ecol Evol 20(6):328–336CrossRefGoogle Scholar
  49. 49.
    Robinson AC, Peuquet DJ, Pezanowski S, Hardisty FA, Swedberg B (2016) Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data. Cartogr Geogr Inf Sci 44:1–13Google Scholar
  50. 50.
    Roddick JF, Spiliopoulou M (1999) A bibliography of temporal, spatial and spatio-temporal data mining research. ACM SIGKDD Explorations Newsletter 1 (1):34–38zbMATHCrossRefGoogle Scholar
  51. 51.
    Scherr M (2008) Multiple and coordinated views in information visualization. Trends in Information Visualization 38:1–33Google Scholar
  52. 52.
    Shanbhag P, Rheingans P et al (2005) Temporal visualization of planning polygons for efficient partitioning of geo-spatial data. In: 2005 IEEE symposium on information visualization, 2005. INFOVIS, IEEE, pp 211–218Google Scholar
  53. 53.
    Shekhar S, Jiang Z, Ali RY, Eftelioglu E, Tang X, Gunturi V, Zhou X (2015) Spatiotemporal data mining: a computational perspective. ISPRS Int J Geo Inf 4(4):2306–2338CrossRefGoogle Scholar
  54. 54.
    Silva R, Moura-Pires J, Santos MY (2012) Spatial clustering in SOLAP systems to enhance map visualization. Int J Data Warehouse Min 8(2):23–43CrossRefGoogle Scholar
  55. 55.
    Silva R, Pires JM, Santos MY, Datia N (2016) Enhancing exploratory analysis by summarizing spatiotemporal events across multiple levels of detail. In: Sarjakoski T, Santos MY, Sarjakoski TL (eds) Geospatial data in a changing world, selected papers of the 19th AGILE conference on geographic information science, Lecture Notes in Geoinformation and Cartography, Springer.  https://doi.org/10.1007/978-3-319-33783-8_13, https://link.springer.com/chapter/10.1007/978-3-319-33783-8_13
  56. 56.
    Silva RA, Pires JM, Santos MY (2015a) A granularity theory for modelling spatio-temporal phenomena at multiple levels of detail. International Journal of Business Intelligence and Data Mining 10(1):33CrossRefGoogle Scholar
  57. 57.
    Silva RA, Pires JM, Santos MY, Leal R (2015b) Aggregating spatio-temporal phenomena at multiple levels of detail. In: AGILE 2015, Springer Science ∖mathplus business media, pp 291–308Google Scholar
  58. 58.
    Sips M, Kȯthur P, Unger A, Hege HC, Dransch D (2012) A visual analytics approach to multiscale exploration of environmental time series. IEEE Trans Vis Comput Graph 18(12):2899–2907CrossRefGoogle Scholar
  59. 59.
    Swedberg B, Peuquet D (2016) Perse visual analytics for calendar related spatiotemporal periodicity detection and analysis. GeoInformatica 21:1–21Google Scholar
  60. 60.
    Swedberg B, Peuquet D (2017) An evaluation of a visual analytics prototype for calendar-related spatiotemporal periodicity detection and analysis. Cartographica: The International Journal for Geographic Information and Geovisualization 52(1):63–79CrossRefGoogle Scholar
  61. 61.
    Thakur S, Rhyne TM (2009) Data vases: 2d and 3d plots for visualizing multiple time series. In: International symposium on visual computing. Springer, pp 929–938Google Scholar
  62. 62.
    Thom D, Bosch H, Koch S, Wörner M, Ertl T (2012) Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In: 2012 IEEE Pacific visualization symposium (pacificvis), IEEE, pp 41–48Google Scholar
  63. 63.
    Tominski C, Schulz HJ (2012) The great wall of space-time. In: Goesele M, Grosch T, Theisel H, Toennies K, Preim B (eds) Vision, modeling and visualization, the Eurographics association.  https://doi.org/10.2312/PE/VMV/VMV12/199-206
  64. 64.
    Tominski C, Schulze-Wollgast P, Schumann H (2005) 3d information visualization for time dependent data on maps. In: Ninth international conference on information visualisation, 2005. Proceedings. IEEE, pp 175–181Google Scholar
  65. 65.
    Tversky B, Morrison JB, Betrancourt M (2002) Animation: can it facilitate? Int J Hum Comput Stud 57(4):247–262CrossRefGoogle Scholar
  66. 66.
    Wang D, Ding W, Lo H, Morabito M, Chen P, Salazar J, Stepinski T (2013) Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns. Comput Environ Urban Syst 39:93–106CrossRefGoogle Scholar
  67. 67.
    Weaver C (2010) Cross-filtered views for multidimensional visual analysis. IEEE Trans Vis Comput Graph 16(2):192–204CrossRefGoogle Scholar
  68. 68.
    Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Transactions on Cybernetics 43(6):1977–1989CrossRefGoogle Scholar
  69. 69.
    Yin J, Gao Y, Du Z, Wang S (2016) Exploring multi-scale spatiotemporal twitter user mobility patterns with a visual-analytics approach. ISPRS Int J Geo Inf 5 (10):187CrossRefGoogle Scholar
  70. 70.
    Zhang L, Stoffel A, Behrisch M, Mittelstadt S, Schreck T, Pompl R, Weber S, Last H, Keim D (2012) Visual analytics for the big data era—a comparative review of state-of-the-art commercial systems. In: 2012 IEEE conference on visual analytics science and technology (VAST). IEEE, pp 173–182Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.ISELInstituto Politécnico de LisboaLisbonPortugal
  2. 2.NOVA LINCS, FCTUniversidade NOVA de LisboaLisbonPortugal
  3. 3.ALGORITMI Research CentreUniversity of MinhoBragaPortugal
  4. 4.INESC-ID and ISTUniversity of LisbonLisbonPortugal

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