, Volume 21, Issue 3, pp 577–597 | Cite as

PerSE: visual analytics for calendar related spatiotemporal periodicity detection and analysis

  • Brian Swedberg
  • Donna Peuquet


Periodicity is embedded in all societies. As most of us organize our lives based on temporal structures, it is hard to imagine what life would be like without it. We experience periodicity through naturally occurring rhythms that exist in nature, such as sunrise/sunset, seasonal changes in the weather, and the tides. We also experience it through abstract means via cultural, political, religious ties, such as the weekend, Independence Day, and Ramadan. Forms of periodicity, like the examples above, are foundational to making sense of human activity because they provide contextual rationale and frame normaility. However, disparate calendars (e.g. Islamic vs. Gregorian), localized idiosyncrasies, and other variables greatly complicate the analytical ability to uncover and understand human activity at a given time within a specified region. We have developed PerSE (Periodicity in Spatiotemporal Events): a web application designed to aid users in the detection and analysis of calendar related periodicity in spatiotemporal event data sets via exploratory user interaction. PerSE is composed of several crossfiltering views: the Map, Attribute View, Time-Wheel, Timeline, and Table. Users interactively set and release filters on one or more of the views to detect and analyze calendar related periodicity. This paper illustrates the utility of PerSE through an in-depth description of the tool and proof of concept usage example.


Geovisualization Geovisual analytics Spatiotemporal Periodicity Calendar 


  1. 1.
    Aigner W, Miksch S, Muller W, Schumann H, Tominski C (2008) Visual methods for analyzing time-oriented data. IEEE Trans Vis Comput Graph 14(1):47–60CrossRefGoogle Scholar
  2. 2.
    Andrienko G, Andrienko N (2008) Spatio-temporal aggregation for visual analysis of movements. In: IEEE Symposium on Visual Analytics Science and Technology, pp 51–58. IEEEGoogle Scholar
  3. 3.
    Andrienko G, Andrienko NV (1999) Interactive maps for visual data exploration. Int J Geogr Inf Sci 13(4):355–374CrossRefGoogle Scholar
  4. 4.
    Andrienko N, Andrienko G, Gatalsky P (2003) Exploratory spatio-temporal visualization: an analytical review. J Vis Lang Comput 14(6):503–541CrossRefGoogle Scholar
  5. 5.
    Army U (2006) FM 3-24 counterinsurgency. Headquarters of the Army, Washington, DCGoogle Scholar
  6. 6.
    Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv (CSUR) 23(3):345–405CrossRefGoogle Scholar
  7. 7.
    Bak P, Mansmann F, Janetzko H, Keim D (2009) Spatiotemporal analysis of sensor logs using growth ring maps. IEEE Trans Vis Comput Graph 15(6):913–920CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Bostock M (2015) D3.
  10. 10.
    Chmaytelli M, Kalin S, Abdelaty A (2016) Islamic state calls for attacks on the west during ramadan in audio message. ReutersGoogle Scholar
  11. 11.
    Cook KA, Thomas JJ (eds) (2005) Illuminating the path: The research and development agenda for visual analytics. Pacific Northwest National LaboratoryGoogle Scholar
  12. 12.
    Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PloS one 6(12)Google Scholar
  13. 13.
    Edsall R, Peuquet D (1997) A graphical user interface for the integration of time into gis. In: Proceedings of the 1997 American Congress of Surveying and Mapping Annual Convention and Exhibition, Seattle, WA, pp 182–189Google Scholar
  14. 14.
    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
  15. 15.
    Havre S, Hetzler B, Nowell L (2000) Themeriver: Visualizing theme changes over time. IEEE Symposium on Information Visualization:115–123Google Scholar
  16. 16.
    Homeland Security Studies and Analysis Institute (2014) Project responder 4: 2014 national technology plan for emergency response to catastrophic incidents, pp 50–60; 70–88Google Scholar
  17. 17.
    International Federation of Red Cross and Red Crescent Societies (2013) World disasters report: Focus on technology and the future of humanitarian action pp 73–101Google Scholar
  18. 18.
    International Organization for Standardization (2004) ISO 8601: Date and time formatGoogle Scholar
  19. 19.
    jQuery Foundation (2015) jQuery.
  20. 20.
    Kapler T, Wright W (2009) Geotime information visualization. Inf Vis 4 (2):136–146CrossRefGoogle Scholar
  21. 21.
    Keim D, Andrienko G, Fekete JD, Görg C, Kohlhammer J, Melançon G (2008) Visual analytics: Definition, process, and challenges. SpringerGoogle Scholar
  22. 22.
    Lahneman WJ (2006) The future of intelligence analysis, volume i final report. College Park: The Center for International and Security Studies at MarylandGoogle Scholar
  23. 23.
    Lammarsch T, Aigner W, Bertone A, Gartner J, Mayr E, Miksch S, Smuc M (2009) Hierarchical temporal patterns and interactive aggregated views for pixel-based visualizations. In: 2009 13th International Conference Information Visualisation, pp 44–50. IEEEGoogle Scholar
  24. 24.
    Lieberthal K (2009) The US intelligence community and foreign policy: Getting analysis right. Brookings InstitutionGoogle Scholar
  25. 25.
    Lipka M, Ghani F (2013) Muslim holiday of ashura brings into focus shia-sunni differences. Pew Research Center
  26. 26.
    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), pp 181–190. IEEEGoogle Scholar
  27. 27.
    Malik A, Maciejewski R, Elmqvist N, Jang Y, Ebert DS, Huang W (2012) A correlative analysis process in a visual analytics environment. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp 33–42. IEEEGoogle Scholar
  28. 28.
    MapBox LLC (2015) Mapbox.
  29. 29.
    Mintz D, Fitz-Simons T, Wayland M (1997) Tracking air quality trends with sas/graph. In: Proceedings of the 22nd Annual SAS User Group International Conference, SAS, pp 807–812Google Scholar
  30. 30.
    Mughal MAZ (2014) Calendars tell history: Social rhythm and social change in rural Pakistan. Hist Anthropol 25(5):592–613CrossRefGoogle Scholar
  31. 31.
    Munn ND (1992) The cultural anthropology of time: A critical essay. Annu Rev Anthropol 21:93–123CrossRefGoogle Scholar
  32. 32.
    National Primary Health Care Development Agency (2013) 2014 nigeria polio eradication emergency planGoogle Scholar
  33. 33.
    Nir SM (2014) At lunar new year, a big, red target for pickpockets. New York Times
  34. 34.
    OpenLayers Contributors (2015) OpenLayers 3.
  35. 35.
    OpenStreetMap Foundation (2015) OpenStreetMap.
  36. 36.
    Pelekis N, Theodoulidis B, Kopanakis I, Theodoridis Y (2004) Literature review of spatio-temporal database models. Knowl Eng Rev 19(3):235–274CrossRefGoogle Scholar
  37. 37.
    Peuquet D (1994) It’s about time: A conceptual framework for the representation of temporal dynamics in geographic information systems. Ann Assoc Am Geogr 84(3):441–461CrossRefGoogle Scholar
  38. 38.
    Raleigh C, Linke A, Hegre H, Karlsen J (2010) Introducing acled-armed conflict location and event data. J Peace Res 47(5):1–10CrossRefGoogle Scholar
  39. 39.
    Razip A, Malik A, Afzal S, Potrawski M, Maciejewski R, Jang Y, Elmqvist N, Ebert DS (2014) A mobile visual analytics approach for law enforcement situation awareness. In: Pacific Visualization Symposium, IEEE, pp 169–176Google Scholar
  40. 40.
    Richards EG (1999) Mapping time: the calendar and its history. Oxford university pressGoogle Scholar
  41. 41.
    Roth RE (2011) Interacting with maps: The science and practice of cartographic interaction. PhD thesis, The Pennsylvania State UniversityGoogle Scholar
  42. 42.
    Shneiderman B (1996) The eyes have it: A task by data type taxonomy for information visualizations. In: IEEE Symposium on Visual Languages. IEEEGoogle Scholar
  43. 43.
    Square Inc (2015) Crossfilter.
  44. 44.
    Sum All and Humanitarian Tracker (2015) Analysis of syria killings.
  45. 45.
    Tominski C, Schumann H (2008) Enhanced interactive spiral display. In: Proceedings of the Annual SIGRAD Conference – Special Theme: Interactivity, pp 53–56Google Scholar
  46. 46.
    Tufte ER (1990) Envisioning Information Graphics Press LLC, Cheshire, CTGoogle Scholar
  47. 47.
    Twitter Inc (2015) Bootstrap.
  48. 48.
    Van Wijk JJ, Van Selow ER (1999) Cluster and calendar based visualization of time series data. In: Proceedings Information Visualization, Symposium on IEEE, pp 4–9Google Scholar
  49. 49.
    Weaver C (2008) Multidimensional visual analysis using cross-filtered views. In: IEEE Symposium on Visual Analytics Science and Technology, pp 163–170. IEEE Google Scholar
  50. 50.
    Weaver C, Fyfe D, Robinson A, Holdsworth D, Peuquet D, MacEachren AM (2007) Visual exploration and analysis of historic hotel visits. Inf Vis 6(1):89–103CrossRefGoogle Scholar
  51. 51.
    Wickham H, Hofmann H, Wickham C, Cook D (2012) Glyph-maps for visually exploring temporal patterns in climate data and models. Environmetrics 23(5):382–393CrossRefGoogle Scholar
  52. 52.
    Wood K (2015) jQuery Calendars Plugin.
  53. 53.
    Zacks JM, Tversky B (2001) Event structure in perception and conception. Psychol Bull 127(1):3–21CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations