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Abstract
With the advent of online gaming, access to in-game data has become increasingly important for players as it provides great opportunities for them to reflect and improve upon their gameplay or to compare their performance with others. Some of the currently most popular games focus on strategy and tactics, requiring players to skillfully position and maneuver units in order to achieve victory in battle. However, current visualizations for retrospective analysis of battles and that are targeted toward players are mainly limited to heatmaps and hence are not well suited for conveying the flow of battle. By contrast, military planners and historians alike have long used maps to provide a concise visual overview of troop movements. In this article, we are proposing an algorithm for automatically creating such battle maps from tracked in-game data. Several parameters allow to adjust the level-of-detail in the resulting maps. To demonstrate the practicality of our approach for post hoc analysis, we apply it to actual gameplay data obtained from a massively multiplayer online game and collected preliminary feedback among players of the game through an online survey.
References
| 1. |
Wallner, G, Kriglstein, S. Visualization-based analysis of gameplay data: a review of literature. Entertain Comput 2013; 4(3): 143–155. Google Scholar | Crossref |
| 2. |
Medler, B. Player dossiers: analyzing gameplay data as a reward. Int J Comput Game Res 2011; 11, http://gamestudies.org/1101/articles/medler Google Scholar |
| 3. |
Bungie Inc . Destiny API, https://www.bungie.net/platform/destiny/help/ (accessed January 2016). Google Scholar |
| 4. |
Riot Games . Riot Games API, https://developer.riotgames.com/api-methods/(accessed January 2016). Google Scholar |
| 5. | Andras Belicza Scelight , https://sites.google.com/site/scelight/ (accessed January 2016). Google Scholar |
| 6. |
Czyz, M . vBAddict, http://www.vbaddict.net/replays (accessed January 2016). Google Scholar |
| 7. |
Bowman, B, Elmqvist, N, Jankun-Kelly, T. Toward visualization for games: theory, design space, and patterns. IEEE T Vis Comput Gr 2012; 18(11): 1956–1968. Google Scholar | Crossref | Medline |
| 8. |
Medler, B, Magerko, B. Analytics of play: using information visualization and gameplay practices for visualizing video game data. Parsons J Inform Map 2011; 3: 1–12. Google Scholar |
| 9. |
Zammitto, V . Visualization techniques in video games. In: Proceedings of EVA, London, UK, 22–24 July 2008, pp. 267–276. BCS: The Chartered Institute for IT. Google Scholar |
| 10. |
Firaxis Games . Sid Meier’s Gettysburg (PC). Baltimore, MD: Firaxis Games, 1997. Google Scholar |
| 11. |
Fireglow Games . Sudden Strike II (PC). 2002 Bruchsal, Germany: Cdv Software Entertainment. Google Scholar |
| 12. |
Paradox Interactive . Hearts of Iron 3 (PC). Stockholm: Paradox Interactive, 2009. Google Scholar |
| 13. |
Paradox Interactive . Hearts of Iron 4 (PC). Stockholm: Paradox Interactive, 2016. Google Scholar |
| 14. |
Game-Labs . Ultimate General Gettysburg (PC). 2014. Game-Labs. Google Scholar |
| 15. |
Wargaming . World of Tanks (PC). Nicosia, Cyprus: Wargaming, 2010. Google Scholar |
| 16. |
Wallner, G, Kriglstein, S. Visualizations for retrospective analysis of battles in team-based combat games: a user study. In: Proceedings of the 2016 annual symposium on computer-human interaction in play, CHI PLAY ‘16, pp. 22–32. ACM. Google Scholar |
| 17. |
Library of Congress . Military battles and campaigns, http://www.loc.gov/collections/military-battles-and-campaigns/ (accessed January 2016). Google Scholar |
| 18. |
Davis, GB, Perry, LJ, Kirkley, JW. The official military atlas of the civil war. New York: Barnes & Noble, 2003. Google Scholar |
| 19. |
Demsar, U, Buchin, K, Cagnacci, F. Analysis and visualisation of movement: an interdisciplinary review. Mov Ecol 2015; 3(1): 1–24. Google Scholar | Crossref | Medline |
| 20. |
Andrienko, N, Andrienko, G. Spatial generalization and aggregation of massive movement data. IEEE T Vis Comput Gr 2011; 17(2): 205–219. Google Scholar | Crossref | Medline | ISI |
| 21. |
Janetzko, H, Jäckle, D, Deussen, O. Visual abstraction of complex motion patterns. In: Proceedings of SPIE, visualization and data analysis, San Francisco, CA, 2 February 2014, vol. 9017. Google Scholar |
| 22. |
Kisilevich, S, Mansmann, F, Nanni, M. Spatio-temporal clustering. In: Maimon, O, Rokach, L (eds) Data mining and knowledge discovery handbook. New York: Springer, 2010, pp. 855–874. Google Scholar |
| 23. |
Parent, C, Spaccapietra, S, Renso, C. Semantic trajectories modeling and analysis. ACM Comput Surv 2013; 45(4): 42: 1–42. Google Scholar | Crossref |
| 24. |
Ying, JJC, Lu, EHC, Lee, WC. Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, San Jose, CA, 2 November 2010, pp. 19–26. New York: ACM. Google Scholar |
| 25. |
Liu, H, Schneider, M. Similarity measurement of moving object trajectories. In: Proceedings of the third ACM SIGSPATIAL international workshop on GeoStreaming, Redondo Beach, CA, 6 November 2012, pp. 19–22. New York: ACM. Google Scholar |
| 26. |
Tufte, E . The visual display of quantitative information. Cheshire, CT: Graphics Press, 2001. Google Scholar |
| 27. |
Phan, D, Xiao, L, Yeh, R. Flow map layout. In: Proceedings of the IEEE symposium on information visualization, Minneapolis, MN, 23–25 October 2005. Washington, DC: IEEE Computer Society, pp. 219–224. Google Scholar |
| 28. |
Verbeek, K, Buchin, K, Speckmann, B. Flow map layout via spiral trees. IEEE T Vis Comput Gr 2011; 17(12): 2536–2544. Google Scholar | Crossref | Medline | ISI |
| 29. |
Börner, K, Penumarthy, S. Social diffusion patterns in three-dimensional virtual worlds. Inform Visual 2003; 2: 182–198. Google Scholar | SAGE Journals |
| 30. |
Chittaro, L, Ranon, R, Ieronutti, L. VU-Flow: a visualization tool for analyzing navigation in virtual environments. IEEE T Vis Comput Gr 2006; 12(6): 1475–1485. Google Scholar | Crossref | Medline |
| 31. |
McLoughlin, T, Jones, MW, Laramee, RS. Similarity measures for enhancing interactive streamline seeding. IEEE T Vis Comput Gr 2013; 19(8): 1342–1353. Google Scholar | Crossref | Medline |
| 32. |
Hoobler, N, Humphreys, G, Agrawala, M. Visualizing competitive behaviors in multi-user virtual environments. In: Proceedings of VIS, Austin, Texas, 10–15 October 2004, pp. 163–170. Washington, DC: IEEE Computer Society. Google Scholar |
| 33. |
Gagné, AR, Seif El-Nasr, M, Shaw, CD. Analysis of telemetry data from a real-time strategy game: a case study. Comput Entertain 2012; 10(1): 2.1–2.25. Google Scholar |
| 34. |
Mitterhofer, S, Kruegel, C, Kirda, E. Server-side bot detection in massively multiplayer online games. Secur Priv 2009; 7(3): 29–36. Google Scholar | Crossref | ISI |
| 35. |
Miller, JL, Crowcroft, J. Group movement in World of Warcraft battlegrounds. Int J Adv Media Commun 2010; 4(4): 387–404. Google Scholar | Crossref |
| 36. |
Thawonmas, R, Kurashige, M, Chen, KT. Detection of landmarks for clustering of online-game players. The Int J Virt Real 2007; 6: 11–16. Google Scholar |
| 37. |
Chen, Y, Jiang, K, Zheng, Y. Trajectory simplification method for location-based social networking services. In: Proceedings of the international workshop on location based social networks, Seattle, WA, 4–6 November 2009, pp. 33–40. New York: ACM. Google Scholar |
| 38. |
Andrienko, GL, Andrienko, NV, Bak, P. Visual analytics of movement. Berlin: Springer, 2013. Google Scholar | Crossref |
| 39. | Kalith , WOT Tactics, http://wottactic.com/ (accessed June 2017). Google Scholar |
| 40. |
StratSketch: Pyratron Studios . Stratsketch, https://www.pyratron.com/projects/stratsketch (accessed January 2016). Google Scholar |
| 41. |
Ester, M, Kriegel, HP, Sander, J. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E, Han, J, Fayyad, U (eds) 2nd international conference on knowledge discovery and data mining. Palo Alto, CA: AAAI Press, 1996, pp. 226–231. Google Scholar |
| 42. |
Birant, D, Kut, A. ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl Eng 2007; 60(1): 208–221. Google Scholar | Crossref |
| 43. |
Wagner, RA, Fischer, MJ. The string-to-string correction problem. J ACM 1974; 21(1): 168–173. Google Scholar | Crossref | ISI |
| 44. |
Andrienko, N, Andrienko, G, Barrett, L. Space transformation for understanding group movement. IEEE T Vis Comput Gr 2013; 19(12): 2169–2178. Google Scholar | Crossref | Medline | ISI |
| 45. |
Electronic Sports League . Wargaming.net league Europe bronze series, http://play.eslgaming.com/worldoftanks/europe/wot/open/bronze-series/ (accessed January 2016). Google Scholar |
| 46. |
Temmerman, J . World of Tanks replay parser, https://github.com/evido/wotreplay-parser (accessed January 2016). Google Scholar |
| 47. |
Schreier, M . Qualitative content analysis in practice. Thousand Oaks, CA: SAGE, 2012. Google Scholar |
