Spatial Game Analytics

Perhaps the most beloved visualization of player behavior is the heatmap, which offers clear and intuitive feedback about the spatial behavior of players. Heatmaps are, however, only the tip of a very deep iceberg of the area – we here will refer to as spatial game analytics – and it has a lot more to offer than heatmaps, not the least a strong explanatory power for deciphering and understanding player behavior. Here we take a plunge into these deep waters, exploring what is already being done and what can be done within this area and to a lesser degree visualization – which is further explored in the following chapters ( Chaps. 18 and 19).

[1]  Katherine Isbister,et al.  Game Usability: Advancing the Player Experience , 2008 .

[2]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[3]  Magy Seif El-Nasr,et al.  Analysis of telemetry data from a real-time strategy game: A case study , 2012, CIE.

[4]  Maneesh Agrawala,et al.  Visualizing competitive behaviors in multi-user virtual environments , 2004, IEEE Visualization 2004.

[5]  Jon Crowcroft,et al.  Avatar movement in World of Warcraft battlegrounds , 2009, 2009 8th Annual Workshop on Network and Systems Support for Games (NetGames).

[6]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[7]  H. Pao,et al.  Game Bot Detection via Avatar Trajectory Analysis , 2010, IEEE Transactions on Computational Intelligence and AI in Games.

[8]  Bruce Phillips,et al.  Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems , 2008, CHI.

[9]  Magy Seif El-Nasr,et al.  Visualizing and understanding players' behavior in video games: discovering patterns and supporting aggregation and comparison , 2011, SIGGRAPH 2011.

[10]  Neil Hamilton.  The engine room. , 2003, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[11]  Michael Mateas,et al.  A data mining approach to strategy prediction , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[12]  Alessandro Canossa,et al.  Analyzing spatial user behavior in computer games using geographic information systems , 2009, MindTrek '09.

[13]  Ki-Joune Li,et al.  Spatial and spatiotemporal analysis of soccer , 2011, GIS.

[14]  Shashi Shekhar,et al.  A Unified Approach to Detecting Spatial Outliers , 2003, GeoInformatica.

[15]  Ruck Thawonmas,et al.  MMOG Player Classification Using Hidden Markov Models , 2004, ICEC.

[16]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.

[17]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[18]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[19]  Chang-Tien Lu,et al.  Algorithms for spatial outlier detection , 2003, Third IEEE International Conference on Data Mining.

[20]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[21]  Alessandro Canossa,et al.  Arrrgghh!!!: blending quantitative and qualitative methods to detect player frustration , 2011, FDG.

[22]  Christian Bauckhage,et al.  Combining Self Organizing Maps and Multilayer Perceptrons to Learn Bot-Behavior for a Commercial Computer Game , 2003 .

[23]  Michael John,et al.  Data cracker: developing a visual game analytic tool for analyzing online gameplay , 2011, CHI.

[24]  Ruck Thawonmas,et al.  Detection of MMORPG bots based on behavior analysis , 2008, ACE '08.

[25]  Georgios N. Yannakakis,et al.  Player modeling using self-organization in Tomb Raider: Underworld , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[26]  Alessandro Canossa,et al.  Evaluating motion: spatial user behaviour in virtual environments , 2011, Int. J. Arts Technol..

[27]  Christian Bauckhage,et al.  Combining Self Organizing Maps and Multilayer Perceptrons to Learn Bot-Behaviour for a Commercial Game , 2003, GAME-ON.

[28]  Weili Wu,et al.  Spatial contextual classification and prediction models for mining geospatial data , 2002, IEEE Trans. Multim..

[29]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.

[30]  Hans-Peter Kriegel,et al.  Algorithms for Characterization and Trend Detection in Spatial Databases , 1998, KDD.

[31]  M. Goodchild,et al.  Geographic Information Systems and Science (second edition) , 2001 .

[32]  Anthony K. H. Tung,et al.  Spatial clustering methods in data mining : A survey , 2001 .

[33]  André Robert Gagné,et al.  Visually exploring player strategies with Pathways, a visual analytics tool , 2011 .

[34]  Jaideep Srivastava,et al.  Sequence Alignment Based Analysis of Player Behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs) , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[35]  Christian Bauckhage,et al.  Learning Human-Like Movement Behavior for Computer Games , 2004 .