Spatial game analytics and visualization

Abstract

The recently emerged field of game analytics and the development and adaptation of business intelligence techniques to support game design and development has given data-driven techniques a direct role in game development. Given that all digital games contain some sort of spatial operation, techniques for spatial analysis had their share in these developments. However, the methods for analyzing and visualizing spatial and spatio-temporal patterns in player behavior being used by the game industry are not as diverse as the range of techniques utilized in game research, leaving room for a continuing development. This paper presents a review of current work on spatial and spatio-temporal game analytics across industry and research, describing and defining the key terminology, outlining current techniques and their application. We summarize the current problems and challenges in the field, and present four key areas of spatial and spatio-temporal analytics: Spatial Outlier Detection, Spatial Clustering, Spatial Predictive Models, Spatial Pattern and Rule Mining. All key areas are well-established outside the context of games and hold the potential to reshape the research roadmap in game analytics.