Organizations that leverage their increasing volume of geospatial data have the potential to enhance their strategic and organizational decisions. However, literature describing the best techniques to make decisions using geospatial data and the best approaches to take advantage of geospatial data’s unique visualization capabilities is limited. This paper reviews the use of geospatial visualization and its effects on decision performance, which is one of the many components of decision-making when using geospatial data. Additionally, this paper proposes a comprehensive model allowing researchers to better understand decision-making using geospatial data and provides a robust foundation for future research. Finally, this paper makes an argument for further research of information-presentation, task-characteristics, user-characteristics and their effects on decision-performance when utilizing geospatial data.

An understanding of geospatial reasoning ability (GRA) is essential to human–computer interaction research, as many recent consumer and commercial technologies require an ability to interpret complex geospatial data. Individuals, as well as government, commercial, and military organizations, use such technologies regularly. For instance, consumer technologies including online mapping services and in-vehicle navigation systems are increasingly prevalent. Business leaders rely on geospatial data when making strategic and operational decisions. Military strategists leverage geospatial systems during mission planning and battlefield operations. Although there has been research into the significance of user characteristics when making decisions using geospatial data, there is conflicting evidence on the impact of GRA on the decision-making process. This article suggests applying a multidimensional measure of GRA to facilitate a better understanding of such interactions. Furthermore, this article proposes a new measurement instrument developed through a rigorous scale development procedure and validated through an exploratory (n = 300) analysis.