Today, the growing use of remote sensors, crowd-sourced data collection, advances in geographic information systems and related analytic techniques, along with inexpensive data storage and the decreased cost of computational resources, have led to enormous quantities of geospatial data being collected across many disciplines ranging from business to science to engineering (Jiang and Shekhar 2017) and (Shekhar et al. 2015). In addition, as the understanding that geospatial, or locational, data has value for many organizations, the focus on this special type of data has also increased. SIGGIS is interested in examining the many opportunities presented by this special type of data.
Jiang, Z., and Shekhar, S. 2017. “Spatial Big Data,” in Spatial Big Data Science: Classification Techniques for Earth Observation Imagery, Z. Jiang and S. Shekhar (eds.), Cham: Springer International Publishing, pp. 3–13. (https://doi.org/10.1007/978-3-319-60195-3_1).
Shekhar, S., Feiner, S. K., and Aref, W. G. 2015. “Spatial Computing,” Commun. ACM (59:1), pp. 72–81. (https://doi.org/10.1145/2756547).