Boolean decomposition of spatiotemporal tensors
Chen, Crystal.; Ellison, Charlotte L.; Roth, Zachary J.; Simper, Mackenzie A.
This technical note (TN) discusses Boolean decompositions of binary tensors containing spatiotemporal data. The increase in data collection technology has led to a proliferation of data containing information on human movement. Effectively managing and gaining insights from this influx of data is presenting a significant challenge to the Army geospatial community. This information can extend beyond just time and position coordinates to include features such as weather, social interactions, etc. The addition of these extra variables can provide new insight into human behavior and patterns of life. The capability to geocomputationally manipulate these related datasets will provide additional insight into human behavior and patterns of life, thereby enhancing the Army GEOINT- HUMINT spectrum of operations. Since spatiotemporal data is often binary, new theory is needed to fully exploit this information and draw conclusions.
Geospatial Research Laboratory (U.S.)Engineer Research and Development Center (U.S.)
Tensor algebra; Algebra, Boolean; Boolean matrices; Geospatial data
Technical Note (Engineer Research and Development Center (U.S.)) ; no. ERDC/GRL TN-19-2
Approved for Public Release; Distribution is Unlimited