Abstract:
The current era of Great Power Competition (GPC) between the People's Republic
of China (PRC), the Russian Federation (Russia), and the United States is characterized
by increased use of \hybrid threats." These are actions, short of military force, that are
designed to fall under existing detection and response thresholds and compromise existing
security norms and decision making processes. National security scholars and practitioners
widely agree that the ability of the United States and its allies to detect and respond to these
hybrid threats is limited at best, and that the Indication and Warning (I & W) intelligence
function, designed to prevent strategic surprise that fundamentally alters policy, plans, and
assumptions about the security environment, has atrophied. This research explores how
geospatial science, through the discipline of Geospatial Intelligence (GEOINT) can detect,
monitor, and provide I &Wintelligence that prevents strategic surprise from hybrid threats.
Specifically, this thesis applied a novel Strategic Intelligence Framework (SIF) to standard
I & W intelligence practices to identify, analyze, and visualize PRC activities that
carried hybrid threat characteristics within a U.S./Canadian Arctic and circumpolar study
area. Through incorporating local spatial context via the Getis-Ord Gi* statistic, as well
as the strength of the hybrid threat \signal," this case study successfully identified and
mapped higher and lower threat regions using kernel density estimation (KDE) in the
form of a Mesoscale Operational Situational Awareness Intelligence Composite
(MOSAIC). The success of this case study shows that the SIF and MOSAIC are powerful
tools for detecting, analyzing, and warning about the collective impact of hybrid threats.
