Boids (Bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (Sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog to model predation risk in predator-prey dynamic. Previous work assumed homogeneous shoids. Real-world observations on sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoid with different parameters from the remainder of the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from actual sheep herding trial by an Unmanned Aerial Vehicle (UAV) to validate the proposed measures. The resultant footprints will allow us in our future research to design more efficient control strategies for the UAV to improve the herding of sheep, by polarising the attention of the machine learning algorithm on those Shoids with influence footprints to drive the flock.