  As a rule statistical measures are often vulnerable to the presence of
outliers and spatial correlation coefficients, critical in the assessment of
spatial data, remain susceptible to this inherent flaw. In contexts where data
originates from a variety of domains (such as, e. g., socio-economic,
environmental or epidemiological disciplines) it is quite common to encounter
not just anomalous data points, but also non-normal distributions. These
irregularities can significantly distort the broader analytical landscape often
masking significant spatial attributes. This paper embarks on a mission to
enhance the resilience of traditional spatial correlation metrics, specifically
the Moran Coefficient (MC), Geary's Contiguity ratio (GC), and the Approximate
Profile Likelihood Estimator (APLE) and to propose a series of alternative
measures. Drawing inspiration from established analytical paradigms, our
research harnesses the power of influence function studies to examine the
robustness of the proposed novel measures in the presence of different outlier
scenarios.
