  In spite of considerable practical importance, current algorithmic fairness
literature lacks technical methods to account for underlying geographic
dependency while evaluating or mitigating bias issues for spatial data. We
initiate the study of bias in spatial applications in this paper, taking the
first step towards formalizing this line of quantitative methods. Bias in
spatial data applications often gets confounded by underlying spatial
autocorrelation. We propose hypothesis testing methodology to detect the
presence and strength of this effect, then account for it by using a spatial
filtering-based approach -- in order to enable application of existing bias
detection metrics. We evaluate our proposed methodology through numerical
experiments on real and synthetic datasets, demonstrating that in the presence
of several types of confounding effects due to the underlying spatial structure
our testing methods perform well in maintaining low type-II errors and nominal
type-I errors.
