The rapid integration of machine learning (ML) into critical decisionmaking systems has heightened concerns over fairness, particularly regarding spatial biases often tied to sensitive socioeconomic factors. In response, we propose a model-agnostic post-processing method for spatial bias mitigation that operates without access to the original training data. Our approach formulates an optimization problem that minimizes a fairness measure robust to gerrymandering, subject to a constraint specifying the allowable deviation from the original model's performance ensuring spatial fairness while preserving accuracy. This measure has a 0–1 scale, offering an intuitive way to quantify spatial bias. Comprehensive evaluations on real-world datasets show that our framework effectively reduces spatial bias and achieves fairer outcomes with minimal performance loss, outperforming other state-of-the-art post-processing methods. This work advances spatial fairness methodologies, offering practitioners an efficient, interpretable, and adaptable post-processing solution to mitigate location-based discrimination in ML applications.