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Accurate tumor margin identification is essential for ensuring complete removal and minimizing the risk of regrowth in cancer surgeries. As pathological changes in tissue results in altered electrical properties, impedance measurements can support surgeons to identify tumorous regions. In comparison to simple impedance sensors, multielectrode sensor arrays allow for a planar impedance mapping of the underlying tissue, enabling a precise tumor margin estimation. However, the measured impedance values do not take into account the different electrode configurations used for each measurement and result in inaccurate electrical maps. In this work, we first introduce an analytical form for the so-called geometry factor for general four-electrode measurement configurations, allowing for a scaled impedance (impedivity) mapping. Then, by simulating impedance measurements with a quadratic multielectrode sensor array, the corresponding impedivity map is created by scaling each impedance value for each convex electrode configuration with the associated geometry factor. We simulate different tumor scenarios to compare our novel impedivity maps to state-of-the-art impedance maps. The tumor margins are estimated using Otsu's method, based on the pixel-wise variations within the generated maps. The results demonstrate that the new impedivity map consistently outperforms the impedance map in tumor margin estimation, with accuracy improvements across all simulation scenarios. For surface tumors, the impedivity map achieves an accuracy of 90.3 % compared to 85.7 % for the impedance map. For deeper-seated tumors, the accuracy of the impedivity map remains consistently high, ranging between 84 % and 91 %. In contrast, the impedance map achieves a maximum accuracy of only 61 %, with its lowest performance dropping to 36 %. In a next step, these findings need to be validated experimentally.