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Radio frequency (RF) fingerprinting-based techniques for localization are a promising approach for ubiquitous positioning systems, particularly indoors. By finding unique fingerprints of RF signals received at different locations within a predefined area beforehand, whenever a similar fingerprint is subsequently seen again, the localization system will be able to infer a user's current location. However, developers of these systems face the problem of finding reliable RF fingerprints that are unique enough and adequately stable over time. We present a visual analytics system that enables developers of these localization systems to visually gain insight on whether their collected datasets and chosen fingerprint features have the necessary properties to enable a reliable RF fingerprinting-based localization system. The system was evaluated by testing and debugging an existing localization system.