Recently, a global trend towards a broader use of secondary data in social sciences has been reinforced by the COVID-19 pandemic. This evoked doubts about the validity of the results unless restrictive assessment procedures are implemented. To address this need in the field of protected area (PA) conflict analysis, we propose a three-fold approach (theory-, method-, and cross-scale simulation-driven) to assess the usefulness of the utilized state register dataset and the indicator analysis methodology for the multi-level recognition of PA conflict determinants. With the ultimate aim to inform case study selection, we processed 187 relevant indicators from the official Statistics Poland register for a Lesser Poland region. We distinguished five types of PA conflict determinants in Lesser Poland (‘urbanity’, ‘agriculture’, ‘tourism’, ‘small-scale entrepreneurship’, and ‘sprawl’) and respective groups of 15 clusters comprising local-level units. For one cluster, we juxtaposed the obtained results with secondary data from another source (Internet content) and for a specific PA (Tatra National Park). Although the reported conflict issues corresponded to the indicator-derived descriptors of the cluster, in the theory-driven phase of the assessment, the state register failed to address the key prerequisites of PA conflicts. We have demonstrated that, in crisis conditions such as COVID-19, the proposed method can serve as a proxy for a multi-level recognition of PA conflict potentials, provided that it synthesises the results of different methodological approaches, followed by in-person interviews in the selected case studies.