The effective mobilisation of inadequate resources is always a challenging task for the administrators especially when it is in high demand during situations like disasters, medical emergencies etc. They usually try to prioritise the most vulnerable areas in such situations. However, they often face challenges in identifying these areas as multiple factors may define the vulnerability. Interestingly, these factors may change their behaviour over time, thereby adding more complexities into the overall process. Sometimes the lack of transparency makes the beneficiaries impatient in such situations as well. In this work, we have presented an explainable spatio-temporal platform, empowered with the novel wheel based clustering techniques, to address these challenges. As per the availability of resources, it facilitates prioritising a subset of administrative regions based on multiple factors and also explains the reasons thereof. In order to validate the effectiveness of the proposed framework, we have conducted an experiment with Covid-19 dataset of India. The proposed platform is able to prioritise vaccination in the areas with 70% caseload during May 2021, when it was struggling with the peak of second wave. Interestingly, these regions constitute only 40% of the national population.