Advances in Research

The work encompasses the analysis and visualisation of the patterns, impact, and trends of disasters, comprising records of 783 disaster incidents that were analysed using Python. The anthropogenic, tectonic, stratification and climate change impacts cause floods, cyclones, droughts, earthquakes, epidemics and chemical disasters. To better understand these events and their consequences, a dataset is needed. Using a long-term (124-year) dataset and utilising geospatial analysis (Q-GIS/RS) with data science libraries (Pandas, Matplotlib) constitutes a solid and scientifically valid approach for disaster informatics. The main finding that climate change-related phenomena (floods and cyclones) are recurrent and catastrophic in certain states (Odisha, Gujarat) is in accordance with extant climate literature relating to the Indian subcontinent and can, therefore, be considered credibleIt provides a reproducible framework for researchers and practitioners to identify disaster-prone areas and recurrent high-impact events like floods and cyclones by utilising Python-based geospatial analysis and visualisation.  This thorough analysis has the power to force management authorities to adopt a proactive approach to risk reduction, infrastructure development, and disaster preparedness rather than a reactive, relief-focused one. This will directly support international initiatives such as the Sustainable Development Goals for the real-world applicability of Python as a geospatially versatile tool.  The future projections shall comply with Sustainable Development Goal 11.5, comprising Sustainable Cities and Communities, specifically in target 11.5, SDG 13 (Climate Action).