Geospatial Data Utilisation in National Disaster Management Frameworks and the Priorities of Multilateral Disaster Management Frameworks: Case Studies of India and Bulgaria

Abstract

Facing the increased frequency of disasters and resulting in massive damages, many countries have developed their frameworks for Disaster Risk Management (DRM). However, these frameworks may differ concerning legal, policy, planning and organisational arrangements. We argue that geospatial data is a crucial binding element in each national framework for different stages of the disaster management cycle. The multilateral DRM frameworks, like the Sendai Framework 2015–2030 and the United Nations Committee of Experts on Global Geospatial Information Management (UNGGIM) Strategic Framework on Geospatial Information and Services for Disasters, provide the strategic direction, but they are too generic to compare geospatial data in national DRM frameworks. This study investigates the two frameworks and suggests criteria for evaluating the utilisation of geospatial data for DRM. The derived criteria are validated for the comparative analysis of India and Bulgaria’s National Disaster Management Frameworks. The validation proves that the criteria can be used for a general comparison across national DRM.

Exhaustive Insights Towards Social-Media Driven Disaster Management Approaches

“… This type of method mainly emphasizes channeling the identified events of disaster to an appropriate communication channel to forward the information to emergency services. Some of the existing research frameworks towards information dissemination implemented are as follows Mobile computing-based emergency management (Astarita et al [37]), Geospatial-information based disaster management (Ghawana et al [38]), User behavior centric framework for disaster screening (Han & Wang [39]), Hubframework connecting the critical community with local emergency management team (Mitcham et al [40]), Micro-Macro level-based disaster alters using social network (Samaddar et al [41]), Edge computing-based named data network for disaster response system (Tran & Kim [42]), Acquisition framework for disaster location analysis (Yang et al [43]), Multimodal framework for disaster data evaluation (Zhang et al [44]), Model for Dynamic theme propagation for rainstorm detection (Zhang et al [45]), Integrated machine learning and spatiotemporal analytical framework (Zhang et al [46]). A closer look into these study models shows that they are mainly meant for the authorities to use social media to propagate safety guidelines, evacuation routes, and emergency information to the affected population.…”

“…R1: Which is the most frequently addressed research problems towards disaster management? Existing research in disaster management are witnessed to addresses a variety of issues, but some of the most frequently addressed problems are i) risk assessment and prediction (Psaroudakis et al [30]), ii) preparedness and planning (Aramendia et al [26], Sultan et al [34]), iii) response and recovery (Tran & Kim [42], Psaroudakis et al [30], Zhang [46], Asif et al [53], Malla et al [70]), iv) community resilience (Tran and Kim [42], Dixon et al [67]), and v) technology and innovation (Ghawana et al [38]). Under risk assessment and prediction, it is related to understanding the factors that contribute to disaster risk, developing models to predict the occurrence and severity of disasters, and identifying vulnerable populations and assets.…”

Enhancing Resilience in Coastal Regions from a Socio-Ecological Perspective: A Case Study of Andhra Pradesh, India

“…The National Policy on Disaster Management by the Ministry of Home Affairs, India, was adopted in 2009. Improving real-time forecasts and early warning systems, supported by responsive and fail-safe communication and information technology [76], is one of its major objectives. Regular estimations and forecasts of climatic risks can be observed with various laboratories and institutions.…”

Đánh giá rủi ro ngập lụt của di sản văn hóa thế giới: Ứng dụng tại thành phố Hội An

“…Cùng với sự phát triển của khoa học và công nghệ mới, chẳng hạn như mô hình học máy hoặc các mô hình thủy văn tích hợp cũng được sử dụng để đánh giá rủi ro cũng như phân tích các kịch bản rủi ro khác nhau dựa trên các bản đồ không gian địa lý với các dữ liệu khác nhau, kinh nghiệm và lưu trữ các thảm họa thiên nhiên hiện tại và trong quá khứ [6]. Thông tin cần thiết này không những hỗ trợ xác định các lĩnh vực ưu tiên mà còn thực hiện các biện pháp để giảm thiểu chi phí, thời gian và rủi ro ngập lụt, đồng thời thiết lập các ưu tiên bảo tồn nhằm thích ứng với những biến động do tác động thiên tai.…”