Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.
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