Asian Basic and Applied Research Journal

Geospatial Big Data has become a general term. Geospatial Big Data is mainly used to describe heterogeneous, massive, and always unstructured or semi-structured digital content that is difficult to process using traditional database management systems, tools, and techniques. The objectives are: Geospatial Big Data helps with best decision making, saves cost from greater efficiency, help us in real-time and analyze spatial connections. The methodology depend on review research.

There are many results divides three parts:

• Part one: Geospatial Big data Analytics Applications Trends: Big data computational methods (geospatial data preprocessing, overlay analysis, land use change prediction, and global scale terrain analysis), big data mining (human mobility, disaster management), knowledge representation (geospatial problem solving, geographic knowledge representation), And big data search (geospatial big data management and searching (climate data).
• Part two: Geospatial Big data Analytics Applications Opportunities: focus on seven factors in Strategic Affair (SIMPEST).
• Part three: Geospatial Big data Analytics Applications Challenges: focus on five characterized by the following, with the first four being more fundamental and important: Volume (Amount of Data), Variety (Different type of data), Value (Cost), Velocity (Streaming Speed), and Validity (Veracity noise elimination). The Geospatial big data challenges vision over Sustainable Development Goals. There are many challenges when handling big data problems, and difficulties lie in capture, storage, processing, analysis, and visualization.

The future researches focus of Geospatial Big data Analytics Applications (Trends, challenges and opportunities).