Geospatial Big Data Analytics Applications Trends, Challenges & Opportunities

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Published: 2022-03-04

Page: 140-144


Taha Alfadul Taha Ali

Alzaiem Alazhri University, Khartoum, Sudan.

*Author to whom correspondence should be addressed.


Abstract

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).

Keywords: Geospatial Data Analyst, IoT, Sustainable & Smart city, cyber security


How to Cite

Taha Ali, T. A. (2022). Geospatial Big Data Analytics Applications Trends, Challenges & Opportunities. Asian Basic and Applied Research Journal, 4(1), 140–144. Retrieved from https://globalpresshub.com/index.php/ABAARJ/article/view/1483

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References

Batty. Big Data, Smart Cities and City Planning” Dialogues in Human Geography. 2013;3:274–279.

Shekhar. Spatial Big-data Challenges Intersecting Mobility and Cloud Computing. In Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access ACM. 2012;1–6.

Jo. Framework to Address the Challenges of Geospatial Big Data., ISPRS Int. J. Geo-Inf. 2019; 8:475.

Zhao. High-Performance Overlay Analysis of Massive Geographic Polygons That Considers Shape Complexity in a Cloud Environment.ISPRS Int. J. Geo-Inf; 2019.

Kang. Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework. ISPRS Int. J. Geo-Inf. 2019;P8:454.

Safanelli. Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis. ISPRS Int.J. Geo-Inf. 2020;9:400.

Gaigalas. Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS. ISPRS Int. J. Geo Inf. 2019;8:494.

Wu. A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data. ISPRS Int. J. Geo-Inf. 2019;8;142.

Yang. Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation. ISPRS Int. J. Geo-Inf. 2019;8:29.

Zhang. Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery.ISPRS Int. J. Geo-Inf. 2019;8:434.

Manyika J. Big data: the next frontier for innovation, competition andproductivity. McKinsey Global Institute, New York; 2019.

Bu Y. Scaling datalog for machine learning onbig data. Computer research repository (CoRR) Cornell University Library; 2012.

Ledolter J. Data mining and business analytics with R. John Wiley & Sons, New York; 2013.

Slavakis K. Modeling and optimization for big data analytics. IEEE Signal Process Mag. 2014; 31(5):18–31

Jain AK. Data clustering: a review. ACM Comput Surv. 1999;31(3).

Ma C-L. A three-dimensional display for big data sets. In: Internationalconference on machine learning and cybernetics (ICMLC). IEEE Computer Society; 2012.