Overview of Methods of Collection and Processing of Geographic Data
Vugar Hacimahmud Abdullayev
Nazila Ali Ragimova
Jala Jamalova
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Keywords

Geoinformatics
geographic data
Big Data
Internet of Things
MapReduce

How to Cite

Abdullayev, V., Ragimova, N., & Jamalova, J. (2021). Overview of Methods of Collection and Processing of Geographic Data. International Journal of Innovative Research and Reviews, 5(1), 6-9. Retrieved from http://www.injirr.com/article/view/57

Abstract

On the eve of Industry 4.0, there are global processes of digitalization and intellectualization of many scientific and economic areas. This article examines changes in geography under the influence of advanced information technologies. These technologies are the Internet of Things (for geographic data collection), Cloud Computing (for data storage), Big Data (for data processing), and Cyber Physical Systems (for physical and digital process management required to operate on geographic data). These technologies turn geographies into geoinformatics, which contributes to the further development of this science. The most useful technologies for geographic data collection are the Internet of Things (including things, people, data and processes) and remote sensing of the Earth (for remote geographic data collection). The most useful technologies for processing geographical data are On-Line Analytical Processing (for analytical processing of multidimensional data), Data Mining (for finding patterns in the obtained geographical data), Machine Learning (for deep analysis of geographical data) and MapReduce (for parallel processing of a large amount of data). Using methods, a geographical data processing algorithm develop, which consists of three stages. The first step is to implement the server necessary to form the foundation for data storage and processing. In order for a server to support the operational processing of big data, it must have a distributed file system. The second stage is the design of the database used for the organization and storage of geographical data. The last step is the basic processing and analysis of available geographical data. A paradigm MapReduce uses as an example of data processing.

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References

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