Acta Scientific Computer Sciences

Literature Review Volume 3 Issue 7

Application of Big Data Geographic Information System to Spatio-Temporal Analysis of Coast Defense Cases: Smuggling Cigarette Investigation

Yahui Meng1, Lucy Huang1, ZY Chen1, Huakun Wu1 and Timothy Chen2*

1School of Science, Guangdong University of Petrochemical Technology, Guangdong, China
2Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

*Corresponding Author: Timothy Chen, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.

Received: May 10, 2021; Published: June 25, 2021


Application of big data geographic information system to spatio-temporal analysis of coast defense cases for smuggling cigarette investigation is paramount. Big data is characterized by rapid change and high efficient. The value of a wide range of density and its performance and so on are obvious, but the fundamental difference between the concept of big data and massive data alone that big data is a high-tech mobile. The use of Internet is available at any time, simply the Internet, network connect, and information systems, etc. The development trend of big data itself, such as data that can be automatically obtained by big data itself, such as navigation and positioning data, mobile phone signaling data, search engine data, e-commerce transaction data, bus swiping data, social media data, transportation real-time sensing data, and so on. By means of deductive collection from the previous perspectives, the study discuss the results and show the practical implication to application of big data geographic information system for smuggling cigarette investigation.

Keywords: Spatio-temporal Analysis; Coast Defense; Smuggling Cigarette; Big Data; Geographic Information System


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Citation: Yahui Meng., et al. “Application of Big Data Geographic Information System to Spatio-Temporal Analysis of Coast Defense Cases: Smuggling Cigarette Investigation". Acta Scientific Computer Sciences 3.7 (2021): 66-71.


Copyright: © 2021 Yahui Meng., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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