Spatial-Temporal Analysis of Interests in Climate Change Topics through Web Search Information, Case Study: Egypt

Research Article- DOI: 10.23953/cloud.ijarsg.390

  • M.A. Hassaan Alexandria Research Center for Adaptation to Climate Change (ARCA), Institute of Graduate Studies and Research – Alexandria University, 163 Horreya Avenue, Chatby, Alexandria, Egypt

Abstract

The accelerated frequently use of the Internet for a variety of purposes generates massive datasets with large structure that are generated by individuals and organizations all over the world at very high rate, which is referred to “Big Data”. Google is one of the most popular web search engines that is widely used by millions of people to look for information. Thus, it represents one of the Big Data sources as they provide valuable information about public interest. This paper is intended to take the advantage of Big Data in assessing temporal pattern of interest in climate change topics in Egypt during the period 2012-2017, highlighting spatial variations of interest in climate change in Egypt and interpret such variations. For this purpose, data on search volume of various climate change topics was retrieved from Google Trend service. The retrieved data was analyzed and interpreted. It was found that the interest in climate change in Egypt has been experiencing different trends during the period 2012-2017, where interest in some issues such as Nile delta and adaptation increased noticeably during this period. Also, the level of interest in climate change varied spatially among different parts of Egypt, the spatial variations were found to be associated with both of human and institutional capacities.


Keywords Big data; Climate change; Egypt; Spatial-temporal analysis


DOI: https://doi.org/10.23953/cloud.ijarsg.390


 


 

Published
2018-12-06
How to Cite
HASSAAN, M.A.. Spatial-Temporal Analysis of Interests in Climate Change Topics through Web Search Information, Case Study: Egypt. International Journal of Advanced Remote Sensing and GIS, [S.l.], v. 7, n. 1, p. pp. 2870-2877, dec. 2018. ISSN 2320-0243. Available at: <https://www.cloudpublications.org/journals/index.php/RemoteSensing/article/view/435>. Date accessed: 14 oct. 2019.
Section
Research Articles