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A Correlation Analysis and Visualization of Climate Change using Post-Disaster Heterogeneous DatasetsView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000701</doi_batch_id><timestamp>1715238903</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 7</title></titles><publication_date><month>10</month><year>2024</year></publication_date><journal_volume><volume>25</volume></journal_volume><issue>7</issue></journal_issue><journal_article><titles><title>A Correlation Analysis and Visualization of Climate Change using Post-Disaster Heterogeneous Datasets</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>There are numerous geo-climatic and human factors that contribute to the occurrence of natural disasters in the real-world scenario. Besides the study of causes and preconditions of such calamities, post-disaster analysis is essential for the efficient management of the disaster situation. This process needs timely and accurate data in light of the increasing frequency and severity of climate change-related extreme weather events. The analysis of disaster data involves the challenging task of integrating multiple heterogeneous sources, data ingestion and visualization. This paper aims at providing a three-dimensional analytical view of disaster data as time-series charts and a statistical model to evaluate the correlation between the occurrence of disasters, climate change and the corresponding economic damages (as a percentage of GDP). The created corpus consists of two unified heterogeneous datasets. The first one forms the basis for exploratory data analysis involving visualization charts for disaster-related factors. The second dataset involves temperature anomaly information which allows for climate change analysis. The integration of the above datasets provides scope for correlation analysis using three different variations of coefficients. Therefore, statistical methodologies are leveraged to play important role in managing disasters, from preparation to recovery and reporting. The graphical representations provide insights on regional trends that follow, related to factors such as the proportion of each type of disaster in the various losses incurred. Therefore, obtaining reliable information about the population, the economy and climate are crucial both for risk management and preparedness and for responding to disasters. The research puts forth a detailed statistical methodology with a spatial dimension to study the impacts on the economy and infrastructure in the aftermath of a disaster thereby ensuring specialized assistance. The inference of the analysis confirmed a positive correlation between climate change and occurrence of natural disasters. Therefore, statistical evidence of an important phenomenon like climate change affecting natural disasters brings awareness among the population in the society to be more environmentally responsible.</p> </p></abstract><publication_date><month>08</month><day>12</day><year>2023</year></publication_date><pages><first_page>155</first_page><last_page>165</last_page></pages><publisher_item><identifier id_type="pii">gnest_04382</identifier></publisher_item><doi_data><doi>10.30955/gnj.004382</doi><timestamp>1715238903</timestamp><resource>https://journal.gnest.org/publication/gnest_04382</resource></doi_data></journal_article></journal></body></doi_batch> </div>
