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Big Data Applications and Challenges...

Forati, Amir Masoud,

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Big Data Applications and Challenges in Giscience (Case Studies: Natural Disaster and Public Health Crisis Management) /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big Data Applications and Challenges in Giscience (Case Studies: Natural Disaster and Public Health Crisis Management) // Amir Masoud Forati.
Author:	Forati, Amir Masoud,
Description:	1 electronic resource (241 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:	Dissertations Abstracts International85-07A.
Subject:	Public health. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30813280
ISBN:	9798381376265

Big Data Applications and Challenges in Giscience (Case Studies: Natural Disaster and Public Health Crisis Management) /
Forati, Amir Masoud,

Big Data Applications and Challenges in Giscience (Case Studies: Natural Disaster and Public Health Crisis Management) /Amir Masoud Forati. - 1 electronic resource (241 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: A.

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis. The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials.The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic's incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic's severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread.By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks.

English

ISBN: 9798381376265Subjects--Topical Terms:

534748
Public health.
Subjects--Index Terms:

Natural disasters

Big Data Applications and Challenges in Giscience (Case Studies: Natural Disaster and Public Health Crisis Management) /
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502 $b Ph.D. $c The University of Wisconsin - Milwaukee $d 2023.
520 $a This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis. The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials.The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic's incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic's severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread.By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks.
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650 4 $a Information technology. $3 532993
650 4 $a Geographic information science. $3 3432445
650 4 $a Geography. $3 524010
653 $a Natural disasters
653 $a GIScience
653 $a MGWR
653 $a Social network analysis
653 $a Spatial analysis
653 $a Social media
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710 2 $a The University of Wisconsin - Milwaukee. $b Geography. $e degree granting institution. $3 3820868
720 1 $a Ghose, Rina $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-07A.
790 $a 0263
791 $a Ph.D.
792 $a 2023
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