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

Theses and Dissertations

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 …

Exploring The Spatial Distribution Of Air Pollutants And Covid-19 Death Rate: A Case Study For Los Angeles County, California, Akhil Mandalapu, Junfeng Jiao, Amin Azimian

International Journal of Geospatial and Environmental Research

Objective

Since March 2020, COVID-19 has rapidly spread across the world with over 240 million cases and over 5 million deaths as of November 2021. It has been unclear what role air pollutants may play in exacerbating respiratory illnesses such as COVID-19 due to their interaction with the respiratory system. The association with air pollutants and COVID-19 severity has been explored at the regional and metropolitan area, however it is unclear if such an association is consistent at the neighborhood level.

Methods

Weekly death rates from COVID-19 from March 2020 to November 2021 were compared using one-sided unpaired t-tests across …

Geospatial Clustering And Hot Spot Detection Of Covid-19 Incidence In 2020: A Global Analysis, Munazza Fatima, Sana Arshad, Ibtisam Butt, Saba Arshad

International Journal of Geospatial and Environmental Research

Emergence and spread of Covid-19 initiated diversified researches based on spatial analysis in visualization, exploration, and modelling of this infection. This short communication is an attempt to comprehend the geographic distribution and spatial clustering of Covid-19 in year 2020. Main objective is to spatially analyze Covid-19 incidence rates, identification of hotspots and clusters outliers at global level. Monthly data of reported cases were taken from World Health Organization dashboard and situation reports. Incidence rate was calculated for each country for each month. Spatial autocorrelation techniques of Global Moran are I and Anselian Local Moran’s I were used to examine the …