scholarly journals Stream Network Geometry Correlates with Climate

Eos ◽  
2017 ◽  
Author(s):  
Terri Cook
Keyword(s):  
United States ◽  
Big Data ◽  
Data Analysis ◽  
Arid Regions ◽  
Big Data Analysis ◽  
Stream Network ◽  
Network Geometry

A "big data" analysis of nearly 1 million river junctions in the contiguous United States shows that branching angles in dendritic drainages vary systematically between humid and arid regions.

scholarly journals Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States

2015 ◽  
Vol 94 (7) ◽  
pp. 1127-1138 ◽  
Author(s):  
Bruno C. Medeiros ◽  
Sacha Satram-Hoang ◽  
Deborah Hurst ◽  
Khang Q. Hoang ◽  
Faiyaz Momin ◽  
...  
Keyword(s):  
United States ◽  
Acute Myeloid Leukemia ◽  
Big Data ◽  
Data Analysis ◽  
Myeloid Leukemia ◽  
The United States ◽  
Big Data Analysis ◽  
Treatment Patterns ◽  
Acute Myeloid

scholarly journals A big-data analysis of political rhetoric relating the developments of the United States, China, and global powers

2020 ◽  
Vol 23 (3) ◽  
pp. 227-243
Author(s):  
Patrick Carter ◽  
Jeffrie Wang ◽  
Davis Chau
Keyword(s):  
United States ◽  
Big Data ◽  
Data Analysis ◽  
Political Rhetoric ◽  
The United States ◽  
Big Data Analysis ◽  
Content Type ◽  
World Power ◽  
The U.S ◽  
Over Time

PurposeThe similarities between the developments of the United States (U.S.) and China into global powers (countries with global economic, military, and political influence) can be analyzed through big data analysis from both countries. The purpose of this paper is to examine whether or not China is on the same path to becoming a world power like what the U.S. did one hundred years ago.Design/methodology/approachThe data of this study is drawn from political rhetoric and linguistic analysis by using “big data” technology to identify the most common words and political trends over time from speeches made by the U.S. and Chinese leaders from three periods, including 1905-1945 in U.S., 1977-2017 in U.S. and 1977-2017 in China.FindingsRhetoric relating to national identity was most common amongst Chinese and the U.S. leaders over time. The differences between the early-modern U.S. and the current U.S. showed the behavioral changes of countries as they become powerful. It is concluded that China is not a world power at this stage. Yet, it is currently on the path towards becoming one, and is already reflecting characteristics of present-day U.S., a current world power.Originality/valueThis paper presents a novel approach to analyze historical documents through big data text mining, a methodology scarcely used in historical studies. It highlights how China as of now is most likely in a transitionary stage of becoming a world power.

scholarly journals O’NEIL, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown, 2016. ISBN 9780553418811

2017 ◽  
Vol 7 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Filipe Meirelles Ferreira Braga ◽  
Pedro Vinhaes Cardoso
Keyword(s):  
United States ◽  
New York ◽  
Big Data ◽  
Data Analysis ◽  
The United States ◽  
Big Data Analysis

Em seu livro Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil analisa os perigos da automatização de processos a nível socioeconômico por algoritmos e sistemas de análise de big data. A partir de exemplos dos Estados Unidos, O’Neil analisa como essas ferramentas podem reproduzir os preconceitos de seus formuladores, perpetuando dinâmicas de desigualdade.ABSTRACTIn her book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil analyses the dangers from the automatization of socioeconomic processes via algorithms and big data analysis systems. Using examples from the United States, O'Neil analyses those tools may reproduce the prejudices from the modelers and perpetuate dynamics of inequality.Palavras-chave: Big Data; Globalização; DemocraciaKeywords: Big Data; Globalization; DemocracyDOI: 10.12957/rmi.2016.25939 Recebido em 09 de Outubro de 2016 | Aceito em 24 de Outubro de 2016Received on October 09, 2016 | Accepted October 24, 2016  

Insights into seismic hazard from big data analysis of ground motion simulations

2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Big Data Analysis ◽  
Ground Motion Simulations
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