Share
Big Data Analysis on Global Community Formation and Isolation: Sustainability and Flow of Commodities, Money, and Humans (in English)
Ikeda, Yuichi ; Iyetomi, Hiroshi ; Mizuno, Takayuki (Author)
·
Springer
· Paperback
Big Data Analysis on Global Community Formation and Isolation: Sustainability and Flow of Commodities, Money, and Humans (in English) - Ikeda, Yuichi ; Iyetomi, Hiroshi ; Mizuno, Takayuki
$ 123.15
$ 129.99
You save: $ 6.84
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, July 08 and
Tuesday, July 09.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Big Data Analysis on Global Community Formation and Isolation: Sustainability and Flow of Commodities, Money, and Humans (in English)"
In this book, the authors analyze big data on global interdependence caused by the flows of commodities, money, and people, using a network science approach to obtain differing views of globalization and to clarify the facts on isolation of communities. Globalization reduces international economic inequality, i.e., it allows emerging countries to catch up while it increases relative poverty in some advanced countries. How should this trade-off between international and domestic inequalities be resolved? At the same time, the reduction of biocultural diversity caused by globalization needs to be avoided. What kind of change is required in local communities to conserve biocultural diversity? On the issue of commodity flow, research results of the supply-chain network, isolation in industry, and resource flows and stocks are presented in this book. For monetary flow, ownership networks, value-added networks, and profit shifting were studied; and regarding the flow of people, linkage of ethnic groups, immigrant assimilation, and refugees were examined. Based on the resulting view of globalization and isolation, the development of the isolation index using machine learning is discussed. Finally, recommendations for evidence-based policymaking in the United Nations are considered.