Geographical networks of international migration

Áron Kincses
Additional contact information
Áron Kincses: Hungarian Central Statistical Office, Budapest

Migration Letters, 2020, vol. 17, issue 6, 799-812

Abstract: In the globalised world, various human activities (business, migration, etc.) organise into networks, and only through these skeletons can we observe the different phenomena that take place. In response to the emergence of globalisation we need to find new, usable tools and methods for the sound measurement of such changing phenomena. Network theory is an innovative approach that can help us handle the complexity of the 21st century. However, so far it has not featured in mainstream official statistics. The international migration offers a new field, in which to harvest the results of network theory (in geographical not in sociological sense). Through the migration countries’ networks (from where and to where migrants move) I provide some of the most important tangible outcomes of network analysis in international migration statistics. The analysis of the entire migration geographical network is limited to the presentation of the main trends and characteristics.

Keywords: international migration; network theory; spatial networks; spatial statistics (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.tplondon.com/ml/article/view/932/874 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mig:journl:v:17:y:2020:i:6:p:799-812

Ordering information: This journal article can be ordered from
https://migrationletters.com/

DOI: 10.33182/ml.v17i6.932

Access Statistics for this article

Migration Letters is currently edited by Kittisak Jermsittiparsert

More articles in Migration Letters from Migration Letters
Bibliographic data for series maintained by ML ().