分析移民和流动性的国际广播时间补充传输。

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Data Science and Analytics Pub Date : 2023-05-25 DOI:10.1007/s41060-023-00396-7

Bilgeçağ Aydoğdu, Hanif Samad, Shiqi Bai, Sami Abboud, Ilias Gorantis, Albert Ali Salah

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引用次数: 0

摘要

国际通话时间充值转账使预付费手机用户能够向其他国家的用户发送充值和数据包，并实时付款。移民大量使用这些工具在经济上帮助他们在本国的家人，因此可以成为移民和流动性分析的宝贵信息来源。然而，补充汇款作为移民汇款的一种形式却被低估了。在本文中，我们探讨了补充交易的决定因素和潜力，以补充汇款和移民统计数据。我们的研究结果表明，这些数据可以深入了解移民群体，特别是非正常移民，并用于估计特定国家移民群体的实时分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Analyzing international airtime top-up transfers for migration and mobility.

查看原文本刊更多论文

Analyzing international airtime top-up transfers for migration and mobility.

International airtime top-up transfers enable prepaid mobile phone users to send top-ups and data bundles to users in other countries, as well as make payments, in real time. These are heavily used by migrants to financially assist their families in their home countries and consequently could be a valuable source of information for migration and mobility analysis. However, top-up transfers are understudied as a form of money remittance in migration. In this paper, we explore the determinants and the potential of top-up transactions to complement remittance and migration statistics. Our results show that such data can provide insights into migrant groups, particularly for irregular migration and for estimating the real-time distribution of migrant groups for a given country.

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics