scholarly journals Measuring migration 2.0: a review of digital data sources

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jasper Tjaden
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Human Migration ◽  
Data Sources ◽  
Digital Data ◽  
Complex Field ◽  
Trace Data ◽  
Policy Analysts

AbstractThe interest in human migration is at its all-time high, yet data to measure migration is notoriously limited. “Big data” or “digital trace data” have emerged as new sources of migration measurement complementing ‘traditional’ census, administrative and survey data. This paper reviews the strengths and weaknesses of eight novel, digital data sources along five domains: reliability, validity, scope, access and ethics. The review highlights the opportunities for migration scholars but also stresses the ethical and empirical challenges. This review intends to be of service to researchers and policy analysts alike and help them navigate this new and increasingly complex field.

scholarly journals Augmenting Surveys With Data From Sensors and Apps: Opportunities and Challenges

2020 ◽  
pp. 089443932097995
Author(s):  
Bella Struminskaya ◽  
Peter Lugtig ◽  
Florian Keusch ◽  
Jan Karem Höhne
Keyword(s):  
Survey Data ◽  
Travel Behavior ◽  
Mobile Apps ◽  
Data Sources ◽  
Digital Data ◽  
Smart Devices ◽  
Special Issue ◽  
Practical Applications ◽  
The Social ◽  
Internal States

The increasing volume of “Big Data” produced by sensors and smart devices can transform the social and behavioral sciences. Several successful studies used digital data to provide new insights into social reality. This special issue argues that the true power of these data for the social sciences lies in connecting new data sources with surveys. While new digital data are rich in volume, they seldomly cover the full population nor do they provide insights into individuals’ feelings, motivations, and attitudes. Conversely, survey data, while well suited for measuring people’s internal states, are relatively poor at measuring behaviors and facts. Developing a methodology for integrating the two data sources can mitigate their respective weaknesses. Sensors and apps on smartphones are useful for collecting both survey data and digital data. For example, smartphones can track people’s travel behavior and ask questions about its motives. A general methodology on the augmentation of surveys with data from sensors and apps is currently missing. Issues of representativeness, processing, storage, data linkage, and how to combine survey data with sensor and app data to produce one statistic of interest pertain. This editorial to the special issue on “Using Mobile Apps and Sensors in Surveys” provides an introduction to this new field, presents an overview of challenges, opportunities, and sets a research agenda. We introduce the four papers in this special issue that focus on these opportunities and challenges and provide practical applications and solutions for integrating sensor- and app-based data collection into surveys.

Big Data and the Study of Communities and Crime

2018 ◽  
Author(s):  
Daniel T. O'Brien
Keyword(s):  
Social Media ◽  
Big Data ◽  
Computational Social Science ◽  
Data Sources ◽  
Digital Data ◽  
Data Sets ◽  
Popular Imagination ◽  
Administrative Records ◽  
Communities And Crime ◽  
Criminology And Criminal Justice

In recent years, a variety of novel digital data sources, colloquially referred to as “big data,” have taken the popular imagination by storm. These data sources include, but are not limited to, digitized administrative records, activity on and contents of social media and internet platforms, and readings from sensors that track physical and environmental conditions. Some have argued that such data sets have the potential to transform our understanding of human behavior and society, constituting a meta-field known as computational social science. Criminology and criminal justice are no exception to this excitement. Although researchers in these areas have long used administrative records, in recent years they have increasingly looked to the most recent versions of these data, as well as other novel resources, to pursue new questions and tools.

Big Data Research in the Tourism Industry

2020 ◽  
Vol 11 (4) ◽  
pp. 26-41
Author(s):  
Imadeddine Mountasser ◽  
Brahim Ouhbi ◽  
Bouchra Frikh ◽  
Ferdaous Hdioud
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Tourism Industry ◽  
Data Sources ◽  
Digital Data ◽  
Current State ◽  
Wide Range ◽  
The World ◽  
Data Acquisition Process

Nowadays, people and things are becoming permanently interconnected. This interaction overloaded the world with an incredible digital data deluge—termed big data—generated from a wide range of data sources. Indeed, big data has invaded the domain of tourism as a source of innovation that serves to better understand tourists' behavior and enhance tourism destination management and marketing. Thus, tourism stakeholders have increasingly leveraging tourism-related big data sources to gather abundant information concerning all tourism industry axes. However, big data has several complexity aspects and brings commensurate challenges that go along with its exploitation. It has specifically changed the way data is acquired and managed, which may influence the nature and the quality of the conducted analyses and the made decisions. Thus, this article investigates the big data acquisition process and thoroughly identifies its challenges and requirements. It also reveals its current state-of-the-art protocols and frameworks.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Taking Aim at Attack Advertising

2019 ◽  
Author(s):  
Kim Fridkin ◽  
Patrick Kenney
Keyword(s):  
Content Analysis ◽  
Focus Groups ◽  
Survey Data ◽  
Data Sources ◽  
Empirical Validation ◽  
Negative Campaigning ◽  
Senate Elections ◽  
Campaign Messages ◽  
Negative Messages ◽  
The Impact

This book develops and tests the “tolerance and tactics theory of negativity.” The theory argues that citizens differ in their tolerance of negative campaigning. Also, candidates vary in the tactics used to attack their opponents, with negative messages varying in their relevance to voters and in the civility of their tone. The interplay between citizens’ tolerance of negativity and candidates’ negative messages helps clarify when negative campaigning will influence citizens’ evaluations of candidates and their likelihood of voting. A diverse set of data sources was collected from U.S. Senate elections (e.g., survey data, experiments, content analysis, focus groups) across several years to test the theory. The tolerance and tactics theory of negativity receives strong empirical validation. First, people differ systematically in their tolerance for negativity, and their tolerance changes over the course of the campaign. Second, people’s levels of tolerance consistently and powerfully influence how they assess negative messages. Third, the relevance and civility of negative messages consistently influence citizens’ assessments of candidates competing for office. That is, negative messages focusing on relevant topics and utilizing an uncivil tone produce significant changes in people’s impressions of the candidates. Furthermore, people’s tolerance of negativity influences their susceptibility to negative campaigning. Specifically, relevant and uncivil messages are most influential for people who are least tolerant of negative campaigning. The relevance and civility of campaign messages also alter people’s likelihood of voting, and the impact of negative messages on turnout is more consequential for people with less tolerance of negativity.

scholarly journals Correction to: Human migration: the big data perspective

2021 ◽  
Author(s):  
Alina Sîrbu ◽  
Gennady Andrienko ◽  
Natalia Andrienko ◽  
Chiara Boldrini ◽  
Marco Conti ◽  
...  
Keyword(s):  
Big Data ◽  
Human Migration

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

2021 ◽  
Vol 37 (1) ◽  
pp. 161-169
Author(s):  
Dominik Rozkrut ◽  
Olga Świerkot-Strużewska ◽  
Gemma Van Halderen
Keyword(s):  
Big Data ◽  
Public Information ◽  
Fundamental Principle ◽  
Data Sources ◽  
Official Statistics ◽  
Development Agenda ◽  
Data Gaps ◽  
Data Source ◽  
Exciting Time ◽  
Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach
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