Editorial for Special Issue on “Big Data and Smart Cities”

2019 ◽  
Vol 17 ◽  
pp. 33-34 ◽  
Author(s):  
Magdalini Eirinaki ◽  
Jerry Gao ◽  
Latifur Khan ◽  
Sourav Mazumder ◽  
Katerina Potika
Keyword(s):  
Big Data ◽  
Author(s):  
Arun Sangaiah ◽  
Ford Gao ◽  
Krishn Mishra

2015 ◽  
Author(s):  
Fahimeh Tabatabaei ◽  
Tahir Wani ◽  
Nastran Hajiheidari
Keyword(s):  
Big Data ◽  

Big Data ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 87-88
Author(s):  
Priyan Malarvizhi Kumar ◽  
Hari Mohan Pandey ◽  
Gautam Srivastava

2021 ◽  
Vol 176 ◽  
pp. 110921
Author(s):  
Apostolos Ampatzoglou ◽  
Peng Xin
Keyword(s):  
Big Data ◽  

2021 ◽  
Vol 24 ◽  
pp. 100192
Author(s):  
Mariagrazia Fugini ◽  
Jacopo Finocchi ◽  
Paolo Locatelli

Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn

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.


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