The Usability of Big Data in Statistical Studies of the Population in the Conditions of War
Keywords:
official statistics, demographic statistics, social statistics, analysis of survey, analysis of social media, analysis of health data, geospatial analysis, analysis of population
Abstract
The war in Ukraine aggravated economic, social, geopolitical, and environmental problems, caused a devastating blow on physical and mental health of the Ukrainian population, worsened its quantitative and structural parameters. Apart from violations of human rights and the occurrence of mental traumas among adults and children alike, terrorist methods of warfare used by the Russian Federation led to heavy losses of the civil population and forced replacements in search for safety.The conventional problems of the Ukrainian official statistics were added by another one posed by the war: the impossibility to study the population and their living standards in the conditions of war. The authors drew attention to the need to fill the gaps in statistical data by use of alternative war-specific sources in course of the statistical studies of the population. When investigating the issue of implementing big data in the social and demographic statistics, emphasis was made on the methods that could be fit for practical applications now: analysis of surveys, analysis of social media, analysis of health data, geospatial analysis and population analysis. The study allowed for highlighting issues of using microdata, intellectual analysis of text or machine training by the official statistics. It was revealed that the issue of implementing data in the official statistics, for population studies in particular, was but a matter of time, considering the rapid development of digitalizing processes in Ukraine.
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References
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9. Keller, S. A., Koonin, S. E., & Shipp, S. (2012). Big data and city living - what can it do for us? Significance. Special Issue: Big Data, 9, 4, 4-7. Retrieved from https://rss. onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2012.00583.x
10. Karn, A., Shrestha, A., Pudasaini, A., Mahara B., & Jaiswal, А. (2018). Statistic-Based Sentiment Analysis of Social Media Data. International Research Journal of Innovations in Engineering and Technology (IRJIET), 2, 5, 28-32. Retrieved from https://www. researchgate.net/publication/333943106_Statistic-Based_Sentiment_Analysis_of_ Social_Media_Data
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13. Gao, Ya., Xie, Z., & Li, D. (2021). Electronic Cigarette Users’ Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data. JMIR Public Health Surveillance, 7 (1):e24859. doi: 10.2196/24859
14. Cho, S. E., Jung, K. & Park, H. W. (2013). Social Media Use during Japan’s 2011 Earthquake: How Twitter Transforms the Locus of Crisis Communication. Media International Australia, 149 (1), 28-40. Retrieved from https://journals.sagepub.com/ doi/10.1177/1329878X1314900105
15. Valdez, D., ten Thij, M., Bathina, K., Rutter, L. & Bollen, J. (2020). Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data. Journal of Medical Internet Research, 22 (12):e21418. Retrieved from https://www.jmir.org/2020/12/e21418/
16. Marshall, J., Chahin, A., & Rush, B. (2016). Review of Clinical Databases. Secondary Analysis of Electronic Health Records. MIT Critical Data. Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-43742-2_2
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19. Verheij, R., Curcin, V., Delaney, B., & McGilchrist, M. M. (2018). Possible sources of bias in primary care electronic health record data use and reuse. Journal of Medical Internet Research, 20 (5):e185. doi: 10.2196/jmir.9134
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21. The National Patient-Centered Clinical Research Network. PCORnet. Retrieved December 20, 2022 from https://pcornet.org/
22. National Health Services (NHS in England). www.england.nhs.uk. Retrieved December 20, 2022 from https://www.england.nhs.uk/
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25. National Surgical Quality Improvement Program The American College of Surgeons Retrieved from https://www facs org/quality-programs/data-and-registries/acs-nsqip/
26. Wearable Technology Market Size, Share & Trends Analysis Report By Product (Head & Eyewear, Wristwear), By Application (Consumer Electronics, Healthcare), By Region (Asia Pacifiic, Europe), And Segment Forecasts, 2023-2030. (2023) . www. grandviewresearch com Retrieved from https://www grandviewresearch com/industry- analysis/wearable-technology-market
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28. Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized Medicine, 15, 5. Retrieved from https://www.futuremedicine.com/ doi/10.2217/pme-2018-0044
29. Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. (2020). Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Health, 2(2):e85-e93. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048388/
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31. Byers, J. S., & Gill, J. (2022). Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions. Mathematics, 10 (21), 4116. Retrieved from https://www. mdpi.com/2227-7390/10/21/4116
32. Sarioglo, V., & Ogay, M. (2022). Approach to population estimation in Ukraine using mobile operators’ data. Statistics in Transition new series and Statistics of Ukraine. Joint Special Issue: A New Role for Statistics, 23, 5, 185-198. Retrieved from https://sit.stat. gov.pl/SiT/OnlineFirst/013%20Sarioglo%20V%20sit%2023%205%20specjalny.pdf
33. Okmi, M., Por, L., Ang, T. F. & Ku, Ch. S. (2023). Mobile Phone Data: A Survey of Techniques, Features, and Applications. Sensors, 23 (2), 908. Retrieved from https:// www.mdpi.com/1424-8220/23/2/908
34. Aasa, A., Kamenjuk, P., Saluveer, E, Simbera, J., & Raun J. (2021). Spatial interpolation of mobile positioning data for population statistics. Journal of Location Based Services, 15 (4), 239-260. Retrieved from https://www.tandfonline.com/doi/full/10.1080/17489 725.2021.1917710
35. Salgado, D., Sanguiao, L., Oancea, B., Barragan, S., & Necula, M. (2021). An end-to-end statistical process with mobile network data for official statistics. EPJ Data Science, 10, 20. Retrieved from https://doi.org/10.1140/epjds/s13688-021-00275-w
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39. American Community Survey. (2021). Vision and Eye Health Surveillance System (VEHSS). Centers for Disease Control and Prevention. Retrieved from https://www.cdc. gov/visionhealth/vehss/data/national-surveys/american-community-survey.html#print
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41. Smart Nation Singapore. Smart Nation and Digital Government Office. Retrieved December 20, 2022 from https://www.smartnation.gov.sg/
42. China Population Information and Research Center (CPIRC). www.pop.upenn.edu. Retrieved December 20, 2022 from https://www.pop.upenn.edu/resource/china- population-information-and-research-center-cpirc
Abstract views: 628 PDF Downloads: 476
Published
2023-07-27
How to Cite
HOROBETS, O., & CHERVONA, S. (2023). The Usability of Big Data in Statistical Studies of the Population in the Conditions of War. Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, (1-2), 5-16. https://doi.org/10.31767/nasoa.1-2-2023.01
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