Можливості використання великих даних у рамках статистичного вивчення населення в умовах війни
Ключові слова:
офіційна статистика, демографічна статистика, соціальна статистика, аналіз результатів опитування, аналіз соціальних медіа, аналіз даних про здоров’я, геопросторовий аналіз, аналіз населення
Анотація
У статті акцентується увага на неможливості повного і достовірного вивчення населення та умов його життя в умовах війни. Автори звертають увагу на заповнення прогалин, які виникли внаслідок війни, у статистичних даних з допомогою альтернативних джерел у рамках статистичного вивчення населення. Досліджуючи питання імплементації великих даних у соціальну та демографічну статистику, було зосереджено увагу на тих підходах, які наразі можуть використовуватися на практиці: аналізі результатів опитування; аналізі соціальних медіа; аналізі даних про здоров’я; геопросторовому аналізі; аналізі населення. У рамках дослідження розкрито також питання використання офіційною статистикою мікроданих, інтелектуального аналізу тексту, машинного навчання. Обґрунтовано, що імплементація великих даних у процеси статистичного вивчення населення - це лише питання часу, з огляду на стрімкий розвиток цифровізаційних процесів в Україні.
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Посилання
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4. Billari, F. C., & Zagheni, E. Big Data and Population Processes: A Revolution? (2017). Proceedings of the Conference of the Italian Statistical Society (28-30 June 2017, Florence). Statistics and Data Science: new challenges, new generations. A. Petrucci, R. Verde (Eds.). (pp. 167-178). Retrieved from https://media.fupress.com/files/ pdf/24/3407/3407_11724
5. UNESCAP. (2021). Big data for population and social statistics. Stats Brief, 29. Retrieved from https://www.unescap.org/sites/default/d8files/knowledge-products/Stats_Brief_ Issue29_Big_data_for_population_and_social_statistics_Apr2021.pdf
6. Magpantay, E. (2022). Information society indicators for the SDGs using mobile phone big data. Mobilizing Big Data and Data Science for the Sustainable Development Goals. Session 1.4. (26 January 2022, Dubai). Retrieved from https://unstats.un.org/ bigdata/blog/2021/expo2020/sessions/1-4/presentations/Magpantay_Information%20 society_session1.4.pdf
7. Ashford, L. S., Kaneda, T, & Letouze, E. (2022). Demystifying Big Data for Demography and Global Health. Population Bulletin, 76, 1. Population Reference Bureau. Retrieved from https://www.prb.org/wp-content/uploads/2022/02/population-bulletin-vol-76-no1- demystifying-big-data.pdf
8. Ruggles, S. (2014). Big Microdata for Population Research. Demography, 51 (1), 287-297. Retrieved from https://www.jstor.org/stable/42919999
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
11. What is social media analytics? IBM. Retrieved December 20, 2022 from https://www. ibm.com/topics/social-media-analytics
12. Olteanu, A., Castillo, C., Diaz, F. & Kiciman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2, 1-33. Retrieved from https://www.frontiersin.org/articles/10.3389/fdata.2019.00013/full
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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
17. Upadhyay S., & Hu H. (2022). A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences. Health Services Insights, 15, 1-7. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC8902175/
18. Gianfrancesco, M. A., & Goldstein, N. D. (2021). A narrative review on the validity of electronic health record-based research in epidemiology. BMC Medical Research Methodology, 21, 234. DOI https://doi.org/10.1186/s12874-021-01416-5
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
20. Medical Information Mart for Intensive Care. mimic.mit.edu. Retrieved December 20, 2022 from https://mimic.mit.edu/
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/
23. eICU Collaborative Research Database. eicu-crd.mit.edu. Retrieved December 20, 2022 from https://eicu-crd.mit.edu/
24. VA Technical Reference Model v 23.2. U. S. Department of Veterans Affairs. Retrieved from https://www.oit.va.gov/Services/TRM/TRMRedirectPage.aspx?type=WA&tid=8338A
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
27. Huhn, S., Matzke, I., Koch, M., Gunga, H. Ch., Maggioni, M. A., & Sie, A. et al. (2022). Using wearable devices to generate real-world, individual-level data in rural, low-resource contexts in Burkina Faso, Africa: A case study. Front Public Health, 10:972177. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561896/#B2
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/
30. Geospatial Analytics Definition. HEAVY.AI. Retrieved December 20, 2022 from https:// www.heavy.ai/technical-glossary/geospatial-analytics
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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37. Tiru, M. (2014). Overview of the Sources and Challenges of Mobile Positioning Data for Statistics. unstats.un.org. Retrieved from https://unstats.un.org/unsd/trade/events/2014/ beijing/Margus%20Tiru%20-%20Mobile%20Positioning%20Data%20Paper.pdf
38. Opryliudneno rezultaty otsinky chyselnosti naiavnoho naselennia Ukrainy [The results of the assessment of the current population of Ukraine have been published]. (2020). Government portal. Retrieved from https://www.kmu.gov.ua/news/oprilyudneno- rezultati-ocinki-chiselnosti-nayavnogo-naselennya-ukrayini [in Ukrainian].
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
40. Data Science for public good. Data Science Campus. Retrieved December 20, 2022 from https://datasciencecampus.ons.gov.uk/
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
Переглядів анотації: 628 Завантажень PDF: 476
Опубліковано
2023-07-27
Як цитувати
HOROBETS, O., & CHERVONA, S. (2023). Можливості використання великих даних у рамках статистичного вивчення населення в умовах війни. Науковий вісник Національної академії статистики, обліку та аудиту, (1-2), 5-16. https://doi.org/10.31767/nasoa.1-2-2023.01
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