Have a personal or library account? Click to login
Exploring Public Interest in Limited-Use Areas and Compensation from Airports in Poland: A Google Trends Analysis Cover

Exploring Public Interest in Limited-Use Areas and Compensation from Airports in Poland: A Google Trends Analysis

Open Access
|Mar 2024

References

  1. Batóg, J., Foryś, I., Gaca, R., Głuszak, M., & Konowalczuk, J. (2019). Investigating the impact of airport noise and land use restrictions on house prices: Evidence from selected regional airports in Poland. Sustainability (Basel), 11(2), 412. https://doi.org/10.3390/su11020412
  2. Bełej, M. (2022). Does Google Trends show the strength of social interest as a predictor of housing price dynamics? Sustainability, 14(9), 5601. https://doi.org/10.3390/su14095601
  3. Bełej, M., Cellmer, R., Foryś, I., & Głuszak, M. (2023). Airports in the urban landscape: Externalities, stigmatization and housing market. Land Use Policy, 126, 106540. https://doi.org/10.1016/j.landusepol.2023.106540
  4. Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural timeseries models. https://projecteuclid.org/journals/annals-of-applied-statistics/volume-9/issue-1/Inferring-causal-impact-using-Bayesian-structural-time-series-models/10.1214/14-AOAS788.short https://doi.org/10.1214/14-AOAS788
  5. Brodersen, K. H., Hauser, A., & Hauser, M. A. (2017). Package CausalImpact. Google LLC., https://mirror.las.iastate.edu/CRAN/web/packages/CausalImpact/CausalImpact.pdf
  6. BSTS. (2023). Bayesian Structural Time Series | SAP Help Portal. https://help.sap.com/docs/SAP_HANA_PLATFORM/2cfbc5cf2bc14f028cfbe2a2bba60a50/b9972576368640da9831d73a9d749c3b.html
  7. Carneiro, H. A., & Mylonakis, E. (2009). Google trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557–1564. https://doi.org/10.1086/630200 PMID:19845471
  8. Castelnuovo, E., & Tran, T. D. (2017). Google it up! A google trends-based uncertainty index for the United States and Australia. Economics Letters, 161, 149–153. https://doi.org/10.1016/j.econlet.2017.09.032
  9. Chatzakou, D., Vakali, A., & Kafetsios, K. (2017). Detecting variation of emotions in online activities. Expert Systems with Applications, 89, 318–332. https://doi.org/10.1016/j.eswa.2017.07.044
  10. Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. The Economic Record, 88(s1), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  11. Flavián-Blanco, C., Gurrea-Sarasa, R., & Orús-Sanclemente, C. (2011). Analyzing the emotional outcomes of the online search behavior with search engines. Computers in Human Behavior, 27(1), 540–551. https://doi.org/10.1016/j.chb.2010.10.002
  12. García, C. B., García, J., López Martín, M. M., & Salmerón, R. (2015). Collinearity: Revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), 648–661. https://doi.org/10.1080/02664763.2014.980789
  13. Göhring, W. (2004). The Memorandum ‘Sustainable Information Society’. In Minier, P. & Susini, A. (Hrsg.), Sh@ring – EnviroInfo 2004. http://enviroinfo.eu/sites/default/files/pdfs/vol110/0278.pdf
  14. Habdas, M. (2020a). Odszkodowania dla właścicieli nieruchomości zlokalizowanych w obszarach ograniczonego użytkowania dla lotnisk–wyzwania dotyczące prawidłowego ustalenia zakresu odpowiedzialności odszkodowawczej i podlegającej kompensacji szkody–część 1. Przegląd Sądowy, 5, 7–31.
  15. Habdas, M. (2020b). Polish dilemmas in compensating landowners in the vicinity of airports–black letter law vs. Law in action. Studia Prawnicze KUL, 4, 27–61.
  16. Huarng, K.-H., Hui-Kuang Yu, T., & Rodriguez-Garcia, M. (2020). Qualitative analysis of housing demand using Google trends data. Ekonomska Istrazivanja, 33(1), 2007–2017. https://doi.org/10.1080/1331677X.2018.1547205
  17. Khafidli, M. K., & Choiruddin, A. (2022). Forecast of aviation traffic in Indonesia based on Google Trend and macroeconomic data using long short-term memory. 2022 International Conference on Data Science and Its Applications (ICoDSA), 220–225. https://doi.org/10.1109/ICoDSA55874.2022.9862894
  18. Konowalczuk, J., Habdas, M., Foryś, I., & Drobiec, Ł. (2021). Wartość nieruchomości w sąsiedztwie lotnisk: Metodyka szacowania szkód i ustalania odszkodowań. Wydawnictwo C. H. Beck.
  19. Li Long, C., Guleria, Y., & Alam, S. (2021). Air passenger forecasting using Neural Granger causal Google Trend queries. Journal of Air Transport Management, 95, 102083. https://doi.org/10.1016/j.jairtraman.2021.102083
  20. Limnios, A. C., & You, H. (2021). Can Google Trends improve housing market forecasts? Curiosity: Interdisciplinary Journal of Research and Innovation, 1(2), 21987.
  21. Massicotte, P., Eddelbuettel, D., & Massicotte, M. P. (2016). Package ‘gtrendsR’. R Package. https://cran.curtin.edu.au/web/packages/gtrendsR/gtrendsR.pdf
  22. Matias, Y. (2013). Nowcasting with Google Trends. International Symposium on String Processing and Information Retrieval, 4. https://doi.org/10.1007/978-3-319-02432-5_4
  23. Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google Trends research: Systematic review. Journal of Medical Internet Research, 20(11), e270. https://doi.org/10.2196/jmir.9366 PMID:30401664 https://doi.org/10.2196/preprints.9366
  24. Miles, J. (2014). Tolerance and Variance Inflation Factor. In R. S. Kenett, N. T. Longford, W. W. Piegorsch, & F. Ruggeri (Eds.), Wiley StatsRef: Statistics Reference Online (1st ed.). Wiley., https://doi.org/10.1002/9781118445112.stat06593
  25. Olszak, C., & Ziemba, E. (2009). The information society development strategy on a regional level. Issues in Informing Science and Information Technology, 6, 213–225. https://doi.org/10.28945/1054
  26. Rizun, N., & Baj-Rogowska, A. (2021). Can web search queries predict prices change on the real estate market? IEEE Access: Practical Innovations, Open Solutions, 9, 70095–70117. https://doi.org/10.1109/ACCESS.2021.3077860
  27. Scott, S. L., & Varian, H. R. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4. https://doi.org/10.1504/IJMMNO.2014.059942
  28. Wilcox, R. R. (2003). Least squares regression and Pearson’s correlation. In Applying Contemporary Statistical Techniques, 173–206. Elsevier. https://doi.org/10.1016/B978-012751541-0/50027-4
  29. Woloszko, N. (2020). Tracking activity in real time with Google Trends. https://www.oecd-ilibrary.org/economics/tracking-activity-in-real-time-with-google-trends_6b9c7518-en
  30. Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences of the United States of America, 112(47), 14473–14478. https://doi.org/10.1073/pnas.1515373112 PMID:26553980
Language: English
Page range: 64 - 76
Submitted on: Dec 7, 2023
|
Accepted on: Mar 20, 2024
|
Published on: Mar 23, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Mirosław Bełej, published by Real Estate Management and Valuation
This work is licensed under the Creative Commons Attribution 4.0 License.