Otwarty dostęp

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


Zacytuj

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Brodersen, K. H., Hauser, A., & Hauser, M. A. (2017). Package CausalImpact. Google LLC., https://mirror.las.iastate.edu/CRAN/web/packages/CausalImpact/CausalImpact.pdf Search in Google Scholar

BSTS. (2023). Bayesian Structural Time Series | SAP Help Portal. https://help.sap.com/docs/SAP_HANA_PLATFORM/2cfbc5cf2bc14f028cfbe2a2bba60a50/b9972576368640da9831d73a9d749c3b.html Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Konowalczuk, J., Habdas, M., Foryś, I., & Drobiec, Ł. (2021). Wartość nieruchomości w sąsiedztwie lotnisk: Metodyka szacowania szkód i ustalania odszkodowań. Wydawnictwo C. H. Beck. Search in Google Scholar

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 Search in Google Scholar

Limnios, A. C., & You, H. (2021). Can Google Trends improve housing market forecasts? Curiosity: Interdisciplinary Journal of Research and Innovation, 1(2), 21987. Search in Google Scholar

Massicotte, P., Eddelbuettel, D., & Massicotte, M. P. (2016). Package ‘gtrendsR’. R Package. https://cran.curtin.edu.au/web/packages/gtrendsR/gtrendsR.pdf Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

eISSN:
2300-5289
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Business and Economics, Political Economics, other