Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. Article 5); there were two types of spatial effects in regional tourism growth, namely spatial spillover and spatial heterogeneity21. In the meantime, to ensure continued support, we are displaying the site without styles Local indicators of spatial association (LISA) for tourism demand in (a) 2011 and (b) 2018. Results in Table3 clearly show that utilizing online search traffic in forecasting tourism demand raises the performance significantly, as the RMSE is reduced for all sending countries and at any forecasting horizon, if autoregressive models were extended by search query series. Urban park green space area-Highway mileage, Average daily hours of sunshine-Highway mileage, Average daily hours of sunshine-Railroad mileage, Average wage of employees-Highway mileage, The number of museums-Average daily hours of sunshine, Average wage of employees-Urban park green space area, Urban park green space area-The number of museums were two-factor none-linearly enhanced interactions with interaction Q-statistic values greater than 0.98, a significant increase in the influence of synergy on tourism demand. ), i.e. Pompili, T., Pisati, M. & Lorenzini, E. Determinants of international tourist choices in Italian provinces: A joint demandsupply approach with spatial effects. Analysis of spatial patterns and driving factors of provincial tourism queries related to skiing) 3 months prior to departure without mentioning any destination, search queries are formulated more precisely 2 months ahead of departure (i.e. Notes on continuous stochastic phenomena. We have updated the search filter for evidence-based literature in our databases. 11). The authors declare no competing interests. Google Scholar, Bangwayo-Skeete PF, Skeete RW (2015) Can Google data improve the forecasting performance of tourist arrivals? Highway mileage enhanced the coverage of inter-regional connections and compressed tourists' time costs to their destinations; on the other hand, it also increased the polarization of intra-regional connections, thus benefiting the central regions rather than the peripheral ones from the traffic44. The three formed a concrete network of interactions that influenced the spatial distribution of tourism demand. Therefore, the results reveal important implications for tourism managers and policy makers: Google Trends data can be effectively used as a tool for forecasting short- and mid-term tourism demand as well as for the detection of future (i.e. J. 3, 6). This is to ensure that we give you the best experience possible. The Future of Tourism: Innovation and Sustainability, Heidelberg: Springer, 2018 =*=*=*=*= Abstract This contribution examines the wicked problem of adequately measuring tourism in its many facets. Headstart | ANC (13 July 2023) - Facebook 2012; Yang et al. Holden Day, San Francisco, Carrire-Swallow Y, Labb F (2013) Nowcasting with Google Trends in an emerging market. The tourism demand network is increasingly prosperous and gradually develops from disorderly to orderly, with eastern regions as the main source of tourists. Traffic conditions, social-economic development level, and physical conditions compose a constant and robust interaction network, which dominates the spatial distribution of tourism demand in different development stages through different interactions. The Y-axis is the origin, and the X-axis is the abbreviation that replaces the destination, the name of each province. Manag. 2010). Accordingly, a fraction of data entries is used as training data, while a consecutive fraction of data entries is used as test data (Liu 2008). First, this study analyzed the drivers of tourism demand at the provincial level in China, with prominent medium- and long-distance tourism characteristics. specified alternatives for the given keyword). Other acquired queries were found to be spelled either with or without special characters (e.g. The authors use international tourist arrivals and tourist expenditures . A measure of spatial stratified heterogeneity. Sci Rep 12, 2260 (2022). Influence of Tourism Seasonality and Financial Ratios on Hotels' Exit Article The tourism resource addresses were geocoded into spatial point data, and kriging interpolation was implemented for spatial interpolation to generate the A-class tourism resources index raster. As can be seen from Fig. Different hierarchical interaction networks are visualized in Fig. While in northeast China, the number of L-L clusters increased, and the provincial growth rate of tourism demand in low-value agglomeration was much lower than the national average during the study period. Asia Pac J Tour Res 5(2):110, Fuchs M, Hpken W, Lexhagen M (2018) Business Intelligence for Destinations: Creating Knowledge from Social Media. (2015), search query series for each sending country were aggregated to compound search indices by shifting single search query series by the most appropriate time lag. Tourism is an essential driver of world economic development. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Several aspects need to be considered in related follow-up studies. 57, 593610 (2020). When using resulting search indices and past arrivals as input to demand prediction and in contrast to existing (autoregressive) approaches, an automatic selection of the most appropriate time lags is performed by a backward feature selection mechanism. Analysis of international tourist arrivals in China: The role of World Heritage Sites. Res 22, 2641 (2020). When modelling time series with statistical approaches it is common to ensure that the time series is within probabilistic limits of stationarity. From the tourism supply side, the geographical and spatial clustering of tourism-related services produces spatial dependence and scale effects at the macro level, thus providing tourists with more acceptable prices and convenient services to achieve regional tourism growth. In 2011, the H-H cluster was in Yunnan Province in southwestern China, the high values surrounded by low values cluster (H-L) appeared in Beijing, and the L-L cluster was in Heilongjiang Province in northeastern China. Handbook of Regional Growth and Development Theories (2019). A combination of spatial autocorrelation and Geodetector is utilized to recognize the spatiotemporal distribution patterns of tourism demand in 2011 and 2018 in 31 provinces of mainland China and detect its driving mechanisms. Therefore, according to Pan et al. Recent econometric forecasting studies have shown strong relationships between tourism demand and the following leading economic indicators: Consumer price index, gross domestic product (as proxy for tourists income), exchange rates, interest and unemployment rate, money supply (M3), and export/import rates (Song and Li 2008, p. 211; Cho 2001). This paper presents a novel approach that extends autoregressive forecasting models by considering travellers' web search behaviour as additional input for predicting tourist arrivals. Third, from the perspective of spatially stratified heterogeneity, this study taps the influence of the main driving factors and the interaction between different potential factors on the spatial heterogeneity of tourism demand. As highlighted above, analysing the correlation between tourist arrivals and query series with different time lags enables conclusions about consumers online search behaviour (Fesenmaier et al. However, these publications are mainly in the . Ann. Second, Google Trends data is normalized between zero and 100 for the selected time-period and location. Discussion and conclusions Abstract Purpose The purpose of this paper is to examine the impact of various cultural amenities on tourism demand in 168 European cities. Yang, X., Bing, P., Evans, J. 2012). Springer, New York, pp 411423, Mukherjee C, White H, Wuyts M (1998) Econometrics and data analysis for developing countries. J Econom 39(12):199211, Grnroos C (2008) Service logic revisitedwho creates value? Repo If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. States of feeling or motivational forces among tourists recognized to be changed in postmodern times: preferences, interests, values and experiences of tourists (Dujmovi, 2015). Inf. Res. & Ma, M. Evaluating impact of air pollution on Chinas inbound tourism industry: A spatial econometric approach. Next, each query \(R_{i}^{n < 0}\) was shifted according to n time lags towards the arrivals series, while queries with \(R_{i}^{0}\) were excluded from the data set, since as suggested by Yang et al. tourist arrivals) to identify most significant time differences between arrivals and respective search queries. Glossary of tourism terms | UNWTO Internet Big data have derived a tremendous amount of Internet operation records of individual Internet users, which provide us with new means of observation. Baidu index (https://index.baidu.com/) has better accuracy in the Greater China region for measuring tourism demand, and keywords query it. Additionally, the KwiatkowskiPhillipsSchmidtShin (KPSS) test has been applied (Hill et al. However, predicting future tourism demand is a difficult and non-trivial task, due to the lack of historical data, seasonal fluctuations, influences of unexpected events, the variety of input factors and the complexity of visitors travel decision-making process (Song et al. (Mathieson and Wall, 1982) Tourism is a dynamic field. 7, significant stratification of tourism demand in 2011 and 2018 on a local spatial basis in China (absolute value of Z-score>2.56, p-value<0.01), consisting mainly of high-high value clusters (HH) and low-low value clusters (L-L). Therefore, before building the regression model for evaluating the predictive power of the indices, the series were checked for stationarity by applying the Augmented DickeyFuller (ADF) test, which tests an autoregressive model for the existence of unit-roots as an indicator for non-stationarity (Baddeley and Barrowclough 2009). of trips = No. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. 74, 829836 (1979). Accordingly, keywords suggested by Googles Keyword Planner tool for /re were obtained to generate appropriate seed queries that re visitors from different sending countries are likely to use. The level of social and personal economic development and transportation conditions also increased influence to 2011. Res. demonstrated that Internet data has a significant driving effect on tourism demand research, with search engine data being the most common Internet data source used by researchers19. This study proposes a novel statistical method, the monotonicity test, to assess whether the nowcasting errors obtained from the ordinary least squares, generalised dynamic factor model and. Rev Econ Stat 82(4):540554, Frechtling DC (2002) Forecasting tourism demand. In 2011 the tourists' origins were concentrated in eastern China, and there were two clusters of high-intensity tourism flows distributed from Beijing and the ring-Beijing area to Yunnan and Hainan; this phenomenon altered significantly in 2018, with high-intensity tourism flows concentrated on one cluster of tourism flows from eastern China to Yunnan Province. Value-added of tertiary industry-Average daily temperature, GDP per capita-Average daily hours of sunshine, GDP per capita-Value added of tertiary industry, GDP per capita-Urban road area, GDP per capita-The number of museums, GDP per capita-Urban park green space area, were two-factor none-linearly enhanced and GDP per capita-Average daily hours of sunshine was two-factor enhanced with interaction Q-statistic values greater than 0.98. Correspondence to Sci. 22, 110 (2017). Springer, New York, pp 253265, Song H, Li G (2008) Tourism demand modelling and forecasting: a review of recent research. The expressions are as follows. means you agree to our use of cookies. As a consequence, queries were transformed in correspondence of their word stem (e.g. In this paper, GeoDetector was adopted to analyze the factors affecting the spatial distribution of tourism demand in 31 provinces of China (Fig. In addition, Granger causality tests were performed to examine the evidence for predictive power between constructed search indices and tourist arrival series. Moran, P. A. Article Methods and measurement techniques in tourism - ResearchGate J. Contemp. More precisely, for tourism businesses it is pivotal to respond promptly to upcoming demand, thus, making limited resources available and ready for co-creative service production processes (Fitzsimmons and Fitzsimmons 2001; Grnroos 2008; Chekalina et al. DMO Development Measuring Tourism Impacts While Pan et al. ADS As a theoretical contribution, the study presented a novel approach to construct tourism-related search indices from Google Trends data as additional input to predict tourism demand. Measuring the Sustainability of Tourism With the support of the United Nations Statistics Division (UNSD), UNWTO has launched the initiative Towards a Statistical Framework for Measuring the Sustainability of Tourism (MST). 2015; Li et al. Section3 discusses methodological issues and related techniques of data collection and preparation, respectively. By investigating the spatial distribution pattern of domestic inter-provincial tourism demand in China, we recognized heterogeneity in the sensitivity of long-distance tourism flows to the distance in different intensities (Fig. Physical conditions existed at the core of the interaction network, mainly in the form of average daily temperature, which interacted extensively with other factors and was the central driver influencing the distribution of tourism demand, indicating that tourism comfort is the basis of the natural scenery tourism attraction.
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