J. 4). The findings of this paper can provide a reference for regional tourism planning decision-makers. 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. Tour Manag 29:203220, Song H, Li G, Witt StF, Fei B (2010) Tourism demand modelling and forecasting: how should demand be measured? In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. of trips8average length of stay Measures of Demand: D = f (Propensity, resistance) Propensity = person's willing to travel Resistance = relative attractiveness of destination It can also be measured by: Tourism Information System Res. The nighttime light index (0.0054) had minimal effects on tourism demand. (2017), the queries were filtered by Hurst exponent in order to assure the search indices to be constructed following the same auto-correlative patterns as its corresponding tourist arrival series. The interactions between the factors are edges, and the Q-statistic values for interaction measured the weight of the edges. 2000; Edgell et al. (2016) proposed a method for noise reduction of search query series which builds on the methodology by Yang et al. How To Attract Wealth, Health And Happiness? 5, where the Euclidean distances of flows were calculated in the Beijing_1954_3_Degree_GK_Zone_35 coordinate system. https://doi.org/10.1016/j.tourman.2009.08.008 (2010). Based on the results of the de-trended cross-correlation analysis (DCCA), the queries were weighted by their maximum cross correlation coefficient by multiplying each query i by \(R_{i}^{n}\). Accordingly, de-trended cross-correlations were calculated between the arrival series and each of the search queries series at lag {0, 1, 2, 3, 4, 5, 6), respectively. 2, in terms of measuring tourism demand, most of the macro studies have used tourist arrivals to measure tourism demand, as indicated by Song et al. Despite that there is no standard methodology for pre-processing web search data, three main necessary tasks in pre-processing search engine queries for prediction purposes are found in the literature: Keyword selection First, researchers start selecting domain specific keyword candidates either by using domain specific knowledge or web scraping and text mining approaches to capture domain specific grammar, or by the help of keyword recommendations from search engine providers (Liu et al. 75, 106119 (2019). The optimization algorithm for stratifying geographic proxy variables parameters proposed by Song et al. Therefore, the third typical task when applying search engine data for forecasting purposes is the construction of an appropriate data set consisting of input variables with significant predictive power. Since Levi and Ruka are popular skiing resorts in Finland, those queries indicate that Finish travellers more critically evaluate various ski resorts before they finally choose to visit re. GeoDetector is an advanced spatial statistical analysis model used to study factors' impact on diseases at a specific geographical area early31. If your library has implemented LibKey you will see in-line links on Search Results and the Abstract pageto connect you seamlessly with your librarys subscription access to the article. Similarly, exponential smoothing models have appeared in the literature. By doing so, it was found that some queries pointed to the same topic, although they were formulated differently. Anselin, L. Local indicators of spatial associationLISA. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand. Measuring Total Tourism Demand - General Guidelines Vol. 1 (English Hence, this study puts a clear emphasis on adding search traffic data as an additional input to predicting tourist arrivals, and not on comparing different forecasting approaches like linear regression and machine learning methods, like artificial neural networks or even non-parametric approaches from the area of deep learning. Technological innovation13 and knowledge spillovers14 cannot be ignored in driving tourism productivity and making tourism demand grow. The results proved that the Baidu index could more accurately reflect tourism demand's spatial and temporal characteristics. Strateg https://doi.org/10.1016/j.tourman.2007.07.016 (2008). The document is divided in two parts. & Ord, V. J. Spatial Processes: Model and Application (1981). Resistance = Yunnan and Guizhou in the southwest and Gansu in the west grew at a much higher rate than the national average; Heilongjiang, Jilin, and Liaoning in the northeast grew at a much lower rate than the average; and Hainan in the south became the only province in the country where tourism demand decreased. The tourism demand network is increasingly prosperous and gradually develops from disorderly to orderly, with eastern regions as the main source of tourists. World Travel & Tourism Council, London, Yang Y, Pan B, Song H (2014) Predicting hotel demand using destination marketing organizations web traffic data. On a global scale, nearly every tenth job relates directly or indirectly to the travel and tourism industry (WTTC 2016). 2010). The regional economic development and construction were the main drivers, followed by the size of the population and the base of tourism services, and again by the traffic conditions, with the influence of natural factors and tourism resources being minimal in 2011. Tour. Thus, an increasing number of tourism researchers are demonstrating that, in particular, Google search engine traffic has the potential to greatly increase forecasting accuracy (Bangwayo-Skeete and Skeete 2015; nder and Gunter 2016; Hpken et al. The keyword database consisted of the combinations of destination provinces name+"tourism". 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. In this study, prediction accuracy is operationalized by the root mean square error (RMSE), which according to Frechtling (2002) and Kim and Kim (2016), is among the most commonly used metrics when evaluating the performance of time series forecasting. Please email CABIsupport@cabi.orgwith your LibKey API Key and LibKey Static ID whichmay berequested from Third Ironsupport@thirdiron.com. Window size was specified as s=25 to capture long-term dependencies between both series. ITS Time to Get Back, How to apply for Senior Research Fellowship after JRF, 10 Major Tourism Highlights in Indian Budget 2022, 10 MOST Frequent Doubts while thinking about pursuing PhD in Tourism Sector, Highlights of National Education Policy 2020. Thus, it can only consider linear relations in data, wherefore it cannot capture issues such as non-stationary time series. ISSN 2045-2322 (online). The existence of the H-L cluster in Beijing in 2011 and the disappearance of this cluster in 2018 indicated that Beijing had strong competitiveness in the region in the early stage, and the weakening polarization effect and the increasing diffusion effect diminished in the later stage when tourism demand was gradually distributed in a balanced manner in the Beijing-ring region. Song, Y., Wang, J., Ge, Y. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Additionally, co-integration relationships were tested by applying the Johansen test to check whether any further data transformations were necessary in case a time series was found to be non-stationary (ibid 2011). 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. 9b,d), Average daily temperature-Urban road area (0.9949), Urban park green space area-Average daily temperature, Average daily temperature-Highway mileage, Total population-Average daily temperature, GDP per capita-Highway mileage, Average wage of employees-Highway mileage, GDP per capita-Total population, The number of museums-Average daily temperature, were two-factor non-linearly augmented interaction patterns with interaction Q-statistic greater than 0.99, which almost wholly control the spatial distribution of tourism demand. Ind. While socio-economic development furnishes the foundation for breeding urban humanistic tourism, the average wage of employees is an efficient indicator of the region's economic development41, where high-quality tourist reception, available public information, travel safety, and diversified recreational convenience services are important factors attracting tourists. At the broadest level, tourism affects the economy through employment and investment. The ten combinations generated the interaction networks with the most explanatory power. Rec. Tourism Demand Modelling and Forecasting: How Should Demand Be Measured tourist arrivals) to identify most significant time differences between arrivals and respective search queries. To obtain Hpken, W., Eberle, T., Fuchs, M. et al. J Travel Res 53(4):433447, Yang X, Pan B, Evans JA, Lv B (2015) Forecasting Chinese tourist volumes with search engine data. Asia Pac. Constable, London, Kim S, Kim A (2016) A new metric of absolute percentage error for intermittent demand forecasts. All authors reviewed and commented on the manuscript. Being one of the important areas in tourism research, demand modelling and forecasting has attracted much attention of both academics and practitioners (Weiermair and Fuchs 1998; Song and Li 2008). In summary, we can catch that the domestic tourism network in China during the period of rapid economic development showed a remarkable complex pattern, with the origins of tourists consolidated in the densely populated and economically developed areas in the east and the destinations distributed in the first-tier cities, and remote areas in the central and western regions. It is now well established from various literature that analytical methods have been implemented to address the single driving mechanisms of tourism demand. In: Schegg R, Stangl B (eds) Information and communication technologies in tourism, Springer, Cham, pp 187199, Hpken W, Eberle Th, Fuchs M, Lexhagen M (2018) Search engine traffic as input for predicting tourist arrivals. following the Hurst exponent, all arrival and corresponding search index data sets are in the range 0.51. PDF CHAPTER 3 TOURISM DEMAND AND SUPPLY 3.1 Introduction At the international level, there are several reasons for this . New evidence from China. As there is no difference whether users search for skiing in sweden or sweden skiing or ski sweden, these queries are likely to point at the same topical subject: Skiing in Sweden. J.Z. As potential travellers extensively search the web before visiting a specific destination (Fesenmaier et al. 2010). Article The number of museums (0.3932), value-added of tertiary industry (0.3892), Average wage of employees (0.3479), Total population (0.3429), Highway mileage (0.3024) were also significant drivers of tourism demand. The Economics of Tourism Destinations : Theory and Practice - Google Books Google Scholar. Research Note: Nowcasting Tourist Arrivals in Barbados - Just Google it Li et al. It is important to note that the results of the ShapiroWilk test do by no means affect or question the reliability of the tests on stationarity or co-integration (cf. Manag. 60, 336353 (2021). However, these publications are mainly in the international market (tourists traveling between countries). It characterized the total tourism demand of that province. Socio-economic development followed closely behind in physical conditions, with the average wage of employees representing the general level of economic development in the region and the prosperity of the tertiary sector, characterizing the level of tourism services, as well as being the foundation for driving the city to be a tourist attraction. Green space coverage index (0.0616), A-class scenic spot index (0.0321). International Tourism Highlights, 2020 Edition (World Tourism Organization (UNWTO), 2021). Finally, Discussion and Conclusions sections are the discussion and conclusion of the findings, respectively. Nevertheless, the results of the Johansen test clearly show the existence of co-integration relationships between the constructed search indices and their corresponding arrival series, as the null hypothesis (i.e. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. Marrocu, E. & Paci, R. Different tourists to different destinations. It indicates that these combinations play a decisive role in the spatial distribution of tourism demand. Improving Tourist Arrival Prediction: A Big Data and Artificial Neural. skiing re vs. skiing in re). The NPP-VIIRS-like NTL Data from Harvard Dataverse (https://library.harvard.edu/services-tools/harvard-dataverse/), which represents the intensity of human activity at night, the elevation data is from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/), the climate data is from the National Meteorological Science Data Center of China (http://data.cma.cn/), and the green space coverage index is from the USGS (https://www.usgs.gov/). time series of tourist arrivals) and x={x1, x2, , xn} is an indicator time series (i.e. While travellers from Denmark start to search for inspiration first by activity-related topics (i.e. Cooper (2004:76) defines demand as "a schedule of the amount of any product or service that people are willing and able to buy at each specific price in a set of possible prices during some specified period of time". Song et al. Tour. 2012). Internet Explorer). Tour Manag 46:454464, Article J. All proxy variables were free of being counted according to provincial administrative boundaries, and the raw data were in a GeoDetector model where sampling points would capture the values of different variables. J. Destination Market. While Pan et al. Park, R. E. Race and Culture (The Free Press, 1950). Evidence from spatial interaction models. Additionally, search engine data can be used by local tourism suppliers for marketing purposes to better understand the decision-making process of travellers when choosing a specific destination, e.g. Tour. From the perspective of the interaction of proxy variables, a strong triangular network community was formed in 2011 by average wages of employeesaverage daily hours of sunshine-highway mileage. CABI is a registered EU trademark. More precisely, firstly, the study evaluates whether the inclusion of time series data on web search behaviour can increase the performance when forecasting tourist arrivals compared to the purely autoregressive approach. The spatial distribution pattern of tourism demand shifted from medium to high clustering in 2011 and 2018, and the positive Morans I revealed the existence of high-value to high-value clustering or low-value to low-value clustering of tourism demand in the study area, and the spatial pattern and spatial dependence of tourism demand with evident clustering. Therefore, tourism demand in one province largely influences tourism demand in the adjacent provinces. Table5 lists search queries with their most relevant time lag, the corresponding DCCA value, as well as the topic and category the query deals with for the sending country Finland.