ABSTRACT
Purpose: This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution;Second, this paper elaborates on those estimates' usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons. Design/methodology/approach: This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models. Findings: The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naive I model). Practical implications: A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors' experience and tourism stakeholders' decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures. Originality/value: High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week. Plain Language Summary: This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors' experience and public and private decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
PurposeThis paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution;Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.Design/methodology/approachThis study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.FindingsThe results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).Practical implicationsA discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.Originality/valueHigh-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.Plain Language SummaryThis research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.
ABSTRACT
Information and communications technologies (ICT)—and more precisely, their use from fulltime connected mobile gadgets—offer valuable opportunities to interact with tourists using their own devices. In order to exploit these benefits, destinations should have appropriate digital infrastructure to allow for bidirectional smart communication with their visitors. However, the spatial distribution of such coverage, and the geographical concurrence of tourism activities and ICT infrastructure, have been poorly examined. This paper contributes to this analysis by quantifying digital accessibility with both a broader regional approach and a narrower local perspective. First, we propose a digital immersion index, and apply it to the Balearic Islands, Spain. Second, alternative Moran’s indices are used to study the spatial distribution and correlation of tourism and technological infrastructure for a local destination. The results are presented through easily interpretable maps, which can inform tourism policies, such as identifying and prioritizing ITC investments.