Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan

Liu, Jun; Cheng, Han; Jiang, Di; Huang, Li

doi:10.1016/j.tourman.2018.08.021

Cited by 54 publications

(27 citation statements)

References 33 publications

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“…An urban heat island (UHI) has been observed and recognized to exist in the most of cities around the world [1]. Compared to their surrounding rural areas, an elevated temperature of urban areas not only directly affects urban ecological environment quality, but also increases human risk of violence and mortality, thus impacting on mental well-being of urban residents and overall livability of cities [2][3][4][5][6][7]. The elderly and children can be more easily influenced by higher outdoor air temperature.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

Sun

Gao

et al. 2019

Remote Sensing

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It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners.

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Section: Introductionmentioning

confidence: 99%

Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

Sun

Gao

et al. 2019

Remote Sensing

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“…Pertinent to climate change and tourism, discussions and warnings also abound (Craig and Feng, 2018;Weir, 2017;Wijaya and Furqan, 2018, just to name a few). Some authors have focused on specific destinations in terms of climate change and its impact on tourism (Wijaya & Furqan, 2018;Chin et al, 2018;Liu et al, 2019;Hewer & Gough, 2018). Nevertheless, 'as a Chicago Tribune article concluded: There are, then, legitimate experts on all sides of the greenhouse theory.…”

Section: Discussionmentioning

confidence: 99%

Tourism climate insurance: implications and prospects

Alipour

Peyravi

Dalir

2019

Asia Pacific Journal of Tourism Research

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This paper investigates the intentions of tourists to purchase climate insurance as part of their holiday packages. A research model, based on Bagozzi's reformulation of attitude theory (BRAT) designed to examine the relationships between climate expectation-disconfirmation and the behavioral intentions of tourists in purchasing tourism climate insurance, with destination loyalty as an outcome. The model hypothesizes and tests whether expectation-disconfirmation can be resolved by insuring the product-climate. A structural equation modeling (SEM) is utilized. The results reveal that the expectation-disconfirmation of tourists in relation to climate is positively associated with their intentions to purchase insurance and negatively affects loyalty.

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“…Initially, they focused on finding influence of changes in major economy indices e.g. Gross Domestic Product or price index [18] as well as environmental factors [19]. Their most important contribution is notion of endogenous and exogenous factors influencing arrivals process.…”

Section: Related Workmentioning

confidence: 99%

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Кamola

Arabas

2020

IEEE Access

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Forecasting occupancy in hospitality business with autoregressive time-series models does not intercept occasional impact of public events. Our goal was to find appropriate datasets and enrich existing predictive models to account for rare and explicable demand surges. The paper proposes processing framework: data source types and formats, and forecast algorithms based on natural language processing. The study shows that classical models using word collocations outperform state of the art deep neural networks. Also, the collocations that turn out to be important, occupy certain locations in a graph that represents the natural language. The findings may result in yet improved forecasts, leading to smarter offer pricing and, finally, increased competitiveness in hospitality business. They may also serve public interest in areas like parking management or public transport planning. INDEX TERMS Time series analysis, recurrent neural networks, regression analysis, natural language processing, predictive models.

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Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan

Cited by 54 publications

References 33 publications

Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

Tourism climate insurance: implications and prospects

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

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