Matthews Lindsay, Scott Daniel, Andrey Jean
Geography & Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada.
Int J Biometeorol. 2021 May;65(5):749-762. doi: 10.1007/s00484-019-01799-7. Epub 2019 Sep 14.
The complexity of the human-environment interface predicates the need for tools and techniques that can enable the effective translation of weather and climate products into decision-relevant information. Indices are a category of such tools that may be used to simplify multi-faceted climate information for economic and other decision-making. Climate indices for tourism have been popularized in the literature over the past three decades, but despite their prevalence, these indices have a number of limitations, including coarse temporal resolution, subjective rating and weighting schemes, and lack of empirical validation. This paper critically assesses the design of the tourism climate index, the holiday climate index-beach, and a new, mathematically optimized index developed for the unique contextual realities of Great Lakes beach tourism. This new methodology combines the use of expert knowledge, stated visitor preferences, and mathematical optimization to develop an index that assigns daily weather scores based on four weather sub-indices (thermal comfort, wind speed, precipitation, and cloud cover). These daily scores are then averaged to the monthly level and correlated to visitation data at two beach parks in Ontario (Canada). This optimized index demonstrates a strong fit (R = 0.734, 0.657) with observed visitation at Pinery Provincial Park and Sandbanks Provincial Park, outperforming both the tourism climate index (R = 0.474, 0.018) and the holiday climate index-beach (R = 0.668, 0.427). This study advances our understanding of the magnitude and seasonality of weather impact on beach tourist visitation and can inform decision-making of tourism marketers and destination managers.
人类与环境界面的复杂性决定了需要工具和技术，以便能够将天气和气候产品有效地转化为与决策相关的信息。指数是这类工具的一种，可以用来简化多方面的气候信息，以用于经济和其他决策。在过去三十年里，旅游气候指数在文献中得到了推广，但尽管它们很普遍，但这些指数有一些局限性，包括时间分辨率粗糙、主观评级和加权方案，以及缺乏实证验证。本文批判性地评估了旅游气候指数、假日气候指数——海滩，以及为五大湖海滩旅游的独特背景现实开发的一种新的、经过数学优化的指数的设计。这种新方法结合了专家知识的运用、游客陈述的偏好以及数学优化，以开发一种基于四个天气子指数（热舒适度、风速、降水量和云量）来分配每日天气分数的指数。然后将这些每日分数平均到月度水平，并与加拿大安大略省两个海滩公园的游客数据进行关联。这个优化后的指数与派尼尔省立公园和桑德班克斯省立公园的实际游客量显示出很强的拟合度（R = 0.734, 0.657），优于旅游气候指数（R = 0.474, 0.018）和假日气候指数——海滩（R = 0.668, 0.427）。这项研究增进了我们对天气对海滩游客量影响的程度和季节性的理解，并可为旅游营销人员和目的地管理者的决策提供参考。
