Measuring the Impact of Physical Geographical Factors on the Use of Coastal Zones Based on Bayesian Networks

Eglė Baltranaitė,Ramūnas Povilanskas,Loreta Kelpšaitė-Rimkienė,Ilona Šakurova,Vitalijus Kondrat

doi:10.3390/su13137173

Eglė Baltranaitė, Ramūnas Povilanskas + Show 3 more

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https://doi.org/10.3390/su13137173

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Journal: Sustainability	Publication Date: Jun 25, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: Klaipėda University

Abstract

Coastal regions of the Baltic Sea are among the most intensively used worldwide, resulting in a need for a holistic management approach. Therefore, there is a need for strategies that even out the seasonality, which would ensure a better utilization of natural resources and infrastructure and improve the social and economic conditions. To assess the effectiveness of coastal zone planning processes concerning sustainable tourism and to identify and substantiate significant physical geographical factors impacting the sustainability of South Baltic seaside resorts, several data sets from previous studies were compiled. Seeking to improve the coastal zone’s ecological sustainability, economic efficiency, and social equality, a qualitative study (content analysis of planning documents) and a quantitative survey of tourists’ needs expressed on a social media platform and in the form of a survey, as well as long-term hydrometeorological data, were used. Furthermore, a Bayesian Network framework was used to combine knowledge from these different sources. We present an approach to identifying the social, economic, and environmental factors influencing the sustainability of coastal resorts. The results of this study may be used to advise local governments on a broad spectrum of Integrated Coastal Management matters: planning the development of the beaches and addressing the seasonality of use, directing investments to improve the quality of the beaches and protect them from storm erosion, and maintaining the sand quality and beach infrastructure. The lessons learned can be applied to further coastal zone management research by utilizing stakeholders and expert opinion in quantified current beliefs.

Highlights

A growing population and intensified human activities are causing increasing pressures on the environment; this is very true for urban areas, especially those located at coasts and relying on the ecosystem services of coastal systems around the world [1,2,3,4].Coastal areas with well-developed tourism infrastructure and strategies in place are accustomed to tourist flows and associated tendencies
The cities that receive the most significant tourist flows are large multifunctional systems where tourists can be absorbed and become physically and economically invisible [5]. This is especially true concerning the local residents of urban areas; they are rarely considered as users of tourism services
This study presents Benford’s law to be a valuable instrument for evaluating the accuracy of qualitative survey data and shows that the Bayesian Network framework can be used as a tool to support coastal management in focusing on sustainability and incorporating environmental/ecological, social, and economic aspects

Summary

Introduction

A growing population and intensified human activities are causing increasing pressures on the environment; this is very true for urban areas, especially those located at coasts and relying on the ecosystem services of coastal systems around the world [1,2,3,4].Coastal areas with well-developed tourism infrastructure and strategies in place are accustomed to tourist flows and associated tendencies. The cities that receive the most significant tourist flows are large multifunctional systems where tourists can be absorbed and become physically and economically invisible [5] This is especially true concerning the local residents of urban areas; they are rarely considered as users of tourism services. The Bayesian probabilistic paradigm is one of personal perspective with tools to update our beliefs about random events by considering new data about those. To incorporate background knowledge update beliefs and form the posterior into analysis, including the lessons of previous studies. The basic steps of the Bayesian analysis are: (1) quantifying current beliefs via a prior distribution, (2) quantifying information provided by new data via the likelihood, (3) using Bayes’ Theorem to update beliefs and form the posterior distribution [28,29]. This increase is related to the availability of Bayesian computation methods in various popular software packages [32]

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