Impact and Recovery of Coastal Tourism Amid COVID-19: Tourism Flow Networks in Indonesia

Wang, Xingshan; Tang, Lu; Chen, Wei; Zhang, Jianxin

doi:10.3390/su142013480

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…The use of Markov chains has advanced on many levels, but, in all cases, such uses are premised on ergodicity, and these are limited models that lack general-purpose applicability. Wang et al conducted sightseeing evaluations before and after the pandemic using Markov chains [24].…”

Section: Related Workmentioning

confidence: 99%

Algorithm for Constructing Ergodic Location Networks using Discrete Markov Chains

Mizuno

Ohba

Sadakuni

et al. 2023

Preprint

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For a region's tourism strategy, connecting famous locations to the surrounding locations can activate the entire area. Connections between locations can be obtained by calculating the central locations of the area and creating routes to other locations from there. This study proposes an algorithm to obtain ergodicity in discrete Markov chains using network centrality relations. The number of calculations to obtain ergodicity was compared using several definitions of network centrality, making it possible to select a type of network centrality for the target network. The proposed method clarifies the network’s central locations and how transition probabilities between locations can be adjusted to achieve an ergodic network. If the location network holds ergodicity, a stationary distribution of the Markov chain exists and optimization applications are expected. Numerical calculations were used to demonstrate the method’s applicability to various networks. Furthermore, using a Wi-Fi log, we observed that the proposed method can reconstruct a network with ergodicity for networks that use access points as locations, indicating the social applicability of the proposed method. The proposed method can be used to analyze various networks in modern society, such as facility operations and tourism stimulation. This method applies to human traffic management and stay analysis in facility operations. To promote tourism, by considering tourist sites as network locations, the method clarifies which routes should be prioritized to construct an uninterrupted tourist network, making the construction and operation of an extensive tourist network possible.

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Section: Related Workmentioning

confidence: 99%

Algorithm for Constructing Ergodic Location Networks using Discrete Markov Chains

Mizuno

Ohba

Sadakuni

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In 2020 and 2021, world tourism revenues fell by more than USD 4 trillion, and the unemployment rate increased by 5.5-15% [15]. In Indonesia, foreign tourists decreased by 76.8%; there were only 3.77 million tourists in 2020, while in the previous year, 2019, there were 16.1 million visits [17].…”

Section: Introductionmentioning

confidence: 99%

The Linkage of Digital Transformation and Tourism Development Policies in Indonesia from 1879–2022: Trends and Implications for the Future

Nanda,

Widianingsih,

Miftah

2023

Sustainability

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This research analyzes digital transformation and tourism development in Indonesia based on policies issued by the central government. The policy was issued in the period 1879–2022, or, precisely, during the Dutch colonial period in Indonesia until the COVID-19 pandemic. This study aims to analyze digital transformation policies and tourism policies in Indonesia that are historically linked, as well as their implications for the future. We analyze the trends and times of the COVID-19 pandemic and their implications for the future. The method used in this research is content analysis by analyzing policy texts quantitatively (number of policies in a certain time) and qualitatively (topics and content). The dataset obtained for analysis in this research contains 87 policies on digital transformation and tourism development with various forms of policies. This study found a linkage between digital transformation policies and tourism development, especially tourism development policies toward digital transformation. In addition, during the COVID-19 period, both policies reached the highest number compared to previous years. This allows for more supportive policies to be born in the coming years and implies opportunities for establishing policies on the use of technology in tourism management.

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“…However, most studies used tourism numbers and tourism revenue to determine the impact of the pandemic on the regional tourist market, which were generalized. In addition, most of them provided suggestions for regional tourism recovery after the pandemic through qualitative analysis, which was subjective [19]. Furthermore, these studies mainly focused on well-known or large cities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tourist Market Structure and Its Driving Factors in Small Cities before and after COVID-19

Liu

et al. 2023

IJGI

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Based on the digital footprint data, exploring the differences in tourist market structure and driving factors before and after COVID-19 is important for identifying tourist market demand and optimizing tourism product supply in the post-pandemic era. Most of the existing studies have explored the impact of the pandemic on the tourist market in well-known or large cities and have provided suggestions for tourism recovery. However, these suggestions are not entirely applicable to smaller cities. Small cities have a single level of tourism product, high homogeneity of tourism resources, small tourist market scale, and high volatility of the tourism industry. Therefore, it is necessary to study the differences in the tourist market structure of small cities and its driving factors before and after the pandemic and to propose targeted measures for the tourism recovery in the post-pandemic period. This paper, taking small cities as the study area and using online travel diaries as the data source, analyzed the differences in the spatial and temporal structures of tourist markets and their driving factors in Dengfeng and Kaifeng, China, before and after the pandemic. Then, countermeasures for tourism industry recovery in the post-pandemic era were proposed. The results were as follows: the difference in the tourism off-peak season increased after the pandemic, and the concentration of tourist market spatial distribution in Dengfeng showed a decreasing trend while that in Kaifeng showed an increasing trend. In addition to region traffic, the driving effects of leisure time, climate comfort and residents’ income level weakened after the outbreak. Dengfeng and Kaifeng can enhance the tourist market tendency and attractiveness by creating special indoor tourism projects, strengthening tourism product promotion and marketing and enhancing the facilities related to self-driving tours.

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Impact and Recovery of Coastal Tourism Amid COVID-19: Tourism Flow Networks in Indonesia

Cited by 3 publications

References 48 publications

Algorithm for Constructing Ergodic Location Networks using Discrete Markov Chains

Algorithm for Constructing Ergodic Location Networks using Discrete Markov Chains

The Linkage of Digital Transformation and Tourism Development Policies in Indonesia from 1879–2022: Trends and Implications for the Future

Analysis of Tourist Market Structure and Its Driving Factors in Small Cities before and after COVID-19

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