| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2020-08-20 |
| 書誌情報 |
en : Sustainability
巻 11,
号 14,
発行日 2019
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| タイトル |
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|
タイトル |
Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
destination |
| キーワード |
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|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Wi-Fi tracking data |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
massive area |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
tourist movement |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
survey technology |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
movement patterns |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
association rule mining |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
sustainability development |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
TOSPORN, Arreeras
有村, 幹治
浅田, 拓海
SAHARAT, Arreeras
|
| 室蘭工業大学研究者データベースへのリンク |
|
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有村 幹治（ARIMURA Mikiharu） |
|
|
http://rdsoran.muroran-it.ac.jp/html/100000083_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
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|
浅田 拓海（ASADA Takumi） |
|
|
http://rdsoran.muroran-it.ac.jp/html/100000065_ja.html |
| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used aWi-Fi scanner device to track tourists’ traveling behavior inHokkaido’s tourismarea,whichoccupies a large regionthat features auniquenatural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important formaintaining the tourismatmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARMmethod integrated with aWi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies. |
|
言語 |
en |
| 出版者 |
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|
出版者 |
MDPI |
|
言語 |
en |
| 出版者版へのリンク |
|
|
|
10.3390/su11143967 |
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|
https://doi.org/10.3390/su11143967 |
| DOI |
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関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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関連識別子 |
10.3390/su11143967 |
| 日本十進分類法 |
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主題Scheme |
NDC |
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主題 |
007 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
©2019 by the authors; licensee MDPI AG, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0/) |
| 著者版フラグ |
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|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
STNBLY_11_14_1_17 (1.9 MB)
