{"created":"2025-01-19T01:17:01.181984+00:00","updated":"2025-01-19T15:49:32.215191+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216379","sets":["581:10784:10786"]},"path":["10786"],"owner":"44499","recid":"216379","title":["人の移動データに基づく地域のクラスタリングと入込観光客数予測への応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-15"},"_buckets":{"deposit":"e3a7309d-84a5-4edd-8d84-8ddf809decf0"},"_deposit":{"id":"216379","pid":{"type":"depid","value":"216379","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"人の移動データに基づく地域のクラスタリングと入込観光客数予測への応用","author_link":["558634","558633","558635","558632"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"人の移動データに基づく地域のクラスタリングと入込観光客数予測への応用"},{"subitem_title":"Location Clustering based on Human Mobility and Its Application for Prediction of Sightseeing Visitors","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:ネットワークサービスと分散処理] ネットワーク分析，コミュニティ検出，教師あり機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2022-02-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社NTTドコモ"},{"subitem_text_value":"株式会社NTTドコモ"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NTT DOCOMO, INC.","subitem_text_language":"en"},{"subitem_text_value":"NTT DOCOMO, INC.","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/216379/files/IPSJ-JNL6302041.pdf","label":"IPSJ-JNL6302041.pdf"},"date":[{"dateType":"Available","dateValue":"2024-02-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6302041.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9a978f58-f94f-4e84-b1eb-8fc3b49941f3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"落合, 桂一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"寺田, 雅之"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Keiichi, Ochiai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masayuki, Terada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"人々の社会生活において移動は基本的な行動であり，人々の移動は経済活動や交通，公衆衛生など様々な分野と関わりが深い．人々の移動データと応用先の分野のデータを組み合わせて解析することで，経済活動の予測や交通の最適化など各分野での分析精度の向上や，より詳細な分析が期待される．そこで本研究では，人の移動データに基づいて市町村をクラスタリングすることの有用性を実データを使って評価する．まず，約11万ユーザの半年間の移動データから市町村をクラスタリングする．次に，クラスタリングの有用性を，(1)市町村別観光消費額の年間推移の類似性，(2)入込観光客数の予測問題という2つの評価実験により検証する．予測問題では，行政が決めた地域区分と比較して予測誤差を削減できることを確認した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In the social life of the people, the movement is a fundamental action, and the movement of the people is deeply related to various fields such as economic activity, traffic, and public health. By combining the movement data of the people and the data of the application field such as the economic activity and optimization of the traffic, it is expected that the analysis accuracy can be improved and more detailed analysis can be conducted. In this study, we evaluate the usefulness of clustering cities based on human movement data using real data. At first, cities are clustered from mobility data of about 110,000 users for half a year. Next, the usefulness of clustering is verified by two evaluation experiments: (1) similarity of annual transition of sightseeing consumption by municipalities, and (2) prediction problem of the number of entering tourists. In the prediction problem, it was confirmed that the prediction error could be reduced in comparison with the region division decided by the administration.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"623","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"615","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"63"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00216271","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":216379,"links":{}}