Vol. 52 No (Apr. 2011) GPS Social Reverse Geocoding SRG SRG SVM Social Reverse Geocoding: Visualizing Image of the City from Geotag
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1 Vol. 52 No (Apr. 2011) GPS Social Reverse Geocoding SRG SRG SVM Social Reverse Geocoding: Visualizing Image of the City from Geotagged Photos Koh Sueda, 1 Takashi Miyaki 1 and Jun Rekimoto 2 In this paper, we propose a concept that provides highly legible geographical information using users tags from Flickr s geotagged photos for mobile user interfaces. Our proposed method, Social Reverse Geocoding, aims to realize visualizing the image of the environments in our mind through the use of an navigation system. In general, people use a number of factors to recognize a place, including landmarks, station names, and famous events. In this study, we utilize tags from geotagged photos as social-tagging from the real world to visualize the image of our city using the folksonomic method GIS Geographic Information Systems GPS GPS Google 1 1) Graduate School of Interdisciplinary Information Studies, Interfaculty Initiative in Information Studies, The University of Tokyo 2 Sony Computer Science Laboratories, Inc c 2011 Information Processing Society of Japan
2 1466 Flickr 2) cool Social Reverse Geocoding SRG SRG (1) SVM SRG (2) Automatic Geo diary Zoomable Socioscape Navigator Easy Tagging Cam 2. Social Reverse Geocoding 2.1 3) 1 Flickr shibuya 53,522 4,250 harajuku 1 Fig. 1 The difference between people s definition and street addresses. 53,522 2,043 Google 1 JR JR 50
3 1467 実世界集合知による利用者の認知地図の可視化とモバイルインタラクションへの適用 2.2 カーネル密度推定法を用いた 集合知による認知地図の作成 の他に 原宿 神宮前 青山 赤字で表示 など 駅名や実際には離れて存在する地番 この場所は 渋谷原宿界隈 などのように都市生活者はしばしば 複数の空間を重複して が混在して記述されていることが観察できる 定義していることがある このため 行政区分のような階層構造の空間定義では 生活者の 空間定義を記述することが困難な場合もある 表 1 は 図 3 で表示されたエリアにおいて 表参道 omotesando とタギングされ たユーザの写真に付加されたタグ群をサンプルしたものである 表中の記録では 表参道 これらをユーザの主観的な空間認識の結果の記述ととらえるならば それぞれのタグに多 少の不整合があったとしても ソーシャルタギングなどの概念になぞらえることで タグを 集積し情報全体の傾向を俯瞰し 分類をするのに有用4) と考える 本研究では これらの写真に記述されたタグの密度の疎密の境界を 界隈 の境目と見立 て 集積することで ユーザ全体の意識として都市空間のイメージを図 2 のような一部に 表1 渋谷近辺で撮影された 表参道 omotesando とタギングされたジオタグ付き写真のサンプル 地名や駅 名などの複数の地理名称が混在して記述されている Table 1 The sample of photos tagged as Omotesando. 重複を含む非階層的なイメージとして提示することを試みた 図 3 は 渋谷区近辺 図 1 と同様 のエリアに対し 地番および駅名が記述されたタグ を含む写真の緯度経度情報を カーネル密度推定法を用い それぞれの地理的な名称に関す る密度に応じて 各領域を定義したものである カーネル密度推定法は 統計学において 確率変数の確率密度関数を推定する手法の 1 つであ る この手法は各標本の密度をカーネル関数によってコブ状に定義するため ヒストグラムなど 図2 インタビューと実踏調査による 1960 年代ボストンのパブリックイメージ 一部の領域は重複または境界が曖 昧に意識される 都市のイメージ 3) より Fig. 2 The public image of Boston in the 1960 s. People define the districts. Some of them are overlapped (Quoted from The image of the city 3) ). 情報処理学会論文誌 Vol. 52 No (Apr. 2011) 図3 ユーザタグをカーネル密度推定法により地名ごとに集計し その領域を提示した結果 生成された等高線に閾 値 赤色破線 を与え タグの密度によって領域を定義した 図中からユーザタグによって複数の領域が重複 して定義されている Fig. 3 The place names of areas are overlapped each other. c 2011 Information Processing Society of Japan
4 Flickr 2010/6 Table 2 The number of tags from Shibuya area on Flickr (June 2010). 5) R 6) kde2d 7) 1. Flickr 1,000 7, kde2d SVM 4,250 2,043 Flickr 53,522 Fig. 4 Plot of the boundary by SVM using tags in geotagged photos from Flickr. 2.3 SVM 4 1 SVM SVM
5 R SVM 8) ksvm K. 3) 5 3. Social Reverse Geocoding 3.1 Automatic Geo diary SRG SRG 6 1 Holux GPS 1 JR 15 1 SRG 5 SVM Fig. 5 The scenery changes from buildings lining the street to the park opened to the sky, and the street is curved. 6 SRG SRG Fig. 6 Comparing with the activity logs by SRG and conventional reverse geocoding. 1 Holux M-241: content.jsp?pno=341
6 GPS 1 Google Social Reverse Geocoding SVM 2 JR SRG 3.2 Zoomable Socioscape Navigator Zoomable Socioscape Navigator ZSN SRG Apple iphone 7 7 Zoomable Socioscape Navigator Flickr API Fig. 7 The screen shots of Zoomable Socioscape Navigator. 3.3 Easy Tagging Cam 1 8
7 Flickr API Fig. 8 The screen shots of Easy Tagging Cam: Simultaneous photo capture and tagging conducted by tapping the overlaid tag recommendation. Easy Tagging Cam Flickr API 8 / 4. Kennedy Flickr 9) Tagmap 10) WebGIS 11) 12) Rekimoto 13) Watanabe Easy Tagging Cam 14) 1960 K. 3)
8 Social Reverse Geocoding 3 9 SRG Flickr Flickr SRG /1960 K. (1)(4)(6) (2) Fig. 9 Comparison of the significant elements of L.A. in the 1960 s and the 2000 s. Bunker Hill (1) Financial district (2) Civic center (3) Little Tokyo (4) Skid row (5) Broadway (6) Little Tokyo Broadway 50 Financial district Financial district th street Broadway 15) Financial district Civic Center 16) 5.3
9 ) 6. Social Reverse Geocoding SRG SVM SRG 1) Paul, L.: Social Tagging and Music Information Retrieval, Journal of New Music Research, Vol.37, No.2, pp (2008). 2) Flickr. 3) (2007). 4) Davis, M., et al.: From context to content: Leveraging context to infer media metadata, Proc. 12th Annual ACM International Conference on Multimedia (MULTI- MEDIA 04 ), pp (2004). 5) Wikipedia 6) R. 7) Two-Dimensional Kernel Density Estimation. 8) R Documentation: Support Vector Machines. 9) Kennedy, L., et al.: How Flickr helps us make sense of the world: Context and content in community-contributed media collections, Proc. 15th International Conference on Multimedia (MULTIMEDIA 07 ), pp (2007). 10) Tagmap, Yahoo! Research Berkeley. 11) GPS WebGIS 2004 pp (2004). 12) Vol.14, No.3, pp (1997). 13) Rekimoto, J., Miyaki, T. and Ishizawa, T.: LifeTag: WiFi-based Continuous Location Logging for Life Pattern Analysis, Proc. 3rd International Symposium on Location- and Context-Awareness (LOCA2007 ), pp (2007). 14) Watanabe, K., Tsukada, K. and Yasumrua, M.: WillCam: A digital camera visualizing users interest, Proc. CHI 2007 Conference Proceedings and Extended Abstracts, pp (2007). 15) Wikipedia, Financial District. Street Financial District 16) Dong, W. and Fu, W.-T.: Cultural difference in image tagging, Proc. 28th International Conference on Human Factors in Computing Systems (CHI 10 ), pp (2010). 17) (1930). ( ) ( )
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