SNS Flickr Flickr Flickr SNS 2. SNS Twitter [2] Flickr [3] [4] Twitter Twitter Flickr Flickr Flickr Flickr Flickr Twitter 1 document 3. Flickr API Fli
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- しょうじ こいまる
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1 SNS SNS Detection of posted convergences with a photo post type SNS as a target and application to the tourism potential map Shusaku Yano 1 Masahiro Migita 2 Masashi Toda 2 Takayuki Nagai 2 Yasuo Musashi 2 Hiroshi Nakano 2 Kenichi Sugita 2 Abstract: Recent,number of foreigner tourists have increasing. Finding a tourism resources where the excellent landscape is important for the tourism. In this paper, we propose a method finding by using a post photo data on SNS and visualizing a heat-mapo on a map. Usually, tourists gather to famous tourist spots. However, there are tourism resources of unrecognized famous place. Thus, it is the best to visualize a heatmap for tourists to display the places where they don t know the name or wide location on the map.our method is effective to increase foreigner tourists. Keywords: Social networks, location-based services, data mining Kumamoto University,2-39-1,chuo-ku,Kumamoto, Japan Kumamoto University,2-39-1,chuo-ku,Kumamoto, Japan [1] c 2016 Information Processing Society of Japan 1
2 SNS Flickr Flickr Flickr SNS 2. SNS Twitter [2] Flickr [3] [4] Twitter Twitter Flickr Flickr Flickr Flickr Flickr Twitter 1 document 3. Flickr API Flickr API SNS Flickr Web API Flickr Flickr Flickr Flickr API Flickr API 4. [8][9] Flickr Kleinberg 4.1 Kleinberg Kleinberg document document 1 document document t document d t x t document d (t+1) document t d t x t d t+1 document document c 2016 Information Processing Society of Japan 2
3 2 3 Aggregation Pyramid document x t document document document 2 document Kleinberg 4 Aggregation Pyramid [8][9] Zhang Shasha Aggregation Pyramid Aggregation Pyramid 3 Aggregation Pyramid 4 N t h c(h, t) 0 N h N h c(h, t) 0 c(0, t h) 5 Aggregation Pyramid c(0, t) gaps arrt gapn 3 1 N N c(h 1, t 1) c(0, t h) c(0, t) c(h, t) c(h 1, t 1) c(0, t) n + 1 x = (x 1, x 2, x 3,, x n ) time = (time 1, time 2, time 3,, time n+1 ) W min W min ( 1 ) 0 c 2016 Information Processing Society of Japan 3
4 ( a ) x i W min c(0, t).gaps = x i c(0, t).arrt = time i+1 c(0, t).gapn = 1 ( b ) x i W min c(0, t).gaps = W min c(0, t).arrt = c(0, t 1).arrt + W min c(0, t).gapn = c(0, t 1).arrt c(0, t).arrt ( 2 ) 1 c(h, t).gaps = c(h 1, t 1).gaps + c(0, t).gaps c(0, t).arrt = c(0, t).arrt c(0, t).gapn = c(h 1, t 1).gapn + c(0, t).gapn W min W min W min W min c(h, t) = (gaps, arrt, gapn) W min = 1 time = {0, 1, 6, 6, 6, 6, 6, 9, 9, 10} x = {1, 5, 0, 0, 0, 0, 3, 0, 1} 6 c(0, 0) = (1, 1, 1) c(0, 1) = (5, 6, 1) c(0, 2) = (1, 7, 4) c(0, 3) = (2, 9, 1) c(0, 4) = (1, 10, 2) 4.3 (1) avg (c(n 1, t 1 h)) N = c(h, t).gaps c(h, t).gapn (1) 6 0 β(0 < β < 1) (2) brt (c(h, t)) brt (c(h, t)) = avg (c(n 1, t 1 h)) β (2) A min c(h, t) (3) c(h, t).gapn < A min (3) blv (c(h, t)) (4) blv (c(h, t)) = { log(brt (c(h, t))) (brt (c(h, t)) β) 0 (brt (c(h, t)) > β) (4) t blv m (c(h, t)) blv m (c(h, t)) c(h, t) c(n 1, t 1 h) C min tgcell (5) c 2016 Information Processing Society of Japan 4
5 ( ) Cmin β (5) tgcell.gapn 7 (6) ( ( )) blv (c(h, t)) = log Cmin tgcell.gapn N > C min (6) W max c(h, t).gaps > W max (7) ( ) c(h, t).gaps β (7) W max 5.2 (10) (13) Gaussian Silverman Epanechnikov Rectangular K(z) = 1 e z2 2 (10) 2π K(z) = 1 ( u u e 2 sin + π ) (11) (1 1 5 z2 ) 5 (if z < 5) K(z) = (12) 0 (otherwise) 1 2 (if z < 1) K(z) = (13) 0 (otherwise) (8) ( blv (c(h, t)) = log 5. ( )) c(h, t).gaps W max (8) [10] 5.1 (9) ˆf(x) = 1 nh n n=1 K( x x i ) (9) h x i n (i = 1, 2, n) n h K Silverman ArcGIS 6. Flickr API [8][9] SNS Flickr Flickr API 1 SNS Flickr Flickr API Flickr API km ArcGIS c 2016 Information Processing Society of Japan 5
6 情報処理学会研究報告 にプロットし 1日ごとの撮影件数をグラフにまとめ る 実験 2 Flickr API から取得した写真データのうち 撮影時間 を用いバースト検知を行う バースト検知の後 バー ストの度合いを定義しバーストの強弱を求め バース トの度合いをグラフで表示する 図 10 実験 2 バースト度合い 実験 3 バースト検知で求めたバーストの度合いと Flickr API から取得した写真データのうち緯度経度情報を用いて ArcGIS にインポートしカーネル関数を使用するポテ ンシャルマップを作成する ポテンシャルマップにお いて色の濃淡によってカーネル関数を用いて求めた値 の高低を表現する 図 11 実験 3 ポテンシャルマップ 6.2 実験結果 実験 1 Flickr から 2015 年に熊本市役所から半径 5km 以内で撮 影された写真として 1785 件のデータを取得した 取得し た写真の件数をグラフにしたものを図 8 に 緯度経度情報 を ArcGIS を用いて地図上に示した地図を図 9 示す 図 8 の横軸は 2015 年 1 月 1 日を 1 として日にちを示している 実験 2 ストの度合いである 実験 3 実験 2 の結果を図 10 の結果を緯度経度座標に反映させ たポテンシャルマップを図 11 に示す 7. 考察 7.1 実験 1 パラメータ (N = 4, Wmin = 2, β = 0.5) を設定してバー Flickr から写真データの抽出を Flickr API を通して行っ スト検知を行った 結果を図 10 を示す 横軸は 1 月 1 日 た 熊本市役所を中心として半径 5km 範囲内で本研究で を 1 として 2015 年の何日目かを示している 縦軸はバー は写真データの抽出を行った 投稿しているユーザーの中 には食べものや景観を撮影したものが見受けられたが そ の反面 関係のない写真 例えば 選挙についての写真も 見受けられた そのため 写真データの抽出後に 画像認 識もしくは構文解析によりあらかじめ食べものや景観に関 係のない写真データ 関係のある写真データを振り分ける 必要があると考えられる 7.2 実験 2 図 年の熊本市役所半径 5km 以内の撮影件数 N = 4, Wmin = 2, β = 0.5 と設定しバースト検知を行っ た バースト検知の際に設定するパラメータの値を変更す ることで入力データに応じたバースト検知を調整すること ができる そのため 様々なパラメータの中から最適な組 み合わせを選ぶ必要性が出てくる バースト検知を行う際 に 最適な組み合わせ 数値を決定する仕組みも必要だと 考えられる 7.3 実験 3 バーストの度合いを緯度経度情報に付加し ポテンシャ ルマップを作成した 本研究では 1 年分を入力データと 図 年の熊本市役所半径 5km 以内の撮影地点 c 2016 Information Processing Society of Japan して ArcGIS にインポートしたため何月何日にバーストが 6
7 7.4 Flickr Twitter Twitter SNS 1 Flickr Flickr [1] / ( ) [2] Twitter DEIM Forum 2012 C7-3 (2012) [3] (8) pp (2015) [4] DBS-158 No.28 (2013) [5] esri ArcGIS ( ) [6] - pp.146 (2005) [7] Java pp4 (2007) [8] Vol.9 No.2 (2010) [9] Vol.5 No.3 pp86-96 (2012) [10] ( ) 8. Flickr Flickr API SNS c 2016 Information Processing Society of Japan 7
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