臺灣博碩士論文加值系統

近年隨著網路技術快速發展與行動裝置的普及，發展出許多網路社群平台，這些平台產生的社群媒體資料，常附有地理標籤 (Geotag)、打卡座標 (Check-in) 等具有空間意涵的資訊，此為一般公民使用社群媒體的同時，運用了空間定位的工具，紀錄與分享其觀察的地理相關資訊，稱之為「自發式地理資訊」 (Volunteered Geographic Information, VGI)，而VGI的出現對於地理學形成衝擊，甚至有學者認為其會對地理資訊科學形成典範轉移 (Paradigm Shift)。
人類空間活動行為是地理學所關注的研究課題，但人們的活動時間、地點等空間移動行為資料在過往取得不易，因此在地理學的研究中較少對於群眾的整體移動行為進行討論，然而藉由自發式地理資訊此種新型態的資料來源，或許能夠突破過往的研究限制，在移動行為研究上產生新的研究方式，探究人群的移動行為。
本研究目的為透過實例，應用大量自發式地理資訊進行移動行為研究，對移動行為研究提出新的研究方式，同時討論自發式地理資訊的研究脈絡與相關概念。
本研究自Flickr 社群平台，獲取15,342位使用者2001年到2015年產生的1,352,500張具有地理標籤 (Geotag) 之相片資料，其中透過這些資料於研究區，台北市，取得86處景點與176,810條單日旅遊移動路徑，後續則透過這兩項資訊進行移動行為探究，探究內容則包含停留地點、移動路線、景點間關聯性、旅客行為模式，而為了解不同旅遊者是否具有不同的移動行為，因此透過旅遊者之居住地將旅遊者分為，外國旅客、台北市當地旅客、台灣它縣市旅客以分別探究其移動型態，而研究成果顯示此三種類型之旅客具有不同的移動行為，本研究此種透過大量自發式地理資訊進行人群移動行為探究的研究取徑，確實能夠獲取過往難以掌握的人群移動資訊，對移動行為研究而言是一種新的研究方式。

In recent years, with the fast development of Internet and the rapid growth of mobile devices, a number of social media have been developed. The data come from social media which often include “geotag”, “check-in” and other spatial information. Individual citizens use positioning tools to record and share their observed geographic information while using social media. This type of information is called Volunteered Geographic Information (VGI). The emergence of VGI has a great influence on Geography, that even some scholars consider it as a paradigm shift for Geographic Information Science.
Geographer are interested in the human spatial behavior but it was difficult to collect the data of human spatial behavior, such as individual’s travel time and activity locations. As a result, there were few studies focusing on crowd movement behavior in the past. However, VGI as a new type of geographic information source may be able to overcome the limitation of past studies and find a new way for movement behavior research.
The purpose of this research aims to introduce a new approach for movement behavior research. Through case study, the movement behavior is analyzed by VGI to explore the movement behavior research. Also, the context and related concept of VGI are discussed.
The study gathered 1,352,500 geotagged photos of 15,342 users generated between 2001 and 2015 from Flickr website and obtained 86 tourist sites and 176,810 traveler’s daily paths in the study area of Taipei city. Then, the data about tourist sites and traveler’s daily paths were used to analyze the movement behavior. The analysis of movement behavior focused on stopping places, travel paths, the connectivity between tourist sites, and the movement pattern of traveler. Moreover, in order to understand the behavior of different types of traveler, the travelers were categorized into foreign traveler, Taipei local traveler, and other Taiwan county’s traveler by their residence. The result indicated that foreign traveler, Taipei local traveler, and other Taiwan county’s traveler have different movement behavior in Taipei city. Exploring massive amount of VGI to understand the crowd movement behavior is indeed a new approach for movement behavior research.

口試委員審定書 I
誌謝 II
摘要 III
ABSTRACT IV
目錄 VI
圖目錄 VIII
表目錄 X
第一章、緒論 1
第一節、研究動機 1
第二節、研究目的 4
第三節、研究架構 5
第二章、文獻回顧 6
第一節、自發式地理資訊 6
第二節、社群網路平台 8
第三節、旅遊移動行為 12
第三章、研究方法 16
第一節、研究流程 16
第二節、資料蒐集與前處理 18
第二節、旅遊活動探究 21
第三節、旅遊者移動行為分析 25
第四章、研究區域 28
第五章、研究成果與討論 29
第一節、VGI 29
第二節、景點 35
第三節、移動路徑 44
第四節、移動行為分析 46
第五節、不同旅遊者之行為差異分析 56
第六章、結論與後續研究 69
第一節、結論 69
第二節、後續研究 70
參考文獻 71
附錄 76

Aggarwal, C. C., & Yu, P. S. (1998, May). A new framework for itemset generation. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (pp. 18-24). ACM.
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216.
Agrawal, R. and R. Srikant (1994) Fast algorithms for mining association rules in large database. Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile
Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science, 29: 379-389.
Carey, N. (2005). Establishing pedestrian walking speeds. Portland State University.
Chareyron, G., Da-Rugna, J., & Branchet, B. (2013, August). Mining tourist routes using flickr traces. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on (pp. 1488-1489). IEEE.
Crandall, D. J., Backstrom, L., Huttenlocher, D., and Kleinberg, J. (2009). Mapping theworld''s photos. In Proceedings of the 18th international conference on World wide web. ACM,New York, NY, USA, 761{770.
M.H. Dunham, “Data Mining - Introductory and Advanced Topics,” Prentice Hall, 2003.
Edwards, D. (2009). Using GPS to Track Tourists Spatial Behaviour in Urban Destinations.
Elwood, S. (2008) Volunteered geographic information: Key questions, concepts and methods to guideemerging research and practice, GeoJournal, 72:133-135.
Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the association of American geographers, 102(3), 571-590.
Elwood, S., Goodchild, M. F., & Sui, D. (2013). Prospects for VGI research and the emerging fourth paradigm. In Crowdsourcing Geographic Knowledge (pp. 361-375). Springer Netherlands.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).
Facebook Inc. (2015). Facebook Developer. Available at:
https://developers.facebook.com/.
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D. (2007). Trajectory pattern mining. In: Proceedings of Conference on Knowledge Discovery and Data mining, pp. 330–339.ACM, New York
Google Inc. (2015). Google Developer. Available at: https://developers.google.com/+/
Goodchild, M. (2007) Citizen as sensors: The world of volunteered geography, GeoJournal, 69:211-221.
Gray, J. (2007). eScience – A transformed scientific method. Presentation made to the NRC-CSTB. http:// research.microsoft.com/en-us/um/people/gray/talks/NRC-CSTB_eScience.ppt. Accessed 19 June 2016.
Handy, S. (1996). Methodologies for exploring the link between urban form and travel behavior. Transportation Research Part D: Transport and Environment,1(2), 151-165.
Hagerstraand, T. (1970). What about people in regional science?. Papers in regional science, 24(1), 7-24.
Instagram Inc. (2015). Instagram Developer. Available at: https://instagram.com/developer/.
Java, A., Song, X., Finin, T., & Tseng, B. (2007, August). Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56-65). ACM.
Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T. (2007). How flickr helps us make sense of the world: context and content in community-contributed mediacollections. In: Proceedings of conference on Multimedia, pp. 631–640. ACM, NewYork
Kisilevich, S., Mansmann, F., & Keim, D. (2010, June). P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In Proceedings of the 1st international conference and exhibition on computing for geospatial research & application (p. 38). ACM.
Kisilevich, S., Keim, D., Andrienko, N., & Andrienko, G. (2013). Towards acquisition of semantics of places and events by multi-perspective analysis of geotagged photo collections (pp. 211-233). Springer Berlin Heidelberg.
Kreitz, M. (2001). Methods for collecting spatial data in household travel surveys. In 5th International Conference on Transport Survey Quality and Innovation. Kruger Park, South Africa: August (pp. 5-10)
Kwan, M. P., & Lee, J. (2004). Geovisualization of human activity patterns using 3D GIS: a time-geographic approach. Spatially integrated social science, 27.
Lafreniere, T. (2011). Bring big data to the enterprise. IBM Corp.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70.
Lee, R., & Sumiya, K. (2010, November). Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. InProceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks (pp. 1-10). ACM.
Lew, A., & McKercher, B. (2006). Modeling tourist movements: A local destination analysis. Annals of tourism research, 33(2), 403-423.
Lim, B. H., Lu, D., Chen, T., & Kan, M. Y. (2015). # mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks. arXiv preprint arXiv:1507.03510.
Panoramio Inc. (2015). Panoranio API. Available at: http://www.programmableweb.com/api/panoramio
Turner, A. (2006). Introduction to neogeography. " O''Reilly Media, Inc.".
Twitter Inc. (2015). Twitter Developer. Available at: https://dev.twitter.com/rest/public.
Woods, R. (1982). Theoretical population geography.
Yahoo! Inc. (2015). Yahoo Developer. Available at: https://developer.yahoo.com/.
Yahoo! Inc. (2015). The Flickr Developer Guide. Available at: http://www.flickr.com/services/developer/.
Zheng, Y. and Xie, X. (2011). Learning travel recommendations from user-generated gps traces.ACM Trans. Intell. Syst. Technol. 2, 2:1{2:29.
Zheng, Y. T., Zha, Z. J., & Chua, T. S. (2012). Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3), 56.
交通部觀光局 (2014)。中華民國103年來臺旅客消費及動向調查。交通部觀光局。
交通部觀光局 (2016)。旅遊台灣>旅遊景點>觀光景點台北市。交通部觀光局。民105年6月5日，取自: http:// taiwan.net.tw/m1.aspx?sNo=0001090。
鄧東波 (2011)。運用社群媒體於災害救援資訊製圖。地圖 21:2 2011.10[民100.10] 頁1-21。
蔡博文 (2011)。NGIS 的下一步：公眾參與。國土資訊系統通訊 80 2011.12[民100.12] 頁2-9。