IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p7060-d405983.html
   My bibliography  Save this article

Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul

Author

Listed:
  • Eunbee Gil

    (Interdisciplinary Graduate Program of Urban Design, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
    R&D Project management Department, Korea Agency for Infrastructure Technology Advancement, 286 Simin-daero, Dongan-gu, Anyang-si 14066, Korea)

  • Yongjin Ahn

    (School of Architecture, College of Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

  • Youngsang Kwon

    (Department of Civil and Environmental Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
    Smart City Research Center, Advanced Institute of Convergence Technology, Seoul National University, 145 Gwangkyo-ro, Youngtong-gu, Suwon-si 16229, Korea)

Abstract

Points of interest (POIs)—areas with a concentration of places that attract people—are important urban planning and tourism policy targets. This study aims to determine the points of interest of urban residents by analyzing big data from search engines to reveal the physical characteristics of POIs. To achieve this, POI data were collected in three city centers in Seoul using a South Korean dominant portal site that includes a search engine. The most popular POIs were determined by using GIS search engine analysis frequency, and correlation and regression analyses were conducted to investigate the relation between POIs and urban elements. The results revealed different POI trends in each city center. While POIs were concentrated in old, narrow streets with small attractions and mixed-use construction near Seoul City Wall (historic downtown district), they also formed around notable architectural landmarks in the newly developed Yeouido and Yeongdeungpo areas. This study found that tourism attraction took different forms in old and new areas, demonstrating that citizens are interested in both historic downtown areas and new areas, as traditional urban theorists suggest. Thus, urban planners and tourism policy makers should consider specific spatial contexts with search engines.

Suggested Citation

  • Eunbee Gil & Yongjin Ahn & Youngsang Kwon, 2020. "Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7060-:d:405983
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/7060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/17/7060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guan, Wanqiu & Gao, Haoyu & Yang, Mingmin & Li, Yuan & Ma, Haixin & Qian, Weining & Cao, Zhigang & Yang, Xiaoguang, 2014. "Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 340-351.
    2. Ann Markusen, 2013. "Fuzzy concepts, proxy data: why indicators would not track creative placemaking success," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 17(3), pages 291-303, November.
    3. Koun Sugimoto & Kei Ota & Shohei Suzuki, 2019. "Visitor Mobility and Spatial Structure in a Local Urban Tourism Destination: GPS Tracking and Network analysis," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
    4. M. Mazhar Rathore & Anand Paul & Awais Ahmad & Gwanggil Jeon, 2017. "IoT-Based Big Data: From Smart City towards Next Generation Super City Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 28-47, January.
    5. Shaojun Liu & Ling Zhang & Yi Long, 2019. "Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion," Sustainability, MDPI, vol. 11(15), pages 1-27, July.
    6. Yandong Wang & Teng Wang & Ming-Hsiang Tsou & Hao Li & Wei Jiang & Fengqin Guo, 2016. "Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongyu Wang & Jian Tang & Pengpeng Xu & Rundong Chen & Haona Yao, 2022. "Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing," Land, MDPI, vol. 11(5), pages 1-22, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bohong Zheng & Rui Guo & Komi Bernard Bedra & Yanfen Xiang, 2022. "Quantitative Evaluation of Urban Style at Street Level: A Case Study of Hengyang County, China," Land, MDPI, vol. 11(4), pages 1-28, March.
    2. Johannes Stübinger & Lucas Schneider, 2020. "Understanding Smart City—A Data-Driven Literature Review," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    3. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    4. Mona El Khafif & Kathy Hsu Wibberly & Elgin Cleckley & Tho H. Nguyen & Marcus H. Divers, 2021. "We Are Martinsville (WAM): Leveraging Mobile Gaming for Community Engagement and Improving Health," International Journal of E-Planning Research (IJEPR), IGI Global, vol. 10(4), pages 63-87, October.
    5. Tosporn Arreeras & Mikiharu Arimura & Takumi Asada & Saharat Arreeras, 2019. "Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    6. Yingzi Chen & Yaqi Hu & Lina Lai, 2022. "Demography-Oriented Urban Spatial Matching of Service Facilities: Case Study of Changchun, China," Land, MDPI, vol. 11(10), pages 1-22, September.
    7. Xin Li & Bingruo Duan, 2018. "Organizational microblogging for event marketing: a new approach to creative placemaking," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(1), pages 59-79, January.
    8. Monther M. Jamhawi & Roa’a J. Zidan & Mohammed Fareed Sherzad, 2023. "Tourist Movement Patterns and the Effects of Spatial Configuration in a Cultural Heritage and Urban Destination: The Case of Madaba, Jordan," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    9. Nikhlesh Pathik & Rajeev Kumar Gupta & Yatendra Sahu & Ashutosh Sharma & Mehedi Masud & Mohammed Baz, 2022. "AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    10. Rizwan Muhammad & Yaolong Zhao & Fan Liu, 2019. "Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 11(10), pages 1-30, May.
    11. Jieping Chen & Chao Ma & Shijun Chen, 2024. "Determinant of the tourism economy in Chinese cities: from an urban centrality perspective," Tourism Economics, , vol. 30(1), pages 44-66, February.
    12. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    13. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.
    14. Shaojun Liu & Yao Long & Ling Zhang & Hao Liu, 2021. "Quantifying and Characterizing Urban Leisure Activities by Merging Multiple Sensing Big Data: A Case Study of Nanjing, China," Land, MDPI, vol. 10(11), pages 1-20, November.
    15. Jia Tao & Meng Yang & Jing Wu, 2022. "Coupling Coordination Evaluation of Lakefront Landscape Spatial Quality and Public Sentiment," Land, MDPI, vol. 11(6), pages 1-29, June.
    16. Junyue Yang & Xiaomei Li & Jia Du & Canhui Cheng, 2023. "Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    17. Li, Yuan & Gao, Haoyu & Yang, Mingmin & Guan, Wanqiu & Ma, Haixin & Qian, Weining & Cao, Zhigang & Yang, Xiaoguang, 2015. "What are Chinese talking about in hot weibos?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 546-557.
    18. Tongwen Wang & Ya Li & Haidong Li & Shuaijun Chen & Hongkai Li & Yunxing Zhang, 2022. "Research on the Vitality Evaluation of Parks and Squares in Medium-Sized Chinese Cities from the Perspective of Urban Functional Areas," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    19. Zhan Liu & Jialu Shan & Nicole Glassey Balet & Gang Fang, 2017. "Semantic social media analysis of Chinese tourists in Switzerland," Information Technology & Tourism, Springer, vol. 17(2), pages 183-202, June.
    20. Xiaoxue Cheng & Guifeng Han & Yifan Zhao & Lin Li, 2019. "Evaluating Social Media Response to Urban Flood Disaster: Case Study on an East Asian City (Wuhan, China)," Sustainability, MDPI, vol. 11(19), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7060-:d:405983. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.