An Analysis of Usage Characteristics of Urban Rgeneration Parks by Data Analysis Techniques

Woo, Kyung-sook; 환경조경학과, 경희대학교 일반대학원; Joohwan, Suh

doi:10.51549/joral.2021.15.2.031

Cited by 3 publications

(2 citation statements)

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“…(Manufacturer: TheIMC, Daegu Metropolitan City, Republic of Korea) was used for data collection, refinement, and matrix generation. Textom is a big data processing solution that automatically collects data from various Internet channels by channel and processes it in batches, including purification and matrix production [15], and is actively used for text mining analysis in Korea [14,16,17]. We used Keyword Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analysis, Convergence of iterated CORrelation (CONCOR) analysis, and Quadratic Assignment Procedure (QAP) correlation analysis with the matrix generated by text mining analysis technique using Textom to identify changes in usage behavior by period.…”

Section: Methodsmentioning

confidence: 99%

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Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea

Youn,

Kim

2024

Forests

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This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods, as follows: pre-COVID-19 (1 January to 31 December 2019), during the COVID-19 pandemic (1 November 2020 to 31 October 2022), and post-COVID-19 (1 November 2022 to 31 October 2023). After the COVID-19 outbreak, healing forest use behavior did not revert to pre-COVID-19 patterns. Moreover, the keywords “tourism” and “hiking” stood out as the main drivers of this change in behavior. Therefore, the Korea Forest Service and related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

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Section: Methodsmentioning

confidence: 99%

“…Behavior is not independent of time; thus, behavior over time must be analyzed as changes over time [14]. In the aftermath of the COVID-19 pandemic, the authorities must develop strategies to attract and activate increased forest visitation in the medium to long term.…”

Section: Introductionmentioning

confidence: 99%

Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea

Youn,

Kim

2024

Forests

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A Comparison of Sense of Place of Visitors to Culture-Led Regeneration Facilities Using Industrial Heritage Separated from the Culture and Arts Center: Using Online Reviews and the Biterm Topic Model

Choe,

Lee

2023

J. Cult. Policy

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Changes in Social Media Big Data on Healing Forests : A Time-series Analysis on the Use Behavior of Healing Forests Before and After the COVID-19 Pandemic in South Korea

Kim,

Youn

2024

Preprint

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This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

show abstract

An Analysis of Usage Characteristics of Urban Rgeneration Parks by Data Analysis Techniques

Cited by 3 publications

References 0 publications

Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea

Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea

A Comparison of Sense of Place of Visitors to Culture-Led Regeneration Facilities Using Industrial Heritage Separated from the Culture and Arts Center: Using Online Reviews and the Biterm Topic Model

Changes in Social Media Big Data on Healing Forests : A Time-series Analysis on the Use Behavior of Healing Forests Before and After the COVID-19 Pandemic in South Korea

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