A Study on the Landscape Guidelines by Big-data Analysis

Ahn, Yea-bom; Ha, Da-yeong; Joo, Shinha

doi:10.36466/klc.12.2.7

Cited by 2 publications

(2 citation statements)

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“…Textom is a program developed by a Korean-based company and is useful and meticulous for analyzing Korean-language articles and big data. Therefore, Textom has been used for big data-based trend analysis when Korean is used as the main keyword in various fields [66][67][68][69]. Using text frequency lists, Textom identifies keywords in the text, selects the necessary words, and generates a matrix.…”

Section: Data Selectionmentioning

confidence: 99%

“…During this period, a total of 30 articles related to Itaewon gentrification were searched. The most mentioned keywords were Cities (106), followed by Space (81), People (68), Cafe (64), Market (53), Kyungridan-gil (45), Restaurant (43 times), Life (39 times), House (38 times), Culture (37 times), Project/Society (34 times), Usadan village (30 times), and Merchant/World/Gentrification (27 times). The others were listed in the order of Progress, Rearrangement, Exploitation, Street, Itaewon-ro, Redevelopment, and Merchants Association (Table 2).…”

Section: Phases Of Gentrification and Degentrificationmentioning

confidence: 99%

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Degentrification? Different Aspects of Gentrification before and after the COVID-19 Pandemic

Han

Bohannon

Kwon

2021

Land

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The purpose of this study is to explore the aspects of “gentrification” and “degentrification” other than economic factors. To this end, this study focused on the gentrification situations occurring before and after the COVID-19 pandemic in the Itaewon area, South Korea, by using semantic network analysis. We analyzed news articles on the gentrification phenomenon in the Itaewon area reported in South Korea. As a result, gentrification in the Itaewon area is divided into four stages. The first stage of gentrification (2010~2014) is initial stage of gentrification. Gentrification stage 2 (2015~2017) is the period of commercialization as a gentrification growth stage. The first stage of degentrification (2018~2019) is the maturation period of gentrification. The second stage of degentrification (2019~30 June 2020) is the period of the COVID-19 pandemic. The results confirm the existing theoretical frameworks while building a more nuanced definition through operationalizing gentrification and degentrification. As with the etymology of the term, the degentrification phenomenon can only be revealed when the gentrification phenomenon is prominently displayed. This study has an implication in that it tried to phenomenologically examine the specific phenomenon of the next stage of gentrification through the term “degentrification”.

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Section: Data Selectionmentioning

confidence: 99%

Section: Phases Of Gentrification and Degentrificationmentioning

confidence: 99%

Degentrification? Different Aspects of Gentrification before and after the COVID-19 Pandemic

Han

Bohannon

Kwon

2021

Land

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Analysis of the Landscape Characteristics of Island Tourist Site Using Big Data - Based on Bakji and Banwol-do, Shinan-gun -

도¹,

서²

2021

J Korean Inst Landsc Archit

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This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji․Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji․Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

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A Study on the Landscape Guidelines by Big-data Analysis

Cited by 2 publications

References 0 publications

Degentrification? Different Aspects of Gentrification before and after the COVID-19 Pandemic

Degentrification? Different Aspects of Gentrification before and after the COVID-19 Pandemic

Analysis of the Landscape Characteristics of Island Tourist Site Using Big Data - Based on Bakji and Banwol-do, Shinan-gun -

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