11:15 〜 11:30
[007] Methods to Anticipate Gentrification: A Chronological Review Toward Potential Value Evaluation in Commercial District
キーワード：Gentrification, Commercial Power Analysis, Quantitative Methodology, Literature Review
Due to the emergence of big data, investigation on gentrification became multi-dimensional, and this implies that it may expand further into the field of prediction (Koo, 2020). The objective of this paper is to identify chronological trends in research methods in examining gentrification. Since gentrification usually has its own lifecycle in accordance with multiple neighborhood features, sales, real estate price, operation period and many other factors (Lee, 2019), multiple parties, like municipality, investors, merchants, engage in analyzing and predicting changes in urban areas, in particular, commercial district to prepare for their planning and management strategies to continue their competition in the market.
This paper adopts a chronological approach to analyze methods in relevant studies to analyze and predict gentrification. As a result, works on gentrification are chronically analyzed in two metrics, method and data. To anticipate tentative gentrification area in Seoul for land investment, this chronical comprehension about relevant studies provides implication on future directions. First, in terms of method, it can be categorized into three groups: logical argumentation, statistical inferencing, and artificial intelligence. These classifications are not mutually exclusive, but the methods are cumulatively adopted time to time by succeeding works. However, during the process of using predictive models that facilitates anticipation with sequential data, figuring a way to discover the implicit relationship between features are crucial. This is vital because the implicit weights are expected to explain the magnitude of importance which directly links to the proposal for the degree of investment. Second, in terms of dataset, the discourse evolves from the classic ‘rent-gap theory’ by Neil Smith (1979) with much more abundance and precision. Recent studies employ crowd-sourced data which exhibits much direct implication on human movement in relatively short time period. Therefore, being able to source distinctive user-generated data is important in plotting future behavior of the gentrification
This paper adopts a chronological approach to analyze methods in relevant studies to analyze and predict gentrification. As a result, works on gentrification are chronically analyzed in two metrics, method and data. To anticipate tentative gentrification area in Seoul for land investment, this chronical comprehension about relevant studies provides implication on future directions. First, in terms of method, it can be categorized into three groups: logical argumentation, statistical inferencing, and artificial intelligence. These classifications are not mutually exclusive, but the methods are cumulatively adopted time to time by succeeding works. However, during the process of using predictive models that facilitates anticipation with sequential data, figuring a way to discover the implicit relationship between features are crucial. This is vital because the implicit weights are expected to explain the magnitude of importance which directly links to the proposal for the degree of investment. Second, in terms of dataset, the discourse evolves from the classic ‘rent-gap theory’ by Neil Smith (1979) with much more abundance and precision. Recent studies employ crowd-sourced data which exhibits much direct implication on human movement in relatively short time period. Therefore, being able to source distinctive user-generated data is important in plotting future behavior of the gentrification