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Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining
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Ikjun Kim (Department of e-business, Ajou University)
Junho Lee (Department of e-business, Ajou University)
Hyomin Kim (Department of e-business, Ajou University)
Juyoung Kang (Department of e-business, Ajou University)
Vol. 26, No. 3, Page: 149 ~ 169
10.13088/jiis.2020.26.3.149
Keywords
Urban Regeneration, Text Mining, Topic Modeling, Classification, Machine Learning
Abstract
"The Urban Renewal New Deal project”, one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments.
However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation”, when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area.
In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017).
In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared.
Therefore, it was possible to confirm political topics and cultural topics resulting from various events.
Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed.
Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."
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텍스트 마이닝을 활용한 지역 특성 기반 도시재생 유형 추천 시스템 제안
김익준 (아주대학교 e-비즈니스학과)
이준호 (아주대학교 e-비즈니스학과)
김효민 (아주대학교 e-비즈니스학과)
강주영 (아주대학교 e-비즈니스학과)
Keywords
도시재생, 텍스트 마이닝, 토픽모델링, 머신러닝 분류 알고리즘, 도시재생 유형 분류
Abstract
현 정부의 주요 국책사업 중 하나인 도시재생 뉴딜사업은 매년 100 곳씩, 5년간 500곳을대상으로 50조를 투자하여 낙후된 지역을 개발하는 것으로 언론과 지자체의 높은 이목이 집중되고 있다. 그러나, 현재 이 사업모델은 면적 규모에 따라“우리동네 살리기, 주거정비지원형, 일반근린형, 중심시가지형, 경제기반형” 등 다섯 가지로 나뉘어 추진되어 그 지역 본래의 특성을 반영하지 못하고 있다. 국내 도시재생 성공 키워드는 “주민 참여”, “지역특화” “부처협업”, “민관협력”이다.
성공 키워드에 따르면 지자체에서 정부에게 도시재생 사업을 제안할 때 지역주민, 민간기업의 도움과 함께 도시의 특성을정확히 이해하고 도시의 특성에 어울리는 방향으로 사업을 추진하는 것이 가장 중요하다는 것을 알 수 있다. 또한 도시재생 사업 후 발생하는 부작용 중 하나인 젠트리피케이션 문제를 고려하면 그 지역 특성에 맞는 도시재생 유형을 선정하여추진하는 것이 중요하다.
이에 본 연구는 ‘도시재생 뉴딜 사업’ 방법론의 한계점을 보완하기 위해, 기존 서울시가 지역 특성에 기반하여 추진하고 있는 “2025 서울시 도시재생 전략계획”의 도시재생 유형을 참고하여 도시재생 사업지에 맞는 도시재생 유형을 추천하는 시스템을 머신러닝 알고리즘을 활용하여 제안하고자 한다. 서울시 도시재생 유형은 “저이용저개발, 쇠퇴낙후, 노후주거, 역사문화자원 특화” 네 가지로 분류된다 (Shon and Park, 2017). 지역 특성을 파악하기 위해 총 4가지 도시재생 유형에 대해 사업이 진행된 22개의 지역에 대한 뉴스 미디어 10만여건의 텍스트 데이터를 수집하였다. 수집된 텍스트를 이용하여 도시재생 유형에 따른 지역별 주요 키워드를 도출하고 토픽모델링을 수행하여 유형별 차이가 있는 지 탐색해 보았다. 다음 단계로 주어진 텍스트를 기반으로 도시재생 유형을 추천하는 추천시스템 구축을 위해 텍스트 데이터를 벡터로변환하여 머신러닝 분류모델을 개발하였고, 이를 검증한 결과 97% 정확도를 보였다. 따라서 본 연구에서 제안하는 추천시스템은 도시재생 사업을 진행하는 과정에서 신규 사업지의 지역 특성에 기반한 도시재생 유형을 추천할 수 있을 것으로기대된다.
Cite this article
JIIS Style
Kim, I., J. Lee, H. Kim, and J. Kang, "Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining", Journal of Intelligence and Information Systems, Vol. 26, No. 3 (2020), 149~169.

IEEE Style
Ikjun Kim, Junho Lee, Hyomin Kim, and Juyoung Kang, "Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining", Journal of Intelligence and Information Systems, vol. 26, no. 3, pp. 149~169, 2020.

ACM Style
Kim, I., Lee, J., Kim, H., and Kang, J., 2020. Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining. Journal of Intelligence and Information Systems. 26, 3, 149--169.
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@article{Kim:JIIS:2020:821,
author = {Kim, Ikjun and Lee, Junho and Kim, Hyomin and Kang, Juyoung},
title = {Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining},
journal = {Journal of Intelligence and Information Systems},
issue_date = {September 2020},
volume = {26},
number = {3},
month = Sep,
year = {2020},
issn = {2288-4866},
pages = {149--169},
url = {http://dx.doi.org/10.13088/jiis.2020.26.3.149 },
doi = {10.13088/jiis.2020.26.3.149},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Urban Regeneration, Text Mining, Topic Modeling, Classification and Machine Learning
},
}
%0 Journal Article
%1 821
%A Ikjun Kim
%A Junho Lee
%A Hyomin Kim
%A Juyoung Kang
%T Suggestion of Urban Regeneration Type Recommendation System Based on Local Characteristics Using Text Mining
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 26
%N 3
%P 149-169
%D 2020
%R 10.13088/jiis.2020.26.3.149
%I Korea Intelligent Information System Society