DEVELOPING PREDICTIVE MODEL FOR VACANT HOUSING DISTRIBUTION USING MUNICIPALITY-OWNED DATA —CASE STUDY IN MAEBASHI CITY—

• BABA Hiroki

  京都大学特定 東南アジア地域研究研究所/白眉センター

• AKIYAMA Yuki

  東京都市大学 建築都市デザイン学部

• YACHIDA Osamu

  前橋市役所未来の芽創造課

Bibliographic Information

Other Title

• 自治体保有データを活用した空き家の空間分布の将来予測モデル構築 ―群馬県前橋市を対象として―

Abstract

 With the increase number in vacancies, researchers attempt to estimate the spatial distribution of vacant housing. In specifying the model, municipality-owned data are recently included due to the existence of features that contribute to the accuracy of the model. This study constructs a predictive model of future vacant housing distribution using municipality-owned data. We employ XGBoost, a machine learning method, to deal with the existence of missing values and non-linearity of data structure. In consequence, we obtain the following findings. First, since the model proposed is based on decision trees, which enable us to flexibly deal with complicated and missing data. Second, the important features we picked up follow the arguments by previous studies, and thus confirmed the validity of the proposed model. Third, the proposed model approximates 84.3 percent of accuracy per 125-meter grid cell, which holds the sufficient level of the accuracy for municipalities to take advantage of.

Journal

• Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management)

  Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 77 (2), 62-71, 2021

  Japan Society of Civil Engineers

Keywords

• vacant house
• machine learning
• basic resident register
• use of tap water
• property tax ledger

Details

• CRID: 1390288082109895552
• NII Article ID: 130008040583
• DOI: 10.2208/jscejipm.77.2_62
• ISSN: 21856540
• Web Site: https://www.jstage.jst.go.jp/article/jscejipm/77/2/77_62/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed