{"title":"Extraction of Areas Encompassing Communities in Which People Desire to Live Using Residential-Preference Questionnaire Data","authors":"Y. Arai, Masaki Aijima, Kayo Koide","doi":"10.7222/marketingreview.2020.007","DOIUrl":null,"url":null,"abstract":"Currently, the mainstream online search method used by many real estate portal sites is to show properties in the order of their distance from the nearest rail (including subway) station (i.e., the closest property first, etc.). However, this method does not necessarily enable a person to search for the place of residence that “fits” that person in a logical manner. For example, if it were known that certain community (town) “clusters” had similar characteristics, this would enable a search method whereby selection is made according to community characteristics. In the present study, a network of desirable communities was constructed from desirable residential-preference questionnaire data. Using a modularity optimization method, an attempt was made to extract area clusters having similar characteristics, with closely resembling spatial and psychological distances. As a result, the Tokyo metropolitan area could be divided into 75 such clusters. Each cluster was subsequently characterized using correspondence analysis. Keyword : Complex network, Community detection, Market segmentation, Correspondence analysis 要約:現在の不動産ポータルサイトの多くは,沿線から駅と順に物件を絞り込んでいくような探索方法が主流である。しかしそ れが,合理的に自分に合った居住地を探索する方法だとは限らない。たとえば,似た特徴を持った街の集団が分かっていれば, 街の特徴から選択するような探索方法も可能である。そこで本研究では,住みたい街アンケート個票データから,住みたい街 ネットワークを構築し,モジュラリティ最大化による手法を用いることで,空間的・心理的距離が近く,似た特徴を持つ街の集 団を圏域として抽出することを試みた。その結果,首都圏を 75の圏域に分割することができた。さらにコレスポンデンス分析 を用いることで,各圏域についての特徴付けを行った。 キーワード:複雑系ネットワーク,コミュニティ抽出,マーケットセグメンテーション,コレスポンデンス分析 Information : Received 12 August 2019; Accepted 17 November 2019","PeriodicalId":266707,"journal":{"name":"Japan Marketing Review","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japan Marketing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7222/marketingreview.2020.007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Currently, the mainstream online search method used by many real estate portal sites is to show properties in the order of their distance from the nearest rail (including subway) station (i.e., the closest property first, etc.). However, this method does not necessarily enable a person to search for the place of residence that “fits” that person in a logical manner. For example, if it were known that certain community (town) “clusters” had similar characteristics, this would enable a search method whereby selection is made according to community characteristics. In the present study, a network of desirable communities was constructed from desirable residential-preference questionnaire data. Using a modularity optimization method, an attempt was made to extract area clusters having similar characteristics, with closely resembling spatial and psychological distances. As a result, the Tokyo metropolitan area could be divided into 75 such clusters. Each cluster was subsequently characterized using correspondence analysis. Keyword : Complex network, Community detection, Market segmentation, Correspondence analysis 要約:現在の不動産ポータルサイトの多くは,沿線から駅と順に物件を絞り込んでいくような探索方法が主流である。しかしそ れが,合理的に自分に合った居住地を探索する方法だとは限らない。たとえば,似た特徴を持った街の集団が分かっていれば, 街の特徴から選択するような探索方法も可能である。そこで本研究では,住みたい街アンケート個票データから,住みたい街 ネットワークを構築し,モジュラリティ最大化による手法を用いることで,空間的・心理的距離が近く,似た特徴を持つ街の集 団を圏域として抽出することを試みた。その結果,首都圏を 75の圏域に分割することができた。さらにコレスポンデンス分析 を用いることで,各圏域についての特徴付けを行った。 キーワード:複雑系ネットワーク,コミュニティ抽出,マーケットセグメンテーション,コレスポンデンス分析 Information : Received 12 August 2019; Accepted 17 November 2019