Song Yena, Ahn Kwangwon, An Sihyun, Jang Hanwool
Department of Geography, Chonnam National University, Gwangju 61186, South Korea.
Department of Industrial Engineering, Yonsei University, Seoul 03722, South Korea.
Data Brief. 2021 Feb 24;35:106877. doi: 10.1016/j.dib.2021.106877. eCollection 2021 Apr.
This article presents a database cleaned and generated for analyzing the economic impact of subway network on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impact and we focused on the economic impact reflected in housing prices. Although our emphasis is on transit accessibility and housing prices, the dataset presented is applicable to other analyses. It includes a wide range of variables closely related to housing prices such as housing properties, local demographic characteristics, local amenities, and seasonal control variables. Various distance variables constructed in a geographic information system environment using public data are useful for exploring the environmental impact on housing prices. These data cover four metropolitan areas-Busan, Daegu, Daejeon, and Gwangju-and provide accurate information on their metropolitan structures distinct from the capital city, Greater Seoul. An empirical analysis performed by Ahn et al. [1] is based on this dataset.
本文展示了一个经过清理和生成的数据库,用于分析地铁网络对大都市地区房价的经济影响。交通网络的提供以及随之而来的可达性改善会产生各种影响,而我们关注的是房价中所反映的经济影响。尽管我们重点关注交通可达性和房价,但所呈现的数据集也适用于其他分析。它包括了一系列与房价密切相关的变量,如房屋属性、当地人口特征、当地便利设施以及季节性控制变量。利用公共数据在地理信息系统环境中构建的各种距离变量,对于探究环境对房价的影响很有用。这些数据涵盖了四个大都市地区——釜山、大邱、大田和光州——并提供了与首都首尔不同的大都市结构的准确信息。安等人[1]所进行的实证分析就是基于这个数据集。