Lepetit Quentin, Viguié Vincent, Liotta Charlotte
Centre International de Recherche sur L'Environnement et le Développement (CIRED), 45bis, Av de La Belle Gabrielle, Nogent-sur-Marne F-94736, France.
TU Berlin, Straße des 17. Juni 135, Berlin D-10623, Germany.
Data Brief. 2023 Feb 9;47:108962. doi: 10.1016/j.dib.2023.108962. eCollection 2023 Apr.
This work presents a gridded dataset on real estate and transportation in 192 worldwide urban areas, obtained from the Google Maps API and the web scraping of real estate websites. For each city of the sample, these data have been associated with the corresponding population density and land cover data, extracted from the GHS POP and ESA CCI data respectively, and aggregated on a 1 km resolution grid, allowing for an integrated analysis. This dataset is the first to include spatialized real estate and transportation data in a large sample of cities covering 800 million people in both developed and developing countries. These data can be used as inputs for urban modeling purposes, transport modeling, or between-city comparisons in urban forms and transportation networks, and allow further analyses on e.g. urban sprawl, access to transportation, or equity in housing prices and access to transportation.
这项研究展示了一个关于全球192个城市地区房地产和交通的网格化数据集，该数据集来自谷歌地图应用程序编程接口（Google Maps API）以及房地产网站的网络爬虫。对于样本中的每个城市，这些数据分别与从全球人类住区层人口（GHS POP）数据和欧洲航天局气候变化倡议土地覆盖（ESA CCI）数据中提取的相应人口密度和土地覆盖数据相关联，并在1公里分辨率的网格上进行汇总，从而实现综合分析。该数据集首次在涵盖发达国家和发展中国家8亿人口的大量城市样本中纳入了空间化的房地产和交通数据。这些数据可作为城市建模、交通建模的输入，或用于城市形态和交通网络的城市间比较，并可进一步分析例如城市扩张、交通可达性或房价公平性以及交通可达性等问题。
