{"title":"Perceptible landscape patterns reveal invisible socioeconomic profiles of cities","authors":"","doi":"10.1016/j.scib.2024.06.022","DOIUrl":null,"url":null,"abstract":"<div><div>Urban landscape is directly perceived by residents and is a significant symbol of urbanization development. A comprehensive assessment of urban landscapes is crucial for guiding the development of inclusive, resilient, and sustainable cities and human settlements. Previous studies have primarily analyzed two-dimensional landscape indicators derived from satellite remote sensing<span>, potentially overlooking the valuable insights provided by the three-dimensional configuration of landscapes. This limitation arises from the high cost of acquiring large-area three-dimensional data and the lack of effective assessment indicators. Here, we propose four urban landscapes indicators in three dimensions (UL3D): greenness, grayness, openness, and crowding. We construct the UL3D using 4.03 million street view images from 303 major cities in China, employing a deep learning approach. We combine urban background and two-dimensional urban landscape indicators with UL3D to predict the socioeconomic profiles of cities. The results show that UL3D indicators differs from two-dimensional landscape indicators, with a low average correlation coefficient of 0.31 between them. Urban landscapes had a changing point in 2018–2019 due to new urbanization initiatives, with grayness and crowding rates slowing, while openness increased. The incorporation of UL3D indicators significantly enhances the explanatory power of the regression model for predicting socioeconomic profiles. Specifically, GDP per capita, urban population rate, built-up area per capita, and hospital count correspond to improvements of 25.0%, 19.8%, 35.5%, and 19.2%, respectively. These findings indicate that UL3D indicators have the potential to reflect the socioeconomic profiles of cities.</span></div></div>","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":null,"pages":null},"PeriodicalIF":18.8000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Bulletin","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S209592732400447X","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Urban landscape is directly perceived by residents and is a significant symbol of urbanization development. A comprehensive assessment of urban landscapes is crucial for guiding the development of inclusive, resilient, and sustainable cities and human settlements. Previous studies have primarily analyzed two-dimensional landscape indicators derived from satellite remote sensing, potentially overlooking the valuable insights provided by the three-dimensional configuration of landscapes. This limitation arises from the high cost of acquiring large-area three-dimensional data and the lack of effective assessment indicators. Here, we propose four urban landscapes indicators in three dimensions (UL3D): greenness, grayness, openness, and crowding. We construct the UL3D using 4.03 million street view images from 303 major cities in China, employing a deep learning approach. We combine urban background and two-dimensional urban landscape indicators with UL3D to predict the socioeconomic profiles of cities. The results show that UL3D indicators differs from two-dimensional landscape indicators, with a low average correlation coefficient of 0.31 between them. Urban landscapes had a changing point in 2018–2019 due to new urbanization initiatives, with grayness and crowding rates slowing, while openness increased. The incorporation of UL3D indicators significantly enhances the explanatory power of the regression model for predicting socioeconomic profiles. Specifically, GDP per capita, urban population rate, built-up area per capita, and hospital count correspond to improvements of 25.0%, 19.8%, 35.5%, and 19.2%, respectively. These findings indicate that UL3D indicators have the potential to reflect the socioeconomic profiles of cities.
期刊介绍:
Science Bulletin (Sci. Bull., formerly known as Chinese Science Bulletin) is a multidisciplinary academic journal supervised by the Chinese Academy of Sciences (CAS) and co-sponsored by the CAS and the National Natural Science Foundation of China (NSFC). Sci. Bull. is a semi-monthly international journal publishing high-caliber peer-reviewed research on a broad range of natural sciences and high-tech fields on the basis of its originality, scientific significance and whether it is of general interest. In addition, we are committed to serving the scientific community with immediate, authoritative news and valuable insights into upcoming trends around the globe.