ABSTRACT

Empirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view image, is rich and abundant. We construct a ResNet-50-based social detection model to explore the potential relationship between street view images and job-housing attributes. The method extracts street view images of a neighborhood in all eight directions to predict land parcels’ job-housing attributes and uses an entropy index to measure the degree of job-housing mixture in Shenzhen as an example. The social-detection model performs well with a low RMSE (0.1094) in identifying job-housing patterns. The eight-direction neighborhood method shows the best support for sufficient neighborhood information from street view images (RMSE = 0.1135) compared with other neighborhood methods. This study demonstrates the feasibility of using street-view images and deep learning to characterize job-housing attributes consistent with findings from urban studies with socioeconomic data; for example, the research finding concurs that Shenzhen has many high job-housing mixtures with very few areas designated for jobs or residences. The proposed method, when applied regularly, can help monitor spatial dynamics of urban job-housing patterns to inform city planning and development.

摘要

实证数据仅限于破译人们在大城市生活和工作的地点；然而，街区信息，如街景图像，丰富而丰富。我们构建了一个基于 ResNet-50 的社会检测模型来探索街景图像和工作住房属性之间的潜在关系。该方法提取一个街区所有八个方向的街景图像来预测地块的就业住房属性，并以深圳的就业住房混合程度为例，使用熵指数来衡量就业住房混合程度。社会检测模型在识别工作住房模式方面表现良好，RMSE (0.1094) 较低。与其他邻域方法相比，八方向邻域方法显示了对街景图像中足够邻域信息的最佳支持 (RMSE = 0.1135)。这项研究证明了使用街景图像和深度学习来表征职业住房属性的可行性，这与城市研究和社会经济数据的结果一致；例如，研究结果一致认为，深圳有许多高就业和住房的混合体，而指定用于工作或居住的区域很少。定期应用所提出的方法可以帮助监测城市就业住房模式的空间动态，为城市规划和发展提供信息。