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Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes cannot be directly extracted from the trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby POI data using a geometry-driven technique to enhance the interpretability of the results. Furthermore, our model captures the semantics and relationships of the data in a high-dimensional space and identifies latent topics representing distinct trip purposes. These learned topics are analyzed using clustering algorithms to group similar trips, enabling trip purpose inference. And we evaluate our model using the trajectory data of Shenzhen and Chengdu, and compare it with baseline models. The results demonstrate that our model performs well. Furthermore, we analyze trajectory data containing trip purpose information to gain insight into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]