In this study, a novel self-supervised contrastive learning framework that integrates geographic knowledge into the analysis of street view imagery for representing urban space is proposed. Traditional methods that rely solely on deep learning often struggle to capture the complex spatial characteristics of urban environments. To address this issue, in the proposed framework, we first extracted visual knowledge (VK) from street view imagery using semantic segmentation and then constructed contrastive samples through VK–imagery pairs. Finally, we introduced distance-weighted temperatures into the contrastive loss function to adjust the similarities of urban features encoded in the representations on the basis of geographical proximity, thereby integrating semantic knowledge among geographical locations (GSK). The method was validated through two case studies: urban village classification in Guangzhou and Foshan and urban-mobility pattern prediction in Shenzhen. The results showed significant improvements in classification accuracy (OA: 0.967) and reduced prediction error (MAE: 28.069) compared with conventional approaches. This research demonstrates the effectiveness of integrating geographic knowledge (including VK and GSK) into contrastive learning. The approach enhances model interpretability and generalizability for urban studies and provides a tool for analyzing urban development patterns and mobility needs that will be useful for urban planning and policymaking.

Local indicators of co-location are fundamental tools for measuring spatial relationships between two types of categorical spatial events. Existing methods have two major shortcomings. First, the results of their indicators are difficult to interpret. Second, when the popular random labeling is used as the null model, significance is assessed via Monte Carlo simulation, overlooking the fact that their probability distribution can be derived directly. To solve these problems, this study proposes a sounder semantic-parametric statistical framework under the random labeling null model. First, we propose a local indicators definition strategy on the basis of the ratio of the observed co-location to the expected co-location under the null hypothesis. This strategy assigns clear semantic meaning to the indicators, improving their interpretability. Then, the mathematical forms of local indicators for the four main spatial proximity settings are derived. Finally, a parametric method is developed to evaluate significance based on the exact probability distribution derived from the mathematical form of the local indicator. The framework is available as an open-source Python package named ESLocalIndi. Two case studies on Philadelphia crimes and Hong Kong traffic crashes demonstrate ESLocalIndi’s utility, offering insights to support better policy formulation.

Urbanization has been a driving force for economic growth, yet it has also caused the emergence of informal urban settlements such as urban villages (UVs), which are characterized by issues such as arbitrary land use, high-density construction, and insufficient infrastructure. In previous studies on UV detection, the semantic imbalance and feature interaction among cross-modal data have not been comprehensively considered, impacting the accuracy and interpretability of the results. In this work, a cross-modal fusion framework is proposed that integrates high-resolution remote sensing and street view images for UV detection. First, convolutional neural networks (ResNet-50) are used for feature extraction from both remote sensing and street view images. Then, an inner product channel attention module is used to dynamically adjust weights while considering multiangle views of street view images. A cross-modal feature fusion module that incorporates dilation convolution and a global-based feature fusion block is used to enhance feature interaction and fusion. The method has an overall accuracy (OA) of 0.975 for UV classification in a case study of the Guangzhou–Foshan metropolitan area in China, outperforming a set of baseline methods. The integration of remote sensing and street view images improves the OA value by approximately 2%. This work enhances the understanding of the distribution of UVs via both top-down and ground-level view data in an automatic and efficient way, providing urban planners with valuable insights to accurately identify UVs and support targeted, sustainable urban renewal aligned with the SDGs for inclusive, resilient cities.

Spatial community detection plays a crucial role in the analysis of spatially embedded networks. However, most existing methods adopt modularity as an objective function, which may fail to detect small spatial communities (i.e. the well-known resolution-limit problem). To alleviate this problem, a local network structure-based spatially constrained Leiden method was developed. First, the weights of the edges were reset based on the local network structure, which facilitated a clearer delineation between distinct communities. Second, we extended Leiden, an effective community detection method, to spatial community detection content by adding spatial constraints. Experiments on simulated datasets demonstrated that the proposed method is superior to four state-of-the-art methods for detecting spatial communities of different sizes. A case study conducted using the Shenzhen taxi dataset demonstrated that the proposed method outperformed four state-of-the-art methods in revealing urban spatial structures. Notably, the modularity of the spatial communities detected using the proposed method exhibited a marked improvement, nearly doubling that of the four comparative methods in the case study. This study presents a novel and promising framework for detecting spatial communities using modularity.

Investigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.

Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification.