Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

Traffic flow forecasting is crucial to Intelligent Transportation Systems (ITS), particularly for route planning and traffic management. Spatiotemporal graph neural networks have been widely used for this purpose, where a global graph structure is mainly used, but local graph features are ignored. Multi-scale spatiotemporal features—the combined application of global and local graph features—can help effectively discover the underlying spatiotemporal patterns. This study proposed a multi-scale adaptive spatiotemporal forecasting model called Adaptive SpatioTemporal InceptionNet (AST-InceptionNet). To enrich the captured features and extract multi-scale spatiotemporal ones, the inception part is applied to combine local spatiotemporal features with several global ones. Additionally, we developed a fully adaptive graph convolution method, including topologically adaptive graph convolution and an adaptive adjacency matrix. It can autonomously and dynamically learn spatial heterogeneity and enable the proposed model to work with unknown adjacency relations. The proficiency of AST-InceptionNet was validated using four publicly available real-world traffic flow datasets. The experiments demonstrate a satisfactory performance from the proposed model, outperforming existing state-of-the-art methods by up to approximately 12%.

Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people’s daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spatiotemporal graph neural network has attracted attention from academic and business domains for its powerful spatiotemporal pattern capturing capability. However, the existing work focused on the overall traffic network instead of traffic nodes, and the latter can be useful in learning different patterns among nodes. Moreover, there are few works that captured fine-grained node-specific spatiotemporal feature extraction at multiple scales at the same time. To unfold the node pattern, a node embedding parameter was designed to adaptively learn nodes patterns in adjacency matrix and graph convolution layer. To address this multi-scale problem, we adopted the idea of Res2Net and designed a hierarchical temporal attention layer and hierarchical adaptive graph convolution layer. Based on the above methods, a novel model, called Temporal Residual II Graph Convolutional Network (Tres2GCN), was proposed to capture not only multi-scale spatiotemporal but also fine-grained features. Tres2GCN was validated by comparing it with 10 baseline methods using two public traffic volume datasets. The results show that our model performs good accuracy, outperforming existing methods by up to 9.4%.

Multi-Resolution Segmentation (MRS) is known to be a general segmentation algorithm for very-high-resolution (VHR) remote sensing applications. The critical problems of MRS are the optimization of the parameters and the evaluation of segmentation quality. Based on the principle of maximizing the intra-object homogeneity and inter-object heterogeneity, we propose a novel Normalized Segmentation Quality Index (NSQI) and use level filtering to acquire the optimal parameters of the MRS algorithm. Using the geometric and arithmetic discrepancy between the segmented object and the reference object as the evaluation criterion, we then evaluate the quality of the segmented objects. The results of two experiments confirm the effectiveness of our meanNSQI and discrepancy measure approach. Additionally, a sensitivity analysis of the segmentation quality index demonstrates the reliability of the meanNSQI and the discrepancy measure.

The aim of this study was to provide scientific support for the creation of policy on inbound tourism. A comprehensive and thorough understanding of fine-grained spatiotemporal dynamic patterns is crucial for tourism management, planning and policymaking. In spite of spatiotemporal pattern analysis on tourism movements, environment- and society-related topics have been developed to stimulate tourism. However, few studies have focused on the fine-grained spatiotemporal analysis of tourist behavior. Depending on a fine-grained Flickr data source, we investigated the spatiotemporal dynamic patterns of inbound tourism in the context of fine granularity in the spatial and temporal dimensions. The proposed approach based on fine-grained Flickr data and the emerging spatiotemporal analysis method was to first conduct a refined temporal variation analysis based on the annual, monthly, and daily variation; second, a thorough analysis of the seasonality of tourism was conducted with the kernel density estimation (KDE) method; third, the correlation between the attraction grade and popularity was complementarily exploited with both qualitative and quantitative methods; and finally, the patterns were identified and visualized with the space-time cube in the context of fine granularity. The results from the analysis revealed that the downtown region of Beijing was the most popular place throughout the year due to the many famous Chinese cultural heritage attractions. In contrast, the landscape sites and thematic parks were nearly cold spots because of their strong seasonality. Our approach can also be applied to other crowdsourced data, such as that from Twitter and Instagram. Spatiotemporal analysis and empirical research have interesting implications for other cities in China or other developing countries.