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Road traffic crashes are becoming thorny issues being faced worldwide. Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out. However, the impact of built environment on traffic crash spatiotemporal trends has not received much attention. Moreover, the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected. To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes, this paper proposed a method of "ST-GWLR" for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression (GWLR) modeling methods. Firstly, the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model, hotspot analysis, and Mann-Kendall trend test. Then, the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression (GLR) model, to improve the accuracy of the model estimation. GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors, to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes. The performance of the GWLR models and GLR models was examined and compared sufficiently. The results showed that the proposed ST-GWLR, which captured spatial non-stationarity, performed better than the classic GLR combined with spatiotemporal analysis, and improved the prediction accuracy of the models by 14.9%, 13.9%, and 15.1%, respectively. There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors. The findings of this paper have positive implications for traffic safety management and urban built environment planning. [ABSTRACT FROM AUTHOR]

Land cover and its changes over time are significant for better understanding the Earth's fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs. [ABSTRACT FROM AUTHOR]

Public Map Service Platforms (PMSPs) provide embedded map services in domains such as forests and rivers. Users from different domains (Domain Users) prefer specific spatial features, and extracting the Browsing Interests of Domain Users (BIDUs) can help elucidate users' access intentions and provide suitable recommendations. Previous research has found that access frequency of spatial features is an indicator of users' browsing interests; however, high-frequency spatial features are sparsely distributed, resulting in inaccurate extraction of browsing interests. Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation. First, to extract spatial features in tiles, we proposed a k-nearest neighbor method for Point-of-Interest (POI) extraction and a template-based method for Land Uses/Land Covers extraction. Then, we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness. Finally, a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs. Compared with the baseline models, the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction. Our findings can help construct domain user profiles and support the development of intelligent PMSPs. [ABSTRACT FROM AUTHOR]