Collaborative geographic analysis can lead to better outcomes but requires complicated interactions among participants, support resources and analytic tools. A process expression with explicit structure and content can help coordinate and guide these interactions. For different geographic problems, the structure and content of collaborative geographic analysis are generally distinct. Since the process structure embodies the pathway of problem-solving and the process content contains the information flow and internal interactions, both the structure and the content of the process expression must be clarified during process customization. However, relevant studies concerning the collaborative geographic analysis process mainly focus on the process structure, which remains a “black box” in terms of the process content, especially the internal interactions. Therefore, this article designs a customizable process expression model that takes both process structure and content into account and proposes a corresponding process customization method for collaborative geographic analysis. Additionally, a support method for geographic analysis process implementation is also provided. To verify the feasibility and capability, these methods were implemented in a prototype system, and a case study on traffic noise assessment was conducted. The results suggest that the proposed strategy can effectively improve geographic analysis by customizing processes, guiding participants, performing interactions, and recording operations throughout the process.

Due to its advantages in participation and collaboration, participatory geo-analysis has been used for solving different types of geographical issues. Participatory geo-analysis is usually a complicated process consisting of various tasks that may involve different multidisciplinary participants. Previous studies have focused primarily on how to improve participation in specific individual tasks, especially idea discussion and decision-making, but they have ignored collaboration throughout the entire process. During a complete participatory geo-analysis effort, the various participants should concentrate on their familiar work and fully exploit their talents to perform work collaboratively. Therefore, we propose an activity-based process construction method to assist different participants in understanding the geo-analysis process and in concentrating on their familiar work. Eight core activities are established for the geo-analysis process: (1) context definition and resource collection, (2) data processing, (3) data analysis, (4) data visualization, (5) geo-analysis model construction, (6) model effectiveness evaluation, (7) geographical simulation, and (8) decision making. By using a visualization-based method, different activities can be linked together to represent the entire analytical process. Moreover, each activity is designed via a specialized web-based workspace in which online tools and resources are accessed to assist the participants with their geo-analysis practices. A prototype system was developed based on the proposed method, and a case study on a participatory risk assessment of coronavirus disease 2019 (COVID-19) was demonstrated using this system. The result suggests that the proposed method can promote collaboration among participants with different backgrounds, and verifies its feasibility and suitability.

Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.