In the contemporary epoch, the management and visualization of geospatial information in web browsers have gained substantial importance. Web maps are indispensable tools across various tourism, goods delivery, and ecology sectors. Furthermore, the extensive support of web browsers on diverse devices enhances the accessibility of geospatial data on the web for various users. However, the incessant increment of geospatial information poses new challenges in efficiently displaying data and navigation through these data on web maps. Therefore, the clustering of geospatial data is crucial in dealing with them. Different clustering methods may affect the performance or visual clarity of web maps.
To improve the user experience and optimize the use of computing resources, geodata clustering becomes a necessary tool for processing large volumes of markers on the map. Despite significant progress in the development of geodata clustering solutions in web maps, there are some challenges that developers and users may encounter. In this article, challenges with scaling, dynamic cluster data, and heterogeneity of data are described. Existing problems in geodata clustering in web maps require additional research and development. Understanding these issues will help developers and researchers improve existing solutions and create new methods and approaches for efficient clustering of geodata in web maps. The urgency of solving the problem lies in the search for effective clustering solutions that provide an opportunity to ensure convenient interactivity and fast processing of geodata in web maps.
This study provides a comprehensive review of data types and clustering methods. Tools and libraries for geodata clustering in web maps are analyzed. Different types of geodata and approaches to working with them were also studied. Concepts such as semi-static data and their positions alongside static and dynamic data types are elucidated.
Through the analysis, optimal scenarios for applying specific clustering methods or the utilization of server-side clustering approaches have been identified. Conclusions have also been drawn on the preferred approach when handling extensive volumes of static or semi-static geospatial data, particularly advocating for the application of server-side clustering with caching.
In conclusion, various clustering approaches in web maps, both client-side and server-side, have been scrutinized. The advantages and disadvantages of both approaches, along with recommendations on when to apply each method, have been delineated. A noticeable absence of explicit approaches in clustering vast geospatial data for web map representation underpins the relevance and necessity of research in this direction.
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