ClusterRadar: an Interactive Web-Tool for the Multi-Method Exploration of Spatial Clusters Over Time
<|reference_start|>ClusterRadar: an Interactive Web-Tool for the Multi-Method Exploration of Spatial Clusters Over Time: Spatial cluster analysis, the detection of localized patterns of similarity in geospatial data, has a wide-range of applications for scientific discovery and practical decision making. One way to detect spatial clusters is by using local indicators of spatial association, such as Local Moran's I or Getis-Ord Gi*. However, different indicators tend to produce substantially different results due to their distinct operational characteristics. Choosing a suitable method or comparing results from multiple methods is a complex task. Furthermore, spatial clusters are dynamic and it is often useful to track their evolution over time, which adds an additional layer of complexity. ClusterRadar is a web-tool designed to address these analytical challenges. The tool allows users to easily perform spatial clustering and analyze the results in an interactive environment, uniquely prioritizing temporal analysis and the comparison of multiple methods. The tool's interactive dashboard presents several visualizations, each offering a distinct perspective of the temporal and methodological aspects of the spatial clustering results. ClusterRadar has several features designed to maximize its utility to a broad user-base, including support for various geospatial formats, and a fully in-browser execution environment to preserve the privacy of sensitive data. Feedback from a varied set of researchers suggests ClusterRadar's potential for enhancing the temporal analysis of spatial clusters.<|reference_end|>
@article{mason2024clusterradar:,
    title={ClusterRadar: an Interactive Web-Tool for the Multi-Method Exploration
  of Spatial Clusters Over Time},
    author={Lee Mason, Bl'anaid Hicks, Jonas S. Almeida},
    journal={arXiv preprint arXiv:2404.05897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05897},
    primaryClass={cs.HC}
}