Geospatial Data Clustering in Network Space: A Survey

ABSTRACT Geospatial data enhances traditional datasets by integrating spatial and temporal dimensions, facilitating advanced visualizations and comprehensive analytical insights. As a fundamental aspect of geospatial analytics, geospatial data clustering (GDC) has become a prominent area of academic research, playing a critical role in theoretical exploration and applied domains. GDC seeks to group geospatial objects based on inherent similarities, a necessity driven by modern datasets' increasing scale and complexity, particularly those within geographic information systems (GIS). This paper highlights key challenges and advancements in GDC, including spatial data clustering (SDC), clustering techniques within GIS, and algorithms designed for geospatial data clustering in network spaces (GDC in NS). Practical implementations of these methodologies encompass diverse applications such as hotspot analysis, infectious disease monitoring, transportation optimization, urban traffic management, and emergency response planning. These contributions are foundational for advancing scholarly research and addressing domain‐specific challenges in this field.