Clustering geospatial event data requires defining a distance function between events as well as representing neighborhood characteristics where an event occurred in numerical or categorical values. For events such as social unrest events, in addition to the geospatial coordinates and time stamps, other factors needed to understand how they evolve include socioeconomic factors that fuel, say, the emergence of a social unrest event, and infrastructural factors that facilitate, say, the propagation of an event to nearby regions. In this paper, we focus on addressing two main challenges in spatiotemporal clustering of such event data: (1) how to derive a numeric representation of nearby geospatial objects in a neighborhood for an event, and (2) how to improve the clustering process to scale well for very large datasets. To address the first challenge, we propose two metrics—proximity and density—of geospatial objects, and incorporate them into the definition of a distance function between events. To address the second challenge, we propose a novel Spatio-Temporal k-Dimensional Tree-based DBSCAN (ST-KDT-DBSCAN) clustering approach that restricts the search radius for each event during clustering by first organizing the dataset into a k-dimensional tree structure, subsequently creating a Fixed-Radius Near Neighbor (FRNN) object for each event, and then carrying out DBSCAN considering only each event’s FRNN object when computing reachability. We have applied the solutions to 29,371 unrest events with socioeconomic and infrastructural factors recorded for the year 2014 in India, to identify event episodes in order to analyze how social unrest evolves. Our results show the viability and scalability of our solutions.