Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 12, 2025ACM Transactions on Spatial Algorithms and SystemsOpen Access

Progressive and Scalable Hotspot Detection through Local K-Function in Spatial Networks

View Full Paper
Ask AI
Bookmark
Share

Authors

YKYunfan KangYLYongyi LiuPJPratham Juvekar

Discussion

Loading...

Member takes

Overview

New algorithms enhance hotspot detection in spatial networks, significantly improving speed and accuracy.

Key Points

  • The proposed algorithms are up to 28 times faster in detecting predefined hotspots compared to existing methods.
  • A notable advancement includes an incremental approach that updates hotspot calculations dynamically with new events.
  • Utilizing the local K-function allows for efficient analysis of density and distribution of activities in spatial networks.
  • Experimental results demonstrate the algorithms' effectiveness using both real and synthetic datasets in large networks.

Cite This Study

Kang et al. (2025) studied this question.

synapsesocial.com/papers/68d44b2231b076d99fa5401ehttps://doi.org/10.1145/3766549
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Analysis of Spatial Association by Use of Distance Statistics1992 · 6,243 citations
  2. 2A density-based approach for detecting network-constrained clusters in spatial point events2018 · 29 citations
  3. 3NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space2019 · 49 citations
  4. 4Road network partitioning method based on Canopy-Kmeans clustering algorithm2020 · 29 citations
  5. 5SIMULATION AND THE MONTE CARLO METHOD1982 · 53 citations