Distributed Clustering by Local Direction Centrality on Spark

publications

Parallelises the clustering-by-local-direction-centrality algorithm on Apache Spark with spatial partitioning and performance optimisations for large datasets.

Citation
Gui Z., Huang Z., Chen H., Peng D., Liu Y., Fang G., Lan Q., Zhao A., Wu H. (2025). A performance-enhanced distributed computing framework for clustering by local direction centrality upon Apache Spark. Big Earth Data, 9(4), 797–827. doi:10.1080/20964471.2025.2529639

Cover figure: Enhanced framework of the distributed D-CDC algorithm. From Gui et al. (2025), Big Earth Data.

Spatial Data
Anqi Zhao
Authors
Anqi Zhao
PhD Student in Geospatial AI
I am a PhD student at the Chair of Cartography and Visual Analytics at the Technical University of Munich, supervised by Prof. Liqiu Meng and mentored by Prof. Yu Feng. I came to AI from maps. I studied geodesy, cartography and GIS at Wuhan University, where I first used language models to make sense of geospatial code, and I now spend most of my time on the AI side of GeoAI. Besides research, I supervise Master’s students and maintain open learning resources on LLM agents and web scraping.