SPATIOTEMPORAL DOMAIN DECOMPOSITION FOR MASSIVE PARALLEL COMPUTATION OF SPACE-TIME KERNEL DENSITY

9 citations as recorded by crossref.

1. A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States M. Desjardins et al. 10.1080/15230406.2018.1515664
2. Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities J. Ajayakumar & E. Shook 10.1080/13658816.2020.1732393
3. Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions A. Hohl et al. 10.1007/s00603-018-1488-z
4. Detecting space–time patterns of disease risk under dynamic background population A. Hohl et al. 10.1007/s10109-022-00377-7
5. Accelerating the discovery of space-time patterns of infectious diseases using parallel computing A. Hohl et al. 10.1016/j.sste.2016.05.002
6. Supporting local health decision making with spatial video: Dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua A. Curtis et al. 10.1016/j.apgeog.2017.08.008
7. Fast slope algorithm with the use of vectorization and parallelization for multicore architectures B. Bylina et al. 10.1007/s10707-023-00500-w
8. Spatiotemporal characteristics of the ancient water wells for the past 3,000a in Zhejiang Province, China: a perspective of geography H. Zeng et al. 10.1080/19475683.2023.2166582
9. Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis Y. Wang et al. 10.1016/j.future.2019.11.036