Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.     

Practical engineering approaches and infrastructure to address the problem of marine debris in Korea

As a solution to the problem of persistent solid marine debris, a nationwide project began in Korea in 1999 to develop and popularize fundamental changes to the infrastructure. The ten year project, called “A Practical Integrated System for Marine Debris,” consists of four linked types of technology: prevention, deep-water survey, removal and treatment (recycling). These reflect the characteristics of marine debris, which though widespread, vary by location and time of generation. Each technical component has each representative outcome that has been outreached the local governments and marine debris-related associations. The in situ infrastructures lead to enhance the retrieval of the marine debris and create direct and indirect benefits to industry. Both end-of-pipe technology improvement and the introduction of front-of-pipe technology should be considered as we strive to reduce the generation of marine debris in Korean coastal areas.