  With increasing point of interest (POI) datasets available with fine-grained
spatial and temporal attributes, space-time Ripley's K function has been
regarded as a powerful approach to analyze spatiotemporal point process.
However, space-time Ripley's K function is computationally intensive for
point-wise distance comparisons, edge correction and simulations for
significance testing. Parallel computing technologies like OpenMP, MPI and CUDA
have been leveraged to accelerate the K function, and related experiments have
demonstrated the substantial acceleration. Nevertheless, previous works have
not extended optimization of Ripley's K function from space dimension to
space-time dimension. Without sophisticated spatiotemporal query and
partitioning mechanisms, extra computational overhead can be problematic.
Meanwhile, these researches were limited by the restricted scalability and
relative expensive programming cost of parallel frameworks and impeded their
applications for large POI dataset and Ripley's K function variations. This
paper presents a distributed computing method to accelerate space-time Ripley's
K function upon state-of-the-art distributed computing framework Apache Spark,
and four strategies are adopted to simplify calculation procedures and
accelerate distributed computing respectively. Based on the optimized method, a
web-based visual analytics framework prototype has been developed. Experiments
prove the feasibility and time efficiency of the proposed method, and also
demonstrate its value on promoting applications of space-time Ripley's K
function in ecology, geography, sociology, economics, urban transportation and
other fields.
