English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

With increasing point of interest (POI) datasets available with fine-grainedspatial and temporal attributes, space-time Ripley's K function has beenregarded as a powerful approach to analyze spatiotemporal point process.However, space-time Ripley's K function is computationally intensive forpoint-wise distance comparisons, edge correction and simulations forsignificance testing. Parallel computing technologies like OpenMP, MPI and CUDAhave been leveraged to accelerate the K function, and related experiments havedemonstrated the substantial acceleration. Nevertheless, previous works havenot extended optimization of Ripley's K function from space dimension tospace-time dimension. Without sophisticated spatiotemporal query andpartitioning mechanisms, extra computational overhead can be problematic.Meanwhile, these researches were limited by the restricted scalability andrelative expensive programming cost of parallel frameworks and impeded theirapplications for large POI dataset and Ripley's K function variations. Thispaper presents a distributed computing method to accelerate space-time Ripley'sK function upon state-of-the-art distributed computing framework Apache Spark,and four strategies are adopted to simplify calculation procedures andaccelerate distributed computing respectively. Based on the optimized method, aweb-based visual analytics framework prototype has been developed. Experimentsprove the feasibility and time efficiency of the proposed method, and alsodemonstrate its value on promoting applications of space-time Ripley's Kfunction in ecology, geography, sociology, economics, urban transportation andother fields.

Optimizing and accelerating space-time Ripley's K function based on Apache Spark for distributed spatiotemporal point pattern analysis