A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation

Yuan, Kunxiaojia; Cheng, Xiaoqiang; Gui, Zhipeng; Li, Fa; Wu, Huayi

doi:10.1080/13658816.2018.1555831

Cited by 34 publications

(22 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Effective approaches for detecting and analyzing these point patterns would be helpful to investigate and interpret the spatiotemporal point process hidden behind geographic phenomenon or social events. Among the approaches of point pattern analysis, Ripley's K function stands out in three aspects: (1) it is a distance-based and scale-independent method, so Modifiable Areal Unit Problem (MAUP) can be avoided; (2) its parameters can be derived from research area, not like the bandwidth in kernel density estimation that usually relies on experience [4]; (3) it considers not only the nearest neighbor like Nearest-Neighbor-Index, but also the other neighbors within the maximum distance, hence the information behind the point pairs can be fully utilized. Therefore, Ripley's K function has been widely applied in many fields, such as ecology [5], archaeology [6], epidemiology [7], criminology [8], sociology [9,10], economics [11][12][13] and , biology and medical science [14].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Gui

et al. 2020

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Highlights: Distributed space-time Ripley's K function optimized by spatiotemporal principles. Performance analysis of different optimization strategies. Guidelines for accelerating spatiotemporal analysis with HPC technologies. Visual analytics framework for analyzing point pattern of large POI datasets. Support various applications, such as industrial spatial agglomeration exploration.Abstract: 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 haven't 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 stateof-the-art distributed computing framework Apache Spark, and four strategies are adopted to simplify calculation procedures and accelerate distributed computing respectively: 1) spatiotemporal index based on R-tree is utilized to retrieve potential spatiotemporally neighboring points with less distance comparison; 2) spatiotemporal edge correction weights are reused by 2tier cache to reduce repetitive computation in L value estimation and simulations; 3) spatiotemporal partitioning using KDB-tree is adopted to decrease ghost buffer redundancy in partitions and support near-balanced distributed processing; 4) customized serialization with compact representations of spatiotemporal objects and indexes is developed to lower the cost of data transmission. 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Wang

Gui

et al. 2020

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…This means computing a heatmap for each frame in an animation takes a significant amount of time. We mitigate this by storing points in a spatial index and only constructing a heatmap for a limited area but, we are likely obtain larger speedups by exploring quadtree [4,25] or GPU [26] heatmap construction algorithms. Future work involves incorporation of tabular data [2] as well a merging spatially-adjacent heatmaps via techniques such as connected component labeling [22].…”

Section: Discussionmentioning

confidence: 99%

Visualizing SpatioTemporal Keyword Trends in Online News Articles

Kastner

Samet

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

Online sources of news have steadily supplanted their paper counterparts alongside the growth of the internet. This growth in online news has led to a surplus of data in the form of the text of news articles published online. While an abundance of data is obviously desirable, it can make it difficult for a human to analyze and find trends in the data without assistance. The application demonstrated in the paper aims to aid users in such analysis by building a spatiotextual and spatiotemporal data visualization based on the existing NewsStand architecture. The application is shown to be applicable to tracking the changing geographic prevalence of a disease (e.g., COVID-19) over time.

show abstract

“…To balance the accuracy of parameter selection and computational efficiency, a fast adaptive heat map generation algorithm based on quadtree partitioning that is suitable for big data analysis and visualization has been proposed. (29) However, for small sample data for urban flood control, the drawback of heat maps is their high computational complexity.…”

Section: Heat Map Generation Algorithm For Spatial Interpolationmentioning

confidence: 99%

Urban Flood Visualization Framework Based on Spatial Grid

Wei¹,

Jing²,

Wang³

et al. 2021

Sensors and Materials

View full text Add to dashboard Cite

To overcome the low accuracy of visual perception caused by the small sample size and spatial heterogeneity of urban flood control data resulting from the use of rainfall gauge sensors, an urban flood visual framework based on a spatial grid was proposed. The framework is an aggregation framework composed of multiple submodels and algorithms. A three-level urban flood control grid based on the territorial management business model was designed for a local administrative bureau. A grid-constrained point data spatial clustering algorithm based on this grid division was proposed to solve the statistical bias problem. An algorithm that increases the number of samples was developed to support the adaptive covering heat map generation. The new algorithm can provide dense sensing information with only a small number of sensors. This framework was tested by an urban flood control business. The results demonstrate that the visual models and algorithms included in this framework eliminate the effects of spatial heterogeneity, solve the statistical bias problem, and improve the visual perception accuracy. The visualization framework is expected to be very helpful for the emergency response and decision making in urban flood control, and can also be applied to other fields such as water conservation and urban management.

show abstract

A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation

Cited by 34 publications

References 59 publications

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

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

Visualizing SpatioTemporal Keyword Trends in Online News Articles

Urban Flood Visualization Framework Based on Spatial Grid

Contact Info

Product

Resources

About