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

2020 ◽  
Vol 105 ◽  
pp. 96-118 ◽  
Author(s):  
Yuan Wang ◽  
Zhipeng Gui ◽  
Huayi Wu ◽  
Dehua Peng ◽  
Jinghang Wu ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Space Time ◽  
Apache Spark ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
K Function

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

An Application of Space-Time Analysis to Improve the Epidemiological Understanding of the Papaya-Papaya Yellow Crinkle Pathosystem

2007 ◽  
Vol 8 (1) ◽  
pp. 65
Author(s):  
Paul D. Esker ◽  
Karen S. Gibb ◽  
Philip M. Dixon ◽  
Forrest W. Nutter
Keyword(s):  
Strong Evidence ◽  
Pattern Analysis ◽  
Space Time ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Incidence Data ◽  
Time Interaction ◽  
Spatio Temporal ◽  
Time Point ◽  
Contagion Process

Yellow crinkle disease of papaya is a serious threat to papaya production in Australia. Space-time point pattern analysis was used to study the spatial and temporal dependence of two phytoplasma strains that cause yellow crinkle: tomato big bud (TBB) and sweet potato little leaf V4 (SPLL-V4). Incidence data for both phytoplasma strains were obtained from a field study conducted in Katherine, NT, Australia, between January 1996 and May 1999. The primary ecological and epidemiological question of interest was to elucidate the scale of spatial or spatio-temporal aggregation of phytoplasma-infected papaya plants. The hypothesis was that there would be a contagion process, where TBB- and SPLL-V4-infected papaya would be aggregated and not random. To test this hypothesis, a point pattern spatial analysis using Monte Carlo simulation was initially applied to the incidence data. Results of this analysis suggested that SPLL-V4 infected papaya plants displayed aggregation with spatial dependence up to 30 m (10 to 15 plants along or across rows), whereas there was not strong evidence to suggest that TBB-infected papaya plants were aggregated. However, when a space-time point pattern analysis was subsequently used to simultaneously test for the interaction between space and time, there was strong evidence (P < 0.01 for SPLL-V4 and P < 0.10 for TBB) to suggest a space-time interaction for both SPLL-V4 and TBB. For SPLL-V4, a space-time risk window of approximately 10 months and 20 m was detected, whereas for TBB, this risk window was 5 months and 10 m. The results of these studies support the hypothesis that papaya infection by both phytoplasma strains appears to be the result of a contagion process, providing support for the contention that insect vectors are the most likely mechanism for acquisition, dispersal, and transmission. Accepted for publication 26 April 2007. Published 26 July 2007.

POINT PATTERN ANALYSIS OF THE SPATIAL PROXIMITY OF RESIDENCES PRIOR TO DIAGNOSIS OF PERSONS WITH HODGKIN'S DISEASE

1990 ◽  
Vol 132 (supp1) ◽  
pp. 53-62 ◽  
Author(s):  
ANDREW ROSS ◽  
SCOTT DAVIS
Keyword(s):  
Hodgkin's Disease ◽  
Pattern Analysis ◽  
Hodgkin’S Disease ◽  
A Priori ◽  
Population Based ◽  
Space Time ◽  
Point Pattern Analysis ◽  
Spatial Proximity ◽  
Point Pattern ◽  
Incident Cases

Abstract There is some evidence to suggest that the etiology of Hodgkin's disease includes an infectious component One approach to investigating this possibility is a formal assessment of the geographic and temporal variation in the incidence of the disease. The present study was designed to better evaluate space-time patterns of residence prior to diagnosis of persons with Hodgkln's disease. Lifetime residential histories were obtained from 279 incident cases of Hodgkin's disease diagnosed between 1974 and 1982 in a defined population in northwestern Washington State, and a similar number of population controls matched to the cases by age, sex, and socioeconomic status. A method of point pattern analysis was modified for use in this context to compare the spatial proximity of case residences to that expected under the null assumption of no difference between the spatial pattern of case and control residences. Analyses were applied within temporal groupings of residences specified a priori assuming 1) no specified latency, 2) four different 5-year latent intervals, and 3) three age at exposure intervals. Assuming no latency, there is no evidence that case residences are more or less clustered than would be expected by chance. Assuming latent intervals of up to 15 years prior to diagnosis, case residences are less clustered than expected, particularly among those who developed Hodgkin's disease after the age of 40 years. In contrast, there is suggestive evidence that persons diagnosed with Hodgkin's disease after age 40 years lived closer together than expected as young children and teenagers. These results illustrate the need to focus such analyses on specific time intervals defined a priori to most likely represent periods of greatest etiologic relevance. To the extent that these findings are not the result of some unknown artifact of the method itself, they may serve to focus additional attention on childhood environment as a particularly important period in the etiology of this disease. Furthermore, the analytic method employed may be useful in identifying space-time clustering using population-based data in other etiologic settings

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

scholarly journals DEVELOPING APACHE SPARK BASED RIPLEY’S K FUNCTIONS FOR ACCELERATING SPATIOTEMPORAL POINT PATTERN ANALYSIS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 545-552 ◽  
Author(s):  
Z. Gui ◽  
Y. Wang ◽  
Z. Cui ◽  
D. Peng ◽  
J. Wu ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Visual Analytics ◽  
Hash Table ◽  
Space Time ◽  
Apache Spark ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Time Dimension ◽  
Ripley’S K ◽  
Ripley's K

Abstract. Ripley’s K functions are powerful tools for studying the spatial arrangement or spatiotemporal distribution characteristics of geographic phenomena and events in spatial analysis and has been used in many fields. However, the K functions are compute-intensive for point-wise distance comparisons, edge correction and simulations for significance test. Although parallel computing technologies have been adopted to accelerate K functions, previous works haven’t extended the optimization from space to space-time dimension. This study presents an acceleration method for K functions upon state-of-the-art distributed computing framework Apache Spark, and four optimization strategies are leveraged to simplify calculation procedures and accelerate distributed computing respectively, including 1) spatiotemporal indexing based on R-tree with Sort-Tile-Recursive (STR) algorithm for reducing distance comparison when retrieving potential spatiotemporally neighbouring points; 2) Hash-Table-based caching for spatiotemporal edge correction weights reuse and reducing repetitive computation; 3) Spatiotemporal partitioning using KDB-tree as well as cylinder intersection redundancy strategy for decreasing ghost buffer redundancy in partitions and supporting near-balanced distributed processing; 4) Customized serialization of spatiotemporal objects and indexes for lowering the overhead of data transmission. Experiments verify the effectiveness and time efficiency of the proposed optimization strategies, and also evaluate the overall performance and scalability. Based on the proposed methods, a web-based visual analytics framework has been developed and publicly shared through GitHub, and four types of the distributed K functions are implemented, including space, space-time, local and cross K functions, which demonstrates its value on promoting geographical and socioeconomic studies.

scholarly journals Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariem Ben-Said
Keyword(s):  
Spatial Patterns ◽  
Pattern Analysis ◽  
Biotic Interactions ◽  
Plant Ecology ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Summary Statistics ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis

Abstract Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

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