scholarly journals Spatio-temporal point pattern analysis on Wenchuan strong earthquake

2009 ◽  
Vol 22 (3) ◽  
pp. 231-237
Author(s):  
Peijian Shi ◽  
Jie Liu ◽  
Zhen Yang
2007 ◽  
Vol 8 (1) ◽  
pp. 65
Author(s):  
Paul D. Esker ◽  
Karen S. Gibb ◽  
Philip M. Dixon ◽  
Forrest W. Nutter

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.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariem Ben-Said

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.


Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas

1984 ◽  
Vol 30 (106) ◽  
pp. 302-307
Author(s):  
B. N. Boots ◽  
R. K. Burns

AbstractResearchers have analyzed various properties of drumlins within individual drumlin fields in order to provide evidence to help in identifying the processes involved in drumlin formation. One property which has been examined is the spatial distribution of drumlins within a field. Traditionally, in such endeavours the individual drumlins have been represented as points and their distribution examined using techniques of point-pattern analysis. We suggest that not only is such a representation inappropriate at this scale, it also introduces statistical bias which makes the results of such analyses questionable. Consequently, we propose an alternative approach which involves representing individual drumlins as areal phenomena and considering their pattern as a two-phase mosaic. The advantages of such an approach are discussed and it is illustrated by applying it to two different drumlin fields.


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