Statistical analysis of spatial point patterns on deep seismic reflection data: a preliminary test

Vasudevan, K.; Eckel, Stefanie; Fleischer, Frank; Schmidt, Volker; Cook, Frederick A.

doi:10.1111/j.1365-246x.2007.03572.x

Cited by 14 publications

(5 citation statements)

References 38 publications

(74 reference statements)

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“…The outcomes of this investigation align with the findings of previous studies conducted (El-Shaarawi & Piegorsch, 2002;Llambi et al, 2004;Vasudevan et al, 2007;Bust, 2008;Smith & Brown, 2009;;O'Sullivan & Unwin, 2010d, 2010a, 2010b, 2010cDixon et al, 2011Diggle et al, 2013;Smith et al, 2015;Smith, 2015;Sadahiro, 2016, Müller et al, 2016González et al, 2016;De Paor et al, 2017;Bilbao et al, 2019;Heyne & Fhoteringham, 2020;Sadahiro & Liu, 2020;Elrahman Yassien et al, 2022;González & Moraga, 2023;Diggle, 2023;Soltisz et al, 2024;Sadahiro & Matsumoto, 2024).…”

Section: Discussionsupporting

confidence: 87%

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

Sa’i,

Aleem,

Musa

et al. 2024

Preprint

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Climate change monitoring and analysis is a critical aspect that involves the consideration of both spatial and temporal dimensions. Improved spatial distribution of the Global Navigation Satellite System (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS Radio Occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley's F, G, K, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran's P-value of 0.001 and a Moran's I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide, Hence, this study suggests the utilization of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution.

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Section: Discussionsupporting

confidence: 87%

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

Sa’i,

Aleem,

Musa

et al. 2024

Preprint

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“…Colony size was set to 50 with the standard deviation varying from 0.01 to 0.5 based on microscopic observations and approximations. Finally, research and our CFU determinations suggest that bacteria are dispersed following a Poisson distribution [25]. Assuming that each cluster is independent and random, the distribution is given by:

f (x, normalλ) = \frac{λ^{x} e^{- λ}}{x!}

…”

Section: Methodsmentioning

confidence: 99%

Injection site microflora in persons with diabetes: why needle reuse is not associated with increased infections?

Wareham-Mathiassen

Bay

Glenting

et al. 2022

APMIS

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SofiaWareham-Mathiassen, Bay L, Glenting VP, Fatima N, Bengtsson H, Bjarnsholt T. Injection site microflora in persons with diabetes: why needle reuse is not associated with increased infections?. APMIS. 2022; 130: 404-416. Needle reuse is a common practice and primary cause of customer compliance issues such as pain, bruising, clogging, injection site reactions (ISR), and associated lipodystrophy. This study aimed to characterize skin microflora at injection sites and establish microbial contamination of used pen injectors and needles. The second objective was to evaluate the risk of infections during typical and repeated subcutaneous injections. 50 participants with diabetes and 50 controls (n = 100) were sampled through tape strips and skin swabs on the abdomen and thigh for skin microflora. Used pen injectors and needles were collected after in-home use and from the hospital after drug administration by health care professionals (HCPs). Samples were analyzed by conventional culture, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF), mass spectrometry (MS), confocal laser scanning microscopy (CLSM), and 16S/ITS high throughput sequencing (HTS). A mathematical model simulated the risk of needle contamination during injections. Injection site populations were in 10 2 cells/cm 2 order, with increased viable bacteria and anaerobic bacteria on the skin in persons with diabetes (p = 0.05). Interpersonal variation dominated other factors such as sex or location. A higher prevalence of Staphylococcus aureus on abdominal skin was found in persons with diabetes than control skin (p ≤ 0.05). Most needles and cartridges (95% and 86%) contained no biological signal. The location of the device collection (hospital vs home-use) and use regimen did not affect contamination. CLSM revealed scarcely populated skin microflora scattered in aggregates, diplo, or single cells. Our mathematical model demonstrated that penetrating bacteria colonies during subcutaneous injection is unlikely. These findings clarify the lack of documented skin infections from subcutaneous insulin injections in research. Furthermore, these results can motivate the innovation and development of durable, reusable injection systems with pharmacoeconomic value and a simplified and enhanced user experience for patients.

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“…Historically, object analysis using RPP was first applied in disciplines where the object could have been located on the map: astronomy, seismology, economics, sociology, materials science, etc. 37 , 38 . Therefore, over the last two decades, SPPA has become increasingly popular in ecological and other research 39 .…”

Section: Background and Related Workmentioning

confidence: 99%

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis

Kosarevych

Lutsyk

Rusyn

et al. 2022

Sci Rep

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Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patterns generation by classifying remote sensing images using convolutional neural network. To increase the accuracy, the training samples are extended by the suggested data augmentation scheme based on the similarities of images within the same part of the landscape for a limited observation time. The image patches are classified in accordance with the labels of previously classified images of the manually prepared training and test samples. This approach has improved the accuracy of image classification by 7% compared to current best practices of data augmentation. A set of image patch centers of a particular class is considered as a random point configuration, while the class labels are used as marks for every point. A marked point pattern is regarded as a combination of several subpoint patterns with the same qualitative marks. We analyze the bivariate point pattern to identify the relationships between points of different types using the features of a marked random point pattern.

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Statistical analysis of spatial point patterns on deep seismic reflection data: a preliminary test

Cited by 14 publications

References 38 publications

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

Injection site microflora in persons with diabetes: why needle reuse is not associated with increased infections?

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis

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