Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

Xu, Wenzhao; Collingsworth, Paris D.; Bailey, Barbara; Mazur, Martha L. Carlson; Schaeffer, Jeff; Minsker, Barbara

doi:10.1016/j.envsoft.2017.06.049

Cited by 9 publications

(4 citation statements)

References 38 publications

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“…Adopt the practices of six articles that Stagge et al (2019) awarded badges to for full and partial reproducible results. For example, these papers • Provided all model and code in a Github (Buscombe 2017;Neuwirth 2017;Xu et al 2017), institutional (Yu et al 2017), or HydroShare (Horsburgh et al 2017) repository. • Had an easy-to-find README file that explained the contents and gave directions to setup and run code (Buscombe 2017;Horsburgh et al 2017;Neuwirth 2017;Xu et al 2017).…”

Section: Verify Your Results Are Reproduciblementioning

confidence: 99%

The Next Frontier: Making Research More Reproducible

Rosenberg

Filion

Teasley

et al. 2020

J. Water Resour. Plann. Manage.

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(NSF and IES 2018) describe reproducibility as a continuum (Fig. 1). The goal is to push work up the continuum to make data, models, code, directions, and other digital artifacts used in the research available for others to reuse (availability). Then, use shared artifacts to exactly reproduce published results (reproducibility, sometimes called bit or computational reproducibility). Finally, use artifacts with existing and new data sets to replicate findings across sites or domains (replicability). For example, the Journal of Water Resources Planning and Management policy to specify the availability of data, models, and code (Rosenberg and Watkins 2018) primarily targets availability in the reproducibility continuum. This Fig. 1. Reproducibility is a continuum.

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Section: Verify Your Results Are Reproduciblementioning

confidence: 99%

The Next Frontier: Making Research More Reproducible

Rosenberg

Filion

Teasley

et al. 2020

J. Water Resour. Plann. Manage.

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“…Geostatistics, which is rooted in the theory of regionalized variables, provides a robust framework for analyzing spatial heterogeneity and correlations in natural phenomena [31]. Its application in ecological research has proven effective in describing spatial data and quantifying uncertainties [32][33][34][35]. To investigate the spatial characteristics of the analyzed data [36], this study employs a geographically weighted regression (GWR) tool to elucidate the spatial patterns of habitat quality and its associated variables.…”

Section: Introductionmentioning

confidence: 99%

Habitat Quality Assessment under the Change of Vegetation Coverage in the Tumen River Cross-Border Basin

et al. 2023

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The continuous deterioration of terrestrial ecosystems has led to the destruction of many biological habitats in recent years. The Tumen River cross-border basin, an important biological habitat, is also affected by this changing situation. Assessing habitat quality (HQ) is crucial for restoring and protecting habitats, and vegetation plays a significant role in this process. In this study, we used geographical detector (GD) to extract fraction vegetation coverage (FVC) features and quantify the contribution of driving factors. By coupling vegetation cover and land use data, we assessed HQ. Our findings reveal a declining trend in FVC from 2000 to 2020, which mainly assumed a spatial pattern inclined from northeast and southwest to southeast. Human activities and natural factors interacted to cause these changes in FVC, with human activities having a more significant impact. Vegetation and land use changes led to a decline in the basin’s HQ index. This study highlights the crucial role of FVC in HQ and provides a relevant scientific reference for optimizing the evaluation of HQ in the Tumen River cross-border basin and promoting the sustainable development of regional ecology.

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“…It has been developed with "shiny", a package to build interactive web applications in the R software environment. The use of this package is becoming very popular nowadays and many different environmental modelling applications are being developed [23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Online relative risks/rates estimation in spatial and spatio-temporal disease mapping

Adin

Goicoa

Ugarte

2019

Computer Methods and Programs in Biomedicine

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Background and objective: Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatiotemporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTC-Dapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. Methods: The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. Results: The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical

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Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis

Cited by 9 publications

References 38 publications

The Next Frontier: Making Research More Reproducible

The Next Frontier: Making Research More Reproducible

Habitat Quality Assessment under the Change of Vegetation Coverage in the Tumen River Cross-Border Basin

Online relative risks/rates estimation in spatial and spatio-temporal disease mapping

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