Hierarchical Spatial Models

2011 ◽  
Keyword(s):  
Spatial Models ◽  
Hierarchical Spatial Models

scholarly journals Hierarchical spatial models for predicting tree species assemblages across large domains

2009 ◽  
Vol 3 (3) ◽  
pp. 1052-1079 ◽  
Author(s):  
Andrew O. Finley ◽  
Sudipto Banerjee ◽  
Ronald E. McRoberts
Keyword(s):  
Tree Species ◽  
Spatial Models ◽  
Species Assemblages ◽  
Hierarchical Spatial Models ◽  
Large Domains

Development of Hierarchical Spatial Models for Assessing Ungulate Abundance and Habitat Relationships

2020 ◽  
pp. 35-82
Author(s):  
N. Samba Kumar ◽  
K. Ullas Karanth ◽  
James D. Nichols ◽  
Srinivas Vaidyanathan ◽  
Beth Gardner ◽  
...  
Keyword(s):  
Spatial Models ◽  
Hierarchical Spatial Models

HIERARCHICAL SPATIAL MODELS OF ABUNDANCE AND OCCURRENCE FROM IMPERFECT SURVEY DATA

2007 ◽  
Vol 77 (3) ◽  
pp. 465-481 ◽  
Author(s):  
J. Andrew Royle ◽  
Marc Kéry ◽  
Roland Gautier ◽  
Hans Schmid
Keyword(s):  
Survey Data ◽  
Spatial Models ◽  
Hierarchical Spatial Models

scholarly journals Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan

2019 ◽  
Vol 31 ◽  
pp. 100301 ◽  
Author(s):  
Mitzi Morris ◽  
Katherine Wheeler-Martin ◽  
Dan Simpson ◽  
Stephen J. Mooney ◽  
Andrew Gelman ◽  
...  
Keyword(s):  
Spatial Models ◽  
Bayesian Hierarchical ◽  
Hierarchical Spatial Models

scholarly journals Predicting species distribution from fishers’ local ecological knowledge: a new alternative for data-poor management

2019 ◽  
Vol 76 (8) ◽  
pp. 1423-1431 ◽  
Author(s):  
Priscila F.M. Lopes ◽  
Júlia T. Verba ◽  
Alpina Begossi ◽  
Maria Grazia Pennino
Keyword(s):  
Local Ecological Knowledge ◽  
Scientific Information ◽  
Spatial Models ◽  
Marine Spatial Planning ◽  
Ecological Knowledge ◽  
Species Occurrence ◽  
Southwest Atlantic ◽  
Bayesian Hierarchical ◽  
Dusky Grouper ◽  
Hierarchical Spatial Models

Many developing countries lack information to manage their endangered species, urging the need for affordable and reliable information. We used Bayesian hierarchical spatial models, with oceanographic variables, to predict the distribution range of Epinephelus marginatus, the dusky grouper, for the entire Southwest Atlantic. We ran a model using scientific information gathered from the literature and another using information gathered from fishers on species presence or absence. In both models, temperature was an important determinant of species occurrence. The predicted occurrence of the dusky grouper overlapped widely (Schoener’s D = 0.71; Warren’s I = 0.91) between the models, despite small differences on the southern and northern extremes of the distribution. These results suggest that basic information provided by fishers on species occurrence in their area can be reliable enough to predict species occurrence over large scales and can be potentially useful for marine spatial planning. Fishers’ knowledge may be an even more viable alternative to data collection than what was previously thought, for countries that both struggle with financial limitations and have urgent conservation needs.

Hierarchical Spatial Models

2015 ◽  
pp. 1-10
Author(s):  
Ali Arab ◽  
Mevin B. Hooten ◽  
Christopher K. Wikle
Keyword(s):  
Spatial Models ◽  
Hierarchical Spatial Models

Hierarchical Spatial Models

2017 ◽  
pp. 837-846 ◽  
Author(s):  
Ali Arab ◽  
Mevin B. Hooten ◽  
Christopher K. Wikle
Keyword(s):  
Spatial Models ◽  
Hierarchical Spatial Models

scholarly journals Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

2010 ◽  
Vol 47 (2) ◽  
pp. 401-409 ◽  
Author(s):  
Tammy L. Wilson ◽  
James B. Odei ◽  
Mevin B. Hooten ◽  
Thomas C. Edwards Jr
Keyword(s):  
Relative Abundance ◽  
Spatial Models ◽  
Pygmy Rabbit ◽  
Hierarchical Spatial Models

scholarly journals Association of Population Density and Distance to the City with the Risks of COVID-19: A Bayesian Spatial Analysis

2021 ◽  
Vol 2123 (1) ◽  
pp. 012001
Author(s):  
M A Tiro ◽  
A Aswi ◽  
Z Rais
Keyword(s):  
Relative Risk ◽  
Population Density ◽  
Spatial Models ◽  
Credible Interval ◽  
Information Criteria ◽  
Capital City ◽  
Bayesian Hierarchical ◽  
Inverse Gamma ◽  
Hierarchical Spatial Models ◽  
The City

Abstract The outbreak of Coronavirus disease-2019 (Covid-19) poses a severe threat around the world. Although several studies of modelling Covid-19 cases have been done, there appears to have been limited research into modelling Covid-19 using Bayesian hierarchical spatial models. This study aims to examine the most suitable Bayesian spatial CAR Leroux models in modelling the number of confirmed Covid-19 cases without and with covariates namely distance to the capital city and population density. Data on the number of confirmed positive cases of Covid-19 (March 20, 2020 - August 30, 2021) in 15 sub-districts in Makassar City, the number of populations, population density, and distance to the city are used. The best model selection is based on several criteria, namely Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), residuals from Moran’s I Modification (MMI), and the 95% credible interval does not contain zero. The results showed that the best model in modelling Covid-19 is spatial CAR Leroux with hyperprior Inverse-Gamma (0.5, 0.05) model with the incorporation of distance to the capital city. It is found that there was a negative correlation between the distance to the capital city and Covid-19 risk, but the association between population density and the relative risk of Covid-19 was not statistically significant. Ujung Pandang district and Sangkarrang Island have the highest and the lowest relative risk respectively.

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