scholarly journals Evaluating health facility access using Bayesian spatial models and location analysis methods

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0218310 ◽  
Author(s):  
Nicholas J. Tierney ◽  
Antonietta Mira ◽  
H. Jost Reinhold ◽  
Giuseppe Arbia ◽  
Samuel Clifford ◽  
...  
Keyword(s):  
Health Facility ◽  
Spatial Models ◽  
Location Analysis ◽  
Analysis Methods ◽  
Bayesian Spatial Models

scholarly journals Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0233019 ◽  
Author(s):  
Earl W. Duncan ◽  
Kerrie L. Mengersen
Keyword(s):  
Spatial Models ◽  
Bayesian Spatial Models

scholarly journals Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Olena Oliveira ◽  
Ana Isabel Ribeiro ◽  
Elias Teixeira Krainski ◽  
Teresa Rito ◽  
Raquel Duarte ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
High Risk ◽  
Control Strategies ◽  
Spatial Models ◽  
Multidrug Resistant ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Mdr Tb ◽  
Risk Areas ◽  
Bayesian Spatial Models

Abstract Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country’s average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (ρ = 0.653; 95% CI 0.457–0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.

scholarly journals Bayesian spatial models with a mixture neighborhood structure

2012 ◽  
Vol 109 ◽  
pp. 88-102 ◽  
Author(s):  
E.C. Rodrigues ◽  
R. Assunção
Keyword(s):  
Spatial Models ◽  
Neighborhood Structure ◽  
Bayesian Spatial Models

Analysis of deforestation and protected area effectiveness in Indonesia: A comparison of Bayesian spatial models

2015 ◽  
Vol 31 ◽  
pp. 285-295 ◽  
Author(s):  
Cyrille Brun ◽  
Alex R. Cook ◽  
Janice Ser Huay Lee ◽  
Serge A. Wich ◽  
Lian Pin Koh ◽  
...  
Keyword(s):  
Protected Area ◽  
Spatial Models ◽  
Bayesian Spatial Models

scholarly journals Geographical inequalities in energy poverty in a Mediterranean city: Using small-area Bayesian spatial models

2022 ◽  
Vol 8 ◽  
pp. 1249-1259
Author(s):  
Marc Marí-Dell’Olmo ◽  
Laura Oliveras ◽  
Carlos Vergara-Hernández ◽  
Lucia Artazcoz ◽  
Carme Borrell ◽  
...  
Keyword(s):  
Small Area ◽  
Spatial Models ◽  
Energy Poverty ◽  
Geographical Inequalities ◽  
Bayesian Spatial Models ◽  
Mediterranean City

scholarly journals Association between network characteristics and bicycle ridership across a large metropolitan region

2021 ◽  
Author(s):  
Ben Beck ◽  
Christopher Pettit ◽  
Meghan Winters ◽  
Trisalyn Nelson ◽  
Hai Vu ◽  
...  
Keyword(s):  
Spatial Models ◽  
Monitoring And Evaluation ◽  
Metropolitan Region ◽  
Metropolitan Regions ◽  
Network Characteristics ◽  
Household Travel Survey ◽  
Bicycle Infrastructure ◽  
Household Travel ◽  
Bayesian Spatial Models ◽  
Network Metrics

Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

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