Spatio-Temporal Areal Unit Modeling in <i>R</i> with Conditional Autoregressive Priors Using the <b>CARBayesST</b> Package

Lee, Duncan; Rushworth, Alastair; Napier, Gary

doi:10.18637/jss.v084.i09

Cited by 114 publications

(111 citation statements)

References 38 publications

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“…Additionally, this analysis should be extended to include available data for previous years between 2010 and 2014, fitting some spatio-temporal models (Cressie and Wikle 2011) under an econometric approach and developing and implementing the temporal effects on Bayesian hierarchical models (Banerjee et al 2004;Lee et al 2015), or on Bayesian autoregressive models (Blangiardo and Cameletti 2015), for count data, trending towards a spatio-temporal Bayesian econometric approach for processing count data. It is expected that analyses like the one considered here for the LS24 data contribute, in general, to the improvement of management policies in several areas of activity, the hospital domain in this case, or in others such as education or road safety.…”

Section: Discussionmentioning

confidence: 99%

A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

et al. 2017

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Abstract:The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health services. This study aims to describe and evaluate the use of LS24. Since for LS24 data, the location attribute is an important source of information to describe its use, this study analyses the number of calls received, at a municipal level, under two different spatial econometric approaches. This analysis is important for future development of decision support indicators in a hospital context, based on the economic impact of the use of this health line. Considering the discrete nature of data, the number of calls to LS24 in each municipality is better modelled by a Poisson model, with some possible covariates: demographic, socio-economic information, characteristics of the Portuguese health system and development indicators. In order to explain model spatial variability, the data autocorrelation can be explained in a Bayesian setting through different hierarchical log-Poisson regression models. A different approach uses an autoregressive methodology, also for count data. A log-Poisson model with a spatial lag autocorrelation component is further considered, better framed under a Bayesian paradigm. With this empirical study we find strong evidence for a spatial structure in the data and obtain similar conclusions with both perspectives of the analysis. This supports the view that the addition of a spatial structure to the model improves estimation, even in the case where some relevant covariates have been included.

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Section: Discussionmentioning

confidence: 99%

A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

et al. 2017

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“…3, the terms P s , D t and G st denote purely spatial, purely temporal and residual spatio-temporal components of variation in risk, respectively. Following [44] and [45] it is assumed that the G st are mutually independent, G st ∼ N (0, τ 2 I ) , and that the spatial random effect, P = (P 1 , . .…”

Section: Model Framework For the Malawi Malaria Datamentioning

confidence: 99%

“…Specifically, the model defines spatial neighbourhood relationships through a symmetric m × m matrix W with elements w ij = 1 if the spatial units i and j are neighbours, and w ij = 0 otherwise; i and j are specified to be neighbours if they share a common boundary. Similarly, temporal neighbourhood relationships are defined by a symmetric n × n matrix V ; following [45], v ij = 1 if |j − i| = 1 and v ij = 0 otherwise. Now, writing P −s for the (m − 1) element vector obtained by removing the sth element from P, and similarly D −t for the (n − 1)-element vector obtained by removing the t-th element from D the model can be defined through its full conditional distributions, Both the P s and D t are mean-centred such that m s=1 P s = n t=1 D t = 0 The following diffuse prior specifications for the fixed effect parameters β and the random effect parameters…”

Section: Model Framework For the Malawi Malaria Datamentioning

confidence: 99%

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Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

Chirombo

Ceccato²,

Lowe

et al. 2020

Malar J

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Background: Malaria transmission is influenced by a complex interplay of factors including climate, socioeconomic , environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. Methods: Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socioeconomic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with districtlevel variation in clinical malaria rates in Malawi, and visualized using maps. Results: Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. Conclusion: The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

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“…Furthermore, few studies explicitly took into account spatial correlation and temporal dynamics simultaneously when modeling urbanization and pollution data under study. However, it is a well-known fact that ignoring spatio-temporal correlations could lead to unreliable statistical inferences on relationships between covariates under key research interest [16][17][18]. The distributions of urbanization and environmental pollution were rarely uniform or even over space.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province

Zhao

Dong

2020

IJERPH

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Urbanization processes at both global and regional scales are taking place at an unprecedent pace, leading to more than half of the global population living in urbanized areas. This process could exert grand challenges on the human living environment. With the proliferation of remote sensing and satellite data being used in social and environmental studies, fine spatial- and temporal-resolution measures of urban expansion and environmental quality are increasingly available. This, in turn, offers great opportunities to uncover the potential environmental impacts of fast urban expansion. This paper investigated the relationship between urban expansion and pollutant emissions in the Fujian province of China by building a Bayesian spatio-temporal autoregressive model. It drew upon recently compiled pollutant emission data with fine spatio-temporal resolution, long temporal coverage, and multiple sources of remote sensing data. Our results suggest that there was a significant relationship between urban expansion and pollution emission intensity—urban expansion significantly elevated the PM2.5 and NOx emissions intensity in Fujian province during 1995–2015. This finding was robust to different measures of urban expansion and retained after controlling for potential confounding effects. The temporal evolution of pollutant emissions, net of covariate effects, presented a fluctuation pattern rather than a consistent trend of increasing or decreasing. Spatial variability of the pollutant emissions intensity among counties was, however, decreasing steadily with time.

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Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package

Cited by 114 publications

References 38 publications

A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province

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