Univariate Spatial Model Research Articles

Estimating LOCP cancer mortality rates in small domains in Spain using its relationship with lung cancer

The distribution of lip, oral cavity, and pharynx (LOCP) cancer mortality rates in small domains (defined as the combination of province, age group, and gender) remains unknown in Spain. As many of the LOCP risk factors are preventable, specific prevention programmes could be implemented but this requires a clear specification of the target population. This paper provides an in-depth description of LOCP mortality rates by province, age group and gender, giving a complete overview of the disease. This study also presents a methodological challenge. As the number of LOCP cancer cases in small domains (province, age groups and gender) is scarce, univariate spatial models do not provide reliable results or are even impossible to fit. In view of the close link between LOCP and lung cancer, we consider analyzing them jointly by using shared component models. These models allow information-borrowing among diseases, ultimately providing the analysis of cancer sites with few cases at a very disaggregated level. Results show that males have higher mortality rates than females and these rates increase with age. Regions located in the north of Spain show the highest LOCP cancer mortality rates.

Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework

In modern spatial statistics, the structure of data has become more heterogeneous. Depending on the types of spatial data, different modeling strategies are used. For example, kriging approaches for geostatistical data; Gaussian Markov random field models for lattice data; or log Gaussian Cox process models for point-pattern data. Despite these different modeling choices, the nature of underlying data-generating (latent) processes is often the same, which can be represented by some continuous spatial surfaces. A unifying framework is introduced for process-based multivariate spatial fusion models. The framework can jointly analyze all three aforementioned types of spatial data or any combinations thereof. Moreover, the framework accommodates different likelihoods for geostatistical and lattice data. It is shown that some established approaches, such as linear models of coregionalization, can be viewed as special cases of the proposed framework. A flexible and scalable implementation using R-INLA is provided. Simulation studies confirm that the prediction of latent processes improves as one moves from univariate spatial models to multivariate spatial fusion models. The framework is illustrated via a case study using datasets from a cross-sectional study linked with a national cohort in Switzerland. The differences in underlying spatial risks between respiratory disease and lung cancer are examined in the case study.

Developing a grouped random parameters multivariate spatial model to explore zonal effects for segment and intersection crash modeling

It is acknowledged that crash occurrence on segments and intersections could be affected by multilevel factors. Omission of important explanatory variables could result in biased and inconsistent parameter estimates. This paper contributes to the literature by examining the zonal effects which are always excluded or ignored in traffic safety research for segments and intersections. A grouped random parameters multivariate spatial model is proposed to identify both observable zonal effects and unobserved heterogeneity at the zonal level by considering the heterogeneous and spatial correlations. The proposed model is evaluated by comparing it with its three counterparts: a fixed parameters univariate spatial model without zonal factors, a random parameters univariate spatial model without zonal factors, and a random parameters univariate spatial model with zonal factors. The results indicate that the three random parameters models could consistently provide better performance than the fixed parameters model and the models including zonal factors outperform the models without zonal factors. Besides, the proposed model has the optimal model performance compared with its counterparts, which validates the concept of adopting the multivariate modeling framework to identify the heterogeneous and spatial correlations of zonal effects. The results confirm the significantly correlated heterogeneous residuals in modeling zonal factors on crash occurrence on segments and intersections. However, the spatial correlations of zonal effects on different types of road entities (segments and intersections) in adjacent zones are not statistically significant. Furthermore, the proposed model provides more valuable insights about the crash occurrence on segments and intersections by revealing segment-/intersection-level factors together with zonal factors.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya

BackgroundPrecise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors.MethodsA theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test.ResultsThe best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R2 = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values.ConclusionOur study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

A multivariate spatial model of crash frequency by transportation modes for urban intersections

This study proposes a multivariate spatial model to simultaneously analyze the occurrence of motor vehicle, bicycle and pedestrian crashes at urban intersections. The proposed model can account for both the correlation among different modes involved in crashes at individual intersections and spatial correlation between adjacent intersections. According to the results of the model comparison, multivariate spatial model outperforms the univariate spatial model and the multivariate model in the goodness-of-fit. The results confirm the highly correlated heterogeneous residuals in modeling crash risk among motor vehicles, bicycles and pedestrians. In regard to spatial correlation, the estimates of variance for spatial correlations of all three crash modes in the multivariate and univariate models are statistically significant; however, the correlations for spatial residuals between different crash modes at adjacent sites are not statistically significant. More interestingly, the results show that the proportion of variation explained by the spatial effects is much higher for motor vehicle crashes than for bicycle and pedestrian crashes, which indicates spatial correlations between adjacent intersections are significantly different between the motor vehicle and non-motorized modes.

Multivariate random parameters collision count data models with spatial heterogeneity

This study investigated the effects of including spatial heterogeneity in multivariate random parameters models and their influence on different collision severity levels. The models were developed for severe (injury and fatal) and no-injury collisions using three years of collision data from the city of Vancouver. Three different modeling formulations were applied to measure the effects of spatial heterogeneity in a multivariate random parameters model. The proposed models were estimated in a Full Bayesian (FB) context using Markov Chain Monte Carlo (MCMC) simulation. The Deviance Information Criteria (DIC) values indicated that all the models were comparable to one another. Therefore, no particular model can be distinctly preferred over others. According to parameter estimates, a variety of traffic and road geometric covariates were found to significantly influence collision severities. The variance for spatial heterogeneity was higher than the variance for heterogeneous effects. The correlation between severe and no-injury collisions for the total random effects (heterogeneous and spatial) was significant and quite high, indicating that higher no-injury collisions are associated with higher severe collisions. These results support the incorporation of spatial heterogeneity in multivariate random parameters models. Furthermore, the multivariate random parameters spatial models were compared with two independent univariate random parameters spatial models with respect to model inference and goodness of fit. The multivariate spatial models outperformed the two univariate spatial models with a very significant drop in the DIC value.

Pemodelan Kerawanan Pangan dan Kemiskinan dengan Geographically Wighted Multivariat Linier Model di Kabupaten Sampang

The problem of food insecurity has long been the focus of attention and is very closely related to the problem of poverty in which the two are interrelated phenomena that have a causal relationship. Food insecurity and poverty is a package that is always the problem faced by the government both central and local government, especially in Sampang. Spatial regression models that have been described in general a univariate spatial model, in which the observations have only one response variable that depends on the location of the observation. Geographically Weighted Multivariat Linier Model a multivariate regression models were used to spatially resolve the influence of spatial heterogeneity caused by differences in the conditions of the location with another location. The purpose of this study was to establish Geographically Weighted Multivariat Linier Model (GWMLM) with a weighted cross – variogram gaussian on the problem of food insecurity and poverty in Sampang. Food insecurity and poverty is a phenomenon of spatial heterogeneity. Based on the 10 sampled villages gained influence the percentage of households without access to electricity (X 1 ), the percentage of main roads are adequate (X 2 ) , the number of health facilities (X 3 ) , and the percentage of malnutrition children (X 4 ) against food insecurity and poverty differently in each location.