China
Africa
Explore chapters and articles related to this topic
Spatial Modelling Concepts for Controlling COVID-19 Risk in Saudi Arabia
Published in Abbas Rajabifard, Greg Foliente, Daniel Paez, COVID-19 Pandemic, Geospatial Information, and Community Resilience, 2021
This GIS-based system can go far beyond the early studies on establishing correlates. So the main purpose can be enhanced to describe the geographical differences in disease occurrence for formulating aetiological hypotheses. Also, the system locates unusual high-risk hotspots to develop a preventive plan. Another purpose is to improve the reliability of disease risk models for allocating resources. Moreover, the system can undertake sophisticated spatial analyses of environmental features and disease rates, together with geostatistical analysis to statistically verify associations [8, 9, 10].
R Graphics and Spatial Health Data
Published in Andrew B. Lawson, Using R for Bayesian Spatial and Spatio-Temporal Health Modeling, 2021
Geostatistical modeling has as its main focus the estimation of continuous spatial fields. As such it is most appropriately used for situations where distance between locations (points) is a fundamental feature. Networks of monitoring sites used for (say) air pollution measurement or geological sampling based on well logs are clear candidates for geostatistical estimation methods. On the other hand health outcomes are usually discrete and based on the experience of individuals. Hence even when residential locations are known for incident cases of disease it is not clear why disease risk should be treated as a continuous process. In the case where an environmental insult is assumed to affect disease risk, then there could be a case made for the use of geostatistical methods. However, in general, this is not the case, and so the discrete nature of risk should be addressed.
Spatial Models
Published in Virgilio Gómez-Rubio, Bayesian Inference with INLA, 2020
Spatial statistics is traditionally divided into three main areas depending on the type of problem and data: lattice data, geostatistics and point patterns (Cressie, 2015). Sometimes, spatial data is also measured over time and spatio-temporal models can be proposed (Cressie and Wikle, 2011). In the next sections models for the different types of spatial data will be considered. In Section 8.6 models for spatio-temporal data will be described. Blangiardo and Cameletti (2015) and Krainski et al. (2018) provide a thorough description of most of the models described in this section. Bivand et al. (2013) and Lovelace et al. (2019) provide general description on handling spatial data in R and are recommended reads.
Competing risks model for clustered data based on the subdistribution hazards with spatial random effects
Published in Journal of Applied Statistics, 2022
Somayeh Momenyan, Farzane Ahmadi, Jalal Poorolajal
If individuals come from different regions, there will be a spatial correlation between survival data because the geographically closer regions usually are the same or similar in terms of the environmental and social factors, therefore, data from the same or nearer regions are likely to be more similar than those from farther regions. Ignoring this spatial correlation results in biased estimates and misleading inferences. Moreover, the spatial survival analysis by mapping the spatial distribution could identify some of the geographical inequalities that exist in survival and find places or populations that require public health improvements. Spatial models are grouped into three categories according to the data structure: point-referenced (geostatistical) data, where the exact geographic locations (e.g. latitude and longitude) are used, areal (lattice) data, where the region of study is divided into a number of areal units with well-defined boundaries and the positions of the units relative to each other are used (e.g. which units neighbor which others), and point pattern data, where the response is often fixed, and only the locations are assumed as random [3].
Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains
Published in Journal of Applied Statistics, 2021
Nedka Dechkova Nikiforova, Rossella Berni, Gabriele Arcidiacono, Luciano Cantone, Pierpaolo Placidoli
The concept of simulated designs, introduced in the seminal contribution to computer experiments [35], is substantially different from physical and classical experimental designs [9]; in fact, the observation is predicted according to a simulated model of the process under study, in order to analyse the deterministic relation between input and output variables. To this end, specific metamodels are used that act as statistical interpolators of the simulated input-output data. One of the most appropriate and widely used metamodels is Kriging that was originally developed for modelling spatial data in the geostatistical field [22,27]. Afterwards, since the seminal contribution of Sacks et al. [35], the Kriging methodology has been further developed for the analysis of computer experiments. In this brief Section, we deal with Kriging's basic theory; for further details see [32,33,37].
Measurement and mapping of the electromagnetic radiation in the urban environment
Published in Electromagnetic Biology and Medicine, 2020
Jin Liu, Minhong Wei, Huafang Li, Xin Wang, Xiaoyu Wang, Song Shi
Since measurement results can hardly be sufficient to cover every corner of the region, spatial interpolation is applied to predict the value of a random field at an unobserved location from observations of its value at nearby locations. Kriging interpolation is a kind of interpolation method based on geostatistics. Kriging method is the best geostatistical linear-unbiased estimator that yields a zero mean residual error and minimizes the error variance. We interpolate the electric field levels at non-sampled points using the ordinary kriging method. We can estimate the value of an unknown point