Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data [0.03%]
利用空间索引对美国本土湖泊电导率进行大数据空间分析模型化研究
Michael Dumelle,Jay M Ver Hoef,Amalia Handler et al.
Michael Dumelle et al.
Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency's National Lakes Assessment using spatial in...
Spatial aggregation with respect to a population distribution: Impact on inference [0.03%]
基于人口分布的空间聚合及其对推断的影响
John Paige,Geir-Arne Fuglstad,Andrea Riebler et al.
John Paige et al.
Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of agg...
Exploring heterogeneity and dynamics of meteorological influences on US PM2.5: A distributed learning approach with spatiotemporal varying coefficient models [0.03%]
基于时空可变系数模型的分布式学习方法探索美国PM2.5气象影响的异质性和动态变化规律
Lily Wang,Guannan Wang,Annie S Gao
Lily Wang
Particulate matter (PM) has emerged as a primary air quality concern due to its substantial impact on human health. Many recent research works suggest that PM2.5 concentrations depend on meteorological conditions. Enhancing current pollutio...
A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data [0.03%]
一种检测定性区域数据中空间模式的假设检验方法
Stella Self,Xingpei Zhao,Anja Zgodic et al.
Stella Self et al.
The vast growth of spatial datasets in recent decades has fueled the development of many statistical methods for detecting spatial patterns. Two of the most commonly studied spatial patterns are clustering, loosely defined as datapoints wit...
A Hypothesis Test for Detecting Distance-Specific Clustering and Dispersion in Areal Data [0.03%]
一种检测区域数据中特定距离的聚类和离散现象的假设检验方法
Stella Self,Anna Overby,Anja Zgodic et al.
Stella Self et al.
Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley's K-func...
Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach [0.03%]
曼尼托巴省COVID-19数据分析的新方法
Leila Amiri,Mahmoud Torabi,Rob Deardon
Leila Amiri
The basic homogeneous SEIR (susceptible-exposed-infected-removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the ...
Modeling geostatistical incomplete spatially correlated survival data with applications to COVID-19 mortality in Ghana [0.03%]
用于预测新冠疫情死亡率的不完备空间生存数据分析模型及应用——以加纳为例
Prince Addo Allotey,Ofer Harel
Prince Addo Allotey
Survival models which incorporate frailties are common in time-to-event data collected over distinct spatial regions. While incomplete data are unavoidable and a common complication in statistical analysis of spatial survival research, most...
Variograms for kriging and clustering of spatial functional data with phase variation [0.03%]
用于空间函数数据克里金插值和聚类的变差函数(包含相位变化)
Xiaohan Guo,Sebastian Kurtek,Karthik Bharath
Xiaohan Guo
Spatial, amplitude and phase variations in spatial functional data are confounded. Conclusions from the popular functional trace-variogram, which quantifies spatial variation, can be misleading when analyzing misaligned functional data with...
Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting [0.03%]
自适应高斯马尔可夫随机场时空模型在传染病制图和预测中的应用
Ying C MacNab
Ying C MacNab
Recent disease mapping literature presents adaptively parameterized spatiotemporal (ST) autoregressive (AR) or conditional autoregressive (CAR) models for Bayesian prediction of COVID-19 infection risks. These models were motivated to captu...
Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions [0.03%]
使用Richards曲线建模COVID-19病例的时空变化：以意大利各地区为例
Marco Mingione,Pierfrancesco Alaimo Di Loro,Alessio Farcomeni et al.
Marco Mingione et al.
We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concern...