scholarly journals Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model

2019 ◽  
Vol 38 (3) ◽  
pp. 693-710 ◽  
Author(s):  
Clifford Lam ◽  
Pedro C.L. Souza
Keyword(s):  
Weight Matrix ◽  
Spatial Lag ◽  
Spatial Weight Matrix ◽  
Spatial Lag Model ◽  
Spatial Weight ◽  
Lag Model ◽  
Selection Of

scholarly journals Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 12013
Author(s):  
Keqiang Dong ◽  
Liao Guo
Keyword(s):  
Spatial Analysis ◽  
Spatial Correlation ◽  
Lagrange Multiplier ◽  
Weight Matrix ◽  
Primary Data ◽  
Lagrange Multiplier Test ◽  
Spatial Lag ◽  
Spatial Weight Matrix ◽  
The World ◽  
Spatial Weight

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Analiza zróżnicowania sytuacji kobiet na lokalnych rynkach pracy w Polsce w latach 2010–2016

2019 ◽  
Vol 2 (341) ◽  
pp. 99-115
Author(s):  
Karolina Lewandowska‑Gwarda
Keyword(s):  
Seemingly Unrelated Regression ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Unrelated Regression ◽  
Lag Model

Głównym celem artykułu jest ocena sytuacji kobiet na lokalnych rynkach pracy w Polsce oraz analiza jej zróżnicowania w czasie i przestrzeni. Podjęto w nim również próbę specyfikacji determinant badanego zjawiska. W analizach wykorzystano taksonomiczny miernik rozwoju, metody geograficznych systemów informacyjnych, metody eksploracyjnej analizy danych przestrzennych oraz wielorównaniowy model o równaniach pozornie niezależnych z autoregresją przestrzenną SUR‑SLM (Seemingly Unrelated Regression Spatial Lag Model). Badania przeprowadzono na podstawie danych statystycznych dla NUTS4 w latach 2010, 2012, 2014 i 2016. Na podstawie uzyskanych wyników zauważono, że zróżnicowanie sytuacji kobiet na lokalnych rynkach pracy w Polsce nie jest duże, niemniej jednak w nieco lepszej sytuacji są Polki mieszkające w okolicach stolicy oraz w zachodniej części kraju. Stwierdzono również, że sytuacja kobiet na lokalnych rynkach pracy nie zmieniła się znacząco w czasie. Dodatkowo potwierdzono, że nie tylko czynniki ekonomiczne, ale w dużej mierze również społeczne wpływają na analizowane zjawisko.

Spatial Aspects of Mortality Rates and Neighborhood Environmental Characteristics in Seoul Mega City Region, South Korea

2015 ◽  
Vol 6 (4) ◽  
pp. 44-64
Author(s):  
Yoohyung Joo ◽  
Hee Yeon Lee
Keyword(s):  
Spatial Patterns ◽  
Statistical Significance ◽  
Mortality Rates ◽  
Spatial Disparity ◽  
City Region ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Spatial Aspect ◽  
Lag Model ◽  
Gwr Model

This study of the spatial patterns of standardized mortality rates (SMRs) in Seoul Mega City Region (SMCR) explores whether neighborhood characteristics affect mortality rates and identifies important determinants of spatial disparity in mortality rates in SMCR. Spatial patterns of mortality rates show a strong positive spatial autocorrelation, suggesting that mortality rates are spatially clustered. A spatial lag model and a GWR model were used to reflect the spatial aspect of mortality rates. The spatial lag model showed better model fitness by considering spatial dependence of mortality rates. It indicates that a higher level of residential deprivation, a less walkable environment, less economic affluence and less social participation are all associated with higher mortality rates with statistical significance. This study suggests that health and welfare policy could incorporate urban planning to consider the neighborhood factors which determine mortality rates in order to improve the health of neighborhood residents.

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