Spatial Error Model (SEM)

Nima Kianfar; Mohammad Saadi Mesgari

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Spatial Error Model (SEM)

NK Nima Kianfar

MM Mohammad Saadi Mesgari

This method is extracted from research article: Spat Spatiotemporal Epidemiol, Mar 2022

GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

DOI: 10.1016/j.sste.2022.100498

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The OLS often refuses to consider the explanatory variables with spatial dependence (Wu et al., 2020). However, the SEM considers spatial dependency in the OLS error term (Mollalo et al., 2020) by decomposing the error term of OLS into two components, including the error term and the random error term. The formula of SEM is as follows: (Ward & Gleditsch, 2018)

where $λ$ signifies the coefficient of spatial component errors, $W_{i}$ represents the weight matrix (a vector of spatial weights) which determines the neighbors at country $i$ and connects the independent variable to the explanatory variables at that country, and $ξ_{i}$ describes the spatial error component.

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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