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      Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country

      , , , , ,
      Science of The Total Environment
      Elsevier BV

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          GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

          During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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            Multiscale Geographically Weighted Regression (MGWR)

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              Ecological intensification of agriculture—sustainable by nature

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                Author and article information

                Journal
                Science of The Total Environment
                Science of The Total Environment
                Elsevier BV
                00489697
                February 2022
                February 2022
                : 808
                : 152126
                Article
                10.1016/j.scitotenv.2021.152126
                34863745
                735b0e59-3b76-4a93-bafd-f430eaad6dc3
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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