Automatic detection of vegetation cover changes in urban-rural interface areas

Barbosa, Bruno; Rocha, Jorge; Costa, Hugo; Caetano, Mário

doi:10.1016/j.mex.2022.101643

Cited by 1 publication

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“…The dates evaluated in the spatial analysis were those in which there was a statistically significant change in the registered cases in relation to the records, before and after the date under analysis. Thus, we used a variant of the Student's t-test, the Welch t-test [39], which allowed us to assume the variances of the samples (previous and posterior) as being equal (i.e., homoscedasticity); this was calculated as follows [40]:…”

Section: Data Collection and Processingmentioning

confidence: 99%

Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal

et al. 2022

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The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.

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Section: Data Collection and Processingmentioning

confidence: 99%