Mining Dependencies Considering Time Lag in Spatio-Temporal Traffic Data

Understanding the initial growth rate of an epidemic is important for epidemiologists and policy makers as it can impact their mitigation strategies such as school closures, quarantines, or social distancing. Because the transmission rate depends on the contact rate of the susceptible population with infected individuals, similar growth rates might be experienced in nearby geographical areas. This research determined the growth rate of cases and deaths associated with COVID-19 in the early period of the 2020 pandemic in Ohio, United States. The evolution of cases and deaths was modeled through a Besag-York-Molliè model with linear- and power-type deterministic time dependence. The analysis showed that the growth rate of the time component of the model was subexponential in both cases and deaths once the time-lag across counties of the appearance of the first COVID-19 case was considered. Moreover, deaths in the northeast counties in Ohio were strongly related to the deaths in nearby counties.

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Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Sustainability ◽

10.3390/su12051939 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1939 ◽

Cited By ~ 3

Author(s):

Edith Olmos-Trujillo ◽

Julián González-Trinidad ◽

Hugo Júnez-Ferreira ◽

Anuard Pacheco-Guerrero ◽

Carlos Bautista-Capetillo ◽

...

Keyword(s):

Arid Region ◽

Climatic Factors ◽

Time Lag ◽

Vegetation Indices ◽

Climatic Variables ◽

Semiarid Region ◽

Linear Regression Models ◽

Spatio Temporal ◽

Semi Arid Region ◽

Semi Arid

In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies.

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