Effect of Experimental Blocking on the Suppression of Spatial Dependence Potentially Attributable to Physicochemical Properties of Soils

Darghan, Aquiles Enrique; Moreno, Giovanni Reyes; Rivera, C.; Camargo, Edwin Francisco Grisales

doi:10.1155/2021/3322074

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Spatial regression models, in addition to allowing the establishment of relations between the variables of interest, allow the variability of spatial dependence to be included in the analysis. This is of great importance because ignoring this factor could lead to biased estimates in the results for the presence of spatial dependence indicates the existence of a functional relation between two locations in two different places; the assumption of independence of the residuals of classical statistics would not be met (47,48). Applications of these models include environmental issues related to particulate pollution and the nitrogen footprint of food (47,49).…”

Section: Spatial Linear Regression Modelsmentioning

confidence: 99%

Relation between Environmental Variables and the Spatial Distribution of the Aedes aegypti Mosquito in Rural Colombia

Cabezas¹,

Marin²,

Andrade‐Rivas³

et al. 2022

Rev. salud. bosque.

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Background: Changes in global and local environmental variables condition the distribution and density of disease vectors. This study aimed to estimate the relationship between the entomological indicator of immature and adult forms of the Aedes aegypti mosquito per unit area, the environmental variables of temperature, precipitation, and relative humidity in rural areas of two municipalities in Colombia. Methods: Four spatial regression models were fitted: The Spatial Autoregressive Model with Autoregressive Disturbances of order [1,1] (SARAR[1,1]), Spatial Error Model (SEM), Spatial Lag Model (SLM), and the Pure Spatial Autoregressive Model. Immature and adult A. aegypti forms were collected in homes during June 2013 (dry season). The houses were chosen at random and were georeferenced. Climate information was obtained from the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM- Instituto de Hidrología, Meteorología y Estudios Ambientales). Climatic information was completed with mathematical interpolation from the Akima library. Results: The most appropriate model was SARAR[1,1], as it showed the lowest values of the Akaike information criterion (AIC = 473.34). In this model, the variable that best explained the entomological indicator (immature and adult forms per unit area) was the altitude of the houses in the rural area where the entomological samples were collected. This means that the higher the altitude, the lower the entomological indicator calculated. The ranges of the environmental variables in which the presence of the mosquito occurred are between 602 to 1414 m.a.s.l (meters above sea level) for altitude, 17 °C to 27 °C for temperature, 27 mm to 86 mm for precipitation, and 70% to 85% for relative humidity. Conclusions: The importance of understanding the relationship between local environmental characteristics and the presence of the vector for designing comprehensive management strategies was highlighted, contributing to better surveillance, prevention, and control of vectors and diseases transmitted by them.

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Section: Spatial Linear Regression Modelsmentioning

confidence: 99%

Relation between Environmental Variables and the Spatial Distribution of the Aedes aegypti Mosquito in Rural Colombia

Cabezas¹,

Marin²,

Andrade‐Rivas³

et al. 2022

Rev. salud. bosque.

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The effect of spatial lag on modeling geomatic covariates using analysis of variance

Aquiles E.,

Darlley S.,

Nair J.

et al. 2024

Appl Geomat

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In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.

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Spatial modeling of infiltration and its relationship with surface coverage of rock fragments and porosity in soils of an andean micro-watershed in Tolima (Colombia)

et al. 2023

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Effect of Experimental Blocking on the Suppression of Spatial Dependence Potentially Attributable to Physicochemical Properties of Soils

Cited by 3 publications

References 36 publications

Relation between Environmental Variables and the Spatial Distribution of the Aedes aegypti Mosquito in Rural Colombia

Relation between Environmental Variables and the Spatial Distribution of the Aedes aegypti Mosquito in Rural Colombia

The effect of spatial lag on modeling geomatic covariates using analysis of variance

Spatial modeling of infiltration and its relationship with surface coverage of rock fragments and porosity in soils of an andean micro-watershed in Tolima (Colombia)

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