Dealing with the Understanding of the Dynamics Related to Multifactorial Temporal Interactions That Spatially Affect the Landscape of Coastal Lagoons

Abstract

Models based on multifactorial interactions are needed to deal with the dynamics taking place in the eutrophication processes of coastal lagoons. However, as the number of indirect drivers stemming from anthropogenic factors increases, temporal disorders between anthropogenic activities may increase, thus hindering the understanding of their dynamics. We have built multifactorial pathways to deal with the dynamics associated with the cultural eutrophication process of a coastal lagoon. The pathways guided the identification of potential temporal disorder patterns between anthropogenic activities, which may exert influence on the disturbances associated with eutrophication process. The identification of temporal disorder patterns derived from anthropogenic activities belonging to different pathways resulted in a valuable form of support for analyzing and evaluating relationships between public policies, technological skills and environmental culture programs. All of which exert influence on the eutrophication process, which in turn cause changes on the trophic state and on the landscape of the coastal lagoon. Pathways composed of multifactorial interactions that take into account spatial and temporal aspects, contribute to improving the understanding of the inherent dynamics of the eutrophication process of coastal lagoons. Temporal disorders between anthropogenic activities may be seen to emerge, thus exerting changes on the trophic state and spatial damage on the landscapes of coastal lagoons.

Analysing the global and local spatial associations of medical resources across Wuhan city using POI data

“…Such studies frequently use regression models, such as ordinary least squares regression (OLS) [ 12 ], geographically weighted regression (GWR) [ 13 , 14 ], and logistic regression (LR) [ 15 ], to model and explain spatial patterns. The majority of the studies use aggregate data from national censuses, cross a range of scales from subdistrict to community [ 16 , 17 ]. Geographically weighted analytical methods, including regression and principal component analysis, have been proven effective and efficient in dealing with spatial heterogeneity, specifically spatial nonstationarity.…”