El presente trabajo se concentra en el análisis multitemporal de imágenes satelitales para determinar el estado de deforestación en las provincias de Pastaza y Orellana de la región amazónica del Ecuador durante el periodo 2000-2020, debido a que estas provincias albergan la mayor área de bosque primario con una extensión de 5125000 ha; dicho análisis se lo realizó utilizando imágenes satelitales Landsat 7 y 8 mediante el preprocesamiento, procesamiento, clasificación de uso de suelo y cobertura vegetal y composición de mapas a través de los softwares ENVI y ArcMap. En base a los datos obtenidos del análisis multitemporal se aplicó la ecuación propuesta por la FAO para el cálculo de la tasa de deforestación en los periodos: 2000-2005, 2005-2010, 2010-2015 y 2015-2020, en donde los datos resultantes evidenciaron el cambio de la cobertura del suelo, debido al crecimiento de la frontera agrícola, pastizales, zonas descubiertas, el crecimiento urbano y la aparición de actividades mineras y petroleras que infieren directamente en la pérdida de cobertura forestal.

Education has been considered a cornerstone for human and economic development. Although there is a national educational strategy in most countries, various implementations are at the state level. This paper studies academic efficiency at the primary and secondary levels and the human development dimensions –long and healthy life, being knowledgeable, and enjoying a decent standard of life– at the state level. For this purpose, a network data envelopment analysis (NDEA) with two stages was proposed. The first stage studies the educational process efficiency, while the second evaluates its impact in the form of the human development index. The study found significant differences between the evaluated states in the education stage, where the lowest efficiencies are mainly in the southwest of Mexico. The results also indicate that better education quality leads to greater regional, economic, and social development at the state level. This study contributes to the NDEA applications on the understanding of the impact that education has in improving the development of the regions holistically.

Data envelopment analysis (DEA) is a methodology for evaluating efficiencies of decision-making units (DMUs) with each unit having its own set of inputs and outputs. However, there are situations where there can be an interdependence among the units. In a previous paper the authors examine efficiency measurement in a situation where university departments are grouped by faculty and share a single resource at the faculty level. Furthermore, the shared resource is assumed to be one which cannot be split up and allocated to the group members. The current paper generalizes that earlier work by considering decision-making units grouped according to multiple attributes and with multiple shared inputs. In addition, the problem of overlapping groups is investigated. A DEA-like methodology is developed for deriving efficiency scores in this multiple attribute situation. Further, we present a methodology for evaluating efficiency at the level of the groups, e.g. the level of the faculty, as well as at the level of the members within the groups. To further demonstrate the need for such methodologies, we present a number of real-world problem settings where shared factors and groupings of DMUs need to be dealt with.