A Q methodology application on disaster perceptions for adaptation and resiliency in an Andean watershed symposium: water and climate in Latin America

A data mining approach to the relationships between landslides and open-pit mining activity: a case study in Soacha (Cundinamarca)

“…That is why it is essential to establish measurable relationships between mining and the effects on the physical environment which produce various sorts of conflicts that impact the actors [7,9]. A clearer identification of causes and effects allows the actors involved to arrive at converging solutions that they can adopt in a more rational and objective way [10].…”

“…Environmental problems arise due to the ways in which each society develops its technological, organizational, economic and political processes and the manner these processes manifest in the planet's biophysical systems. Environmental problems are only solved to the extent that structural changes occur in the processes that generate culture [9,10], which can only be achieved through an objective determination of causes and effects.…”

“…Research about environmental issues and conflicts linked to mining activity has been tackled from alternative approaches rooted in human geography and economic geography. From these perspectives, territorial configurations and the social production of space and are riddled with conflicts [1,9,10].…”

“…Environmental conflicts arise when a social group perceives that the use, appropriation or meaning it gives to the territory is under threat due to the impacts that derive from the practices of other groups. The presence of an environmental problem does not imply per se the occurrence of a conflict; to have one, it is necessary that the groups involved have a dynamic of opposition [10][11]. Environmental conflicts around mining activity occur as a product of the physical-social relationship, in a defined time and space and under the influence of the ideas and visions of the social groups involved as active and passive actors [8,12].…”

“…Data mining consists of obtaining: 1) dependency analysis 2) identification of classes, 3) description of concepts, and 4) detection of deviations, extreme cases, or anomalies [13]. Supervised and unsupervised techniques are used to perform data mining [10,[13][14][15][16]. Among the supervised techniques are decision trees, neural induction, regression, and time series; and among the unsupervised techniques are deviation detection, segmentation, clustering, association techniques, and sequential patterns.…”