A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A soft clustering approach to detect socio-ecological landscape boundaries using bayesian networks
Detecting socio-ecological boundaries in traditional rural landscapes is very important for the planning and sustainability of these landscapes. Most of the traditional methods to detect ecological boundaries have two major shortcomings: they are unable to include uncertainty, and they often exclude socio-economic information. This paper presents a new approach, based on unsupervised Bayesian network classifiers, to find spatial clusters and their boundaries in socio-ecological systems. As a case study, a Mediterranean cultural landscape was used. As a result, six socio-ecological sectors, following both longitudinal and altitudinal gradients, were identified. In addition, different socio-ecological boundaries were detected using a probability threshold. Thanks to its probabilistic nature, the proposed method allows experts and stakeholders to distinguish between different levels of uncertainty in landscape management. The inherent complexity and heterogeneity of the natural landscape is easily handled by Bayesian networks. Moreover, variables from different sources and characteristics can be simultaneously included. These features confer an advantage over other traditional techniques.
A soft clustering approach to detect socio-ecological landscape boundaries using bayesian networks
Detecting socio-ecological boundaries in traditional rural landscapes is very important for the planning and sustainability of these landscapes. Most of the traditional methods to detect ecological boundaries have two major shortcomings: they are unable to include uncertainty, and they often exclude socio-economic information. This paper presents a new approach, based on unsupervised Bayesian network classifiers, to find spatial clusters and their boundaries in socio-ecological systems. As a case study, a Mediterranean cultural landscape was used. As a result, six socio-ecological sectors, following both longitudinal and altitudinal gradients, were identified. In addition, different socio-ecological boundaries were detected using a probability threshold. Thanks to its probabilistic nature, the proposed method allows experts and stakeholders to distinguish between different levels of uncertainty in landscape management. The inherent complexity and heterogeneity of the natural landscape is easily handled by Bayesian networks. Moreover, variables from different sources and characteristics can be simultaneously included. These features confer an advantage over other traditional techniques.
A soft clustering approach to detect socio-ecological landscape boundaries using bayesian networks
Ropero, Rosa F. (author) / Maldonado, Ana D. (author) / Uusitalo, Laura (author) / Salmerón, Antonio (author) / Rumí, Rafael (author) / Aguilera, Pedro A. (author)
2021-01-01
Article (Journal)
Electronic Resource
English
kulttuurimaisema , klusterointi , clustering , Bayesian analysis , socio-ecosystems , bayesilainen menetelmä , climate changes , sustainable development , maisemat , Välimeri , landscape management , kestävä kehitys , maisemanhoito , boundary detection , landscape planning , Mediterranean cultural landscape , kulttuuriekologia , landscape , Bayesian networks , sosio-ekosysteemit
DDC:
710
Future scenarios for socio-ecological production landscape and seascape
| Springer Verlag | 2019
FEATURES - Multiscale Characterization of Boundaries and Landscape Ecological Patterns
| Online Contents | 2001
Landscape planning and ecological networks
| TIBKAT | 1994
Landscape planning and ecological networks
| Catalogue agriculture | 1994
Call for Papers for “Future scenarios for socio-ecological production landscape and seascape”
| Springer Verlag | 2017