Spatial literacy influences stakeholder’s recognition and mapping of peri-urban and urban ecosystem services

There are several spatial valuation and ecosystem service mapping studies using participatory methods in North America, Australia, Europe and Japan. But, there is much less information from urban areas in the Global South using these approaches, particularly regarding the influence of spatial literacy on such methods. Accordingly, we tested how two-dimensional (2D) maps and three-dimensional models (3DCM) influence the identification of urban and peri-urban ecosystem services by different stakeholders near forested landscapes adjacent to Bogotá, Colombia. We used on-site interviews, quantitative machine-learning statistics, and qualitative methods to identify predictors and assess the ability of different stakeholders to identify: peri-urban forest ecosystem services, threats to forest ecosystems, and in locating points of interest. We found that age, residential proximity to the study sites, and education were the best predictors for estimating the number of ecosystem services. Older and non-local interviewees less than 20 years old recognized a greater number of ecosystem services. Using 2D maps to locate predesignated sites resulted in better results than when using a 3DCM; particularly with younger respondents. However, respondents were able to locate more predesignated sites with the 3DCM when they had a higher level of education. As opposed to other studies, our stakeholders more frequently identified regulating as opposed to cultural ecosystem services. Our study identified socio-demographic predictors that could be used to assess different stakeholder’s abilities in recognizing different processes from landscapes as well as their difficulty in accurately locating areas of interest. Such low cost and participatory approaches can be used to design more context-relevant survey instruments for ecosystem service valuation research and assessments. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

• Fecha de publicación: 2020
• Institución: Universidad del Rosario
• Repositorio: Repositorio EdocUR - U. Rosario
• Idioma: eng
• OAI Identifier: oai:repository.urosario.edu.co:10336/23410
• Acceso en línea: https://doi.org/10.1007/s11252-020-00962-y; https://repository.urosario.edu.co/handle/10336/23410
• Palabra clave: Classification and regression trees; Mapping literacy; Participatory geographic information systems; Peri-urban forest ecosystem services
• License: Abierto (Texto Completo)