Abstract

A reduction of accessible green space has deteriorated peoples’ opportunities for recreation. To be able to assess landscapes’ potential for recreation, indices and frameworks have been developed. These have mostly relied on expert knowledge rather than analyses of empirical data. Empirical data could be achieved by studying actual landscape usage by recreationists via Public Participatory GIS (PPGIS), where surveys are employed to gain spatial data of peoples’ recreational habits. Analysing such data is challenging, as other aspects than preference, for instance accessibility, also affect where recreation occurs.

This thesis investigates what landscape characteristics are important for recreationists, how an index of recreational potential can be created, and how PPGIS methodology can be improved to better understand recreation. It also evaluates the Perceived Sensory Dimensions framework, a proposed design tool based on how humans perceive environments. These aims are achieved through a literature review of which forest characteristics are preferable, combined with two PPGIS studies employing novel methodology to analyse the choice of location for recreation in Sweden.

The literature review resulted in a proposal for a recreation potential index for forests in Sweden, where large trees, proximity to water, and the absence of traces of forestry were identified as the most important elements. The PPGIS studies showed that the improved methodology, including the use of machine learning models and viewshed analysis, yielded accurate models. The models indicated several characteristics of particular importance for recreationists, such as proximity to water, recreational infrastructure and lack of urban noise. Finally, the evaluation of the PSD framework revealed it to have good internal validity, aligning with theoretical expectations. However, it also concluded that it is unsuitable as a tool for mapping landscapes based on their characteristics.

Keywords

PPGIS; Outdoor recreation; Landscape preference; Recreation potential; Machine learning

Published in

Acta Universitatis Agriculturae Sueciae
2024, number: 2024:30
ISBN: 978-91-8046-324-9, eISBN: 978-91-8046-325-6
Publisher: Swedish University of Agricultural Sciences

SLU Authors

• Lehto, Carl

  • Department of Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Landscape Architecture
Forest Science


Publication identifier

DOI: https://doi.org/10.54612/a.5nfb4mggh5

Permanent link to this page (URI)

https://res.slu.se/id/publ/128541