AID-PRIGSHARE

Automatization of indicator development in green space health research in QGIS. Accompanying script to the PRIGSHARE reporting guidelines

Journal article (2023)

Authors

M. Cardinali Heritage & Architecture - Architecture and the Built Environment
Marcel Cardinali Heritage & Architecture - Architecture and the Built Environment
Mariëlle A. Beenackers Erasmus Medical Centre
Arjan Timmeren Environmental Technology and Design - Architecture and the Built Environment
Arjan van van Timmeren Environmental Technology and Design - Architecture and the Built Environment
A. van Timmeren Environmental Technology and Design - Architecture and the Built Environment
A. Timmeren Environmental Technology and Design - Architecture and the Built Environment
Arjan van Timmeren Environmental Technology and Design - Architecture and the Built Environment
Arjan van Timmeren Environmental Technology and Design - Architecture and the Built Environment
Uta Pottgiesser Heritage & Architecture - Architecture and the Built Environment
U. Pottgiesser Heritage & Architecture - Architecture and the Built Environment
Research Group
Heritage & Architecture (Architecture and the Built Environment) (TU Delft)
Copyright: © 2023 M. Cardinali, Mariëlle A. Beenackers, A. van Timmeren, U. Pottgiesser
DOI: https://doi.org/10.1016/j.simpa.2023.100506
More Info
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Published Date

2023

Language

English

Faculty

Architecture and the Built Environment

Department

Architectural Engineering +Technology

Research Group

Heritage & Architecture

Abstract

In the interdisciplinary field of green space health research, there is a demand to reduce the effort to assess green space, especially for non-spatial disciplines. To address this issue, we developed AID-PRIGSHARE, an open-source script that automates over 400 QGIS processes to substantially reduces the time-intensive task of generating green space indicators. AID-PRIGSHARE calculates greenness, public green space, access to green infrastructure, and green space uses within distances of 100–1500 m around geolocations. This substantially reduces the effort for sensitivity analysis and may provide support for research that aims to understand the impact of different green space features and distances on health outcomes.