Evaluation of Open-source Green Infrastructure-based Planning Support Tools in the Global North and South: A Systematic Review
Skillset Required:
The student should have a solid foundation in both quantitative and qualitative research methodologies and be adept at data mining across various platforms. Familiarity with ATLAS.ti for text screening and data extraction is essential but not a must.
Other Info:
This study will rigorously follow the FAIR principles. The student should be prepared for an extensive review process involving diverse sources of information (including social media).
Urban environmental planning and management, both in theory and practice, have indicated the need for participatory inclusivity as much as possible. Over the years, different green infrastructure (GI) based infrastructure planning support tools have been developed, either for reducing urban heat incidences or making cities aesthetically appealing, amongst others. Some of these tools include i-Tree Suite, Green City Watch, and OPENSiUC. These and other related tools are often developed from different perspectives of developers and are to be used in different terrains by stakeholders. A critical aspect is that these end users also have their unique factors responsible for their acceptance and use of these tools. Thus, the emphasis of this study is on the review of open-source tools and the evaluation of user stories as this will open up new areas for the development or improvement of existing GI-based tools while addressing the shortcomings that will be revealed.
This study intends to identify all GI-based planning support tools that are open and analyse the features of the tool as well as the feedback from its users as contained in published articles (including tool documentation) and from other channels, where possible. Machine learning and other AI tools will be used to support the screening of articles and the extraction of data into an open data pipeline. Through this tightly coupled curation process, data obtained will be analysed using both qualitative and quantitative methods. Various multivariate models and approaches will then be applied to reveal and make a comparative assessment of GI-based planning support systems in two global regions and indicate core areas for improvement. This study will also highlight improvement areas for designing or distribution of future tools.
Aguilar, R., Calisto, L., Flacke, J., Akbar, A., & Pfeffer, K. (2021). OGITO, an Open Geospatial Interactive Tool to support collaborative spatial planning with a maptable. Computers, Environment and Urban Systems, 86(November 2020), 101591. https://doi.org/10.1016/j.compenvurbsys.2020.101591
American Society of Landscape Architects. (2022). Green Infrastructure: Cities. Professional Practice. https://www.asla.org/ContentDetail.aspx?id=43535
Kuller, M., Farrelly, M., Deletic, A., & Bach, P. M. (2018). Building effective Planning Support Systems for green urban water infrastructure—Practitioners’ perceptions. Environmental Science and Policy, 89(July), 153–162. https://doi.org/10.1016/j.envsci.2018.06.011
Omitaomu, O. A., Kotikot, S. M., & Parish, E. S. (2021). Planning green infrastructure placement based on projected precipitation data. Journal of Environmental Management, 279(October 2020), 111718. https://doi.org/10.1016/j.jenvman.2020.111718