A GIS-based Decision Support System for Neighbourhood Greening
Bibliographic record
Abstract
A prototype decision support tool is described which provides options for the management of existing green spaces and for the establishment of new green space in suburban neighbourhoods. Suggested neighbourhood greening techniques include the naturalization of existing parks and increased foliage along streets and rights of way. The naturalization approach involves less frequent cutting in grassy fields, the introduction of native species, and the cessation of pesticide and herbicide applications. Increased plantings along streets and boulevards would improve the aesthetics of neighbourhoods, and may provide some relief from climatic extremes and urban heat island effects. The creation of new green space in already-built suburban neighbourhoods provides a longer term challenge to neighbourhood planners. Potential strategies include the introduction of small pocket parks and community gardens in vacant lots and school yards. Modelled outcomes from such neighbourhood greening strategies could be used in public meetings both to incorporate attitudes of the impacted community and to demonstrate benefits to a wider community. In particular, strategies should take into account issues of safety and perceived safety that commonly arise with the greater use of naturalization in green space management. The developed prototype decision support tool has been coded as an ArcView GIS extension and provides the opportunity to model and evaluate future scenarios better aligned to principles of sustainable community development. Three applications of this tool are discussed to illustrate some of the benefits of undertaking a range of neighbourhood greening strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".