Smarter forests for smarter cities? : an exploration of digital and smart technologies in urban forest management
Bibliographic record
Abstract
Urban forests are recognised as integral to urban sustainability and liveability due to their multifunctional contributions to positive ecological, economic, health, and social outcomes. Recently, digital-based technologies are increasingly garnering attention, with dialogues about the use of data and connected technologies in urban planning and service delivery proliferating among municipal stakeholders. As practitioners are faced with demand to support and address broader sustainability and resiliency objectives, it is unclear how more ubiquitous digital practices may shape the planning and management of urban forests and green spaces. To address this gap, this dissertation (1) introduces and discusses frameworks for understanding and studying the integration of digital and smart technologies in urban forest management, (2) identifies current and emerging trends for technology applications in forested ecosystems, (3) explores practitioner perspectives of technology adoption in urban forestry and green space management, and (4) tests the use of two emerging digital technologies, consumer remotely piloted aircraft systems (RPAS, or drones) and smartphone-location data, through a case study of urban parks in Vancouver, BC, Canada. The research found that remote sensing and geographic information systems (GIS) are most frequently used for mapping and inventory purposes, while newer applications include sensing devices and networks, mobile tracking, and values elicitation. In the context of new technology adoption, practitioners expressed both concern about organisational capacity, expertise, and data usage and reliability, in addition to interest in the collection of finer-scale social and ecological data to inform management, planning, and policy. To this end, the testing of RPAS and smartphone data highlighted opportunities for practitioners to bridge social and ecological data gaps in urban park management, and presented a novel approach for identifying and assessing technology adoption opportunities in urban forestry. The overarching goal of this work is to advance understanding at the intersection of technology and forests, paving the way for future theoretical, methodological, and empirical research examining relationships between people, nature, and technology, as we tackle critical environmental problems in this digital age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".