An investigation of urban agriculture on residential blocks in Vancouver
Bibliographic record
Abstract
Urban agriculture, which encompasses growing food on residential land, community and school gardens, rooftop gardens and inner city farms and any other food growing that occurs within a city, is an increasingly popular activity in the City of Vancouver. Urban agriculture has many environmental and social benefits and this is recognized in recent publications by municipal governments of the region. The focus of this project was to investigate how land is partitioned on residential blocks, how much residential land is being used to grow food, and if there is space and willingness to increase food growing on residential blocks. We also investigated whether residents would be willing to share their yard space if they have it. To answer these questions, we analyzed aerial photos of one block randomly selected from each of Vancouver’s twenty-two neighbourhoods and administered a survey to the residents of these neighbourhoods. We grouped the twenty-two neighbourhoods of our analysis into four categories based on their dwelling density. Some important findings were that 77% of Vancouver residents believe it is important to eat food that is grown locally, 52% of Vancouver residents grow some food for some period of the year. There is a significant correlation between individual residents’ responses relating to the importance of eating local food and whether they grow food. We also found that between 8-12% of the yard area of residential blocks is currently being used to grow food, which leaves space for food growing to increase. Our survey data indicates that there is not only available space, but also willingness on behalf of residents for food growing to increase. The data also indicates that given various conditions, at a minimum 26% of residents would be willing to share their yard space for others to grow food on. Based on the findings from our analysis of aerial photos and from our survey, we make the following recommendations: • Increase education on the benefits of growing food and how to tend a food garden in Vancouver. • Encourage and support Vancouver residents to grow food on residential spaces. • Support the existing supply of local food in its many forms and preserve existing agricultural land. • Provide more long lasting spaces for growing food for recreational and commercial purposes. • Use data on current high density areas as predictors for the needs of low and medium density areas in the future. Green space on residential blocks is an inventory of land that currently contributes to urban agriculture in the City of Vancouver. With appropriate action on behalf of residents and local governments, this inventory could be used to increase urban agriculture, which has the potential to benefit the City and its residents in various ways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".