Quantify the Spatial Association between the Distribution of Catering Business and Urban Spaces in London Using Catering POI Data and Image Segmentation
Bibliographic record
Abstract
The impacts of global climate change on food systems will be broad, complex, and profoundly affected by urban context. Food-related urbanism has been investigated for decades to explore how food access influences placemaking and urban forms. With global climate change, foodscapes within urban spaces are an important consideration in urban design and planning for food security and community health. The distribution of catering businesses (restaurants and cafés), one critical method of access to food, is highly associated with urban spaces because of their high impact on diet patterns, human physical activities, travel behaviors, and the use of public spaces. This research explores the spatial associations that exist between the distribution of catering businesses and the design and planning of urban spaces in London. This quantitative research includes three parts: (1) uses Open Street Map data and the GIS spatial analysis method to study the distribution of catering businesses; (2) uses the imagery segmentation method in machine learning to categorize urban spaces into open, landscape, and conflict spaces; and (3) establishes the association between the distribution of catering businesses and the categories of urban spaces through Spearman’s correlation and a linear regression model. The results indicate that the spatial distributions of catering businesses are highly correlated with urban spaces. Conflict and landscape spaces have a significant positive influence on the distribution of catering businesses, while open space has a significant negative influence. Based on the context of global climate change, this research contributes a quantitative urban design and planning approach to promote access to food increase food options and advocate active lifestyles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".