Urban Cultural Signature with Web Data: A Case Study with Google Places Venues
Bibliographic record
Abstract
Providing knowledge about the characteristics of diverse cultural groups worldwide and identifying cultural similarities between their respective occupation regions can yield significant economic and social benefits. However, much of the existing research in this field relies on user behavior data, which may limit scalability and generalization due to the difficulty in obtaining such data. To address this, our work focuses on extracting venue data from Google Places and proposing a methodology based on the Scenes concept to enrich this dataset for generating cultural signatures of urban areas. This approach also considers the influence of different area sizes. Using Curitiba, Brazil, and Chicago, USA, as case studies, the results demonstrate that the proposed method can identify cultural similarities between regions while supporting an area-division strategy for analyzing cities across different countries. The findings show consistency, as evidenced by the segmentation of Curitiba and Chicago into culturally distinct clusters. This highlights the societal benefits of the proposal, such as location recommendations based on cultural criteria and real-time service validation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".