Location-Based Social Network Data for Exploring Spatial and Functional Urban Tourists and Residents Consumption Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban tourist destinations’ increasing popularity has been a catalyst for discussion about the tourist activity geographical circumscription. In this context, Big Data and more specifically location-based social networks (LBSN), appear as a valuable source of information to approach tourist and residents spatial interactions from a renewed perspective. This paper focuses on approaching similarities and differences between tourists and residents’ geographical and functional use of urban economic units. A user classificatory algorithm has been developed and applied on YELP’s Dataset for that purpose. A residents and tourists integration ratio has then been calculated and applied by types of businesses categories and their associated spatial distribution of the of 11 metropolitan areas provided in the sample: Champaign (Illinois, US), Charlotte (North Carolina, US), Cleveland (Ohio, US), Edinburgh (Scotland, UK), Las Vegas (Nevada, US), Madison (Wisconsin, US), Montreal (Quebec, CA), Pittsburgh (Pennsylvania, US), Phoenix (Arizona, US), Stuttgart (DE) and Toronto (Ontario, CA). Business category results show strong similarities in tourists and residents functional coincidence in the use of urban spaces and leisure offer, while there is a clear geographical concentration of activity for both user types in all analysed case studies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it