Predicting Urban Functional Zones with Twitter Data Using the Space-Time Scan Statistics Method and the Random Forest Classifier
Bibliographic record
Abstract
<p>Studies relating to spatiotemporal data clustering in geosocial media data have laid the groundwork for the analysis of clusters over a large study area. This thesis aims to identify whether urban functional zones can be predicted based on a pattern of data clusters that occurs within each functional zone. A framework is presented for the segmentation, annotation, classification, and validation of the functional zones of land segments in an urban study area. The Space-Time Scan Statistics approach was adopted and used to cluster a dataset of tweets into ‘events’. Characteristic attributes of the detected cluster areas were converted into land segment attributes and used as the input in the Random Forest classifier. Labels for each segment were determined based on high-level land use classes. The classifier was trained, validated, and tested on subsets of the study area. The resulting precision, recall, and F1 score were 88.96%, 89.15%, and 88.23%, respectively. </p>
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".