Research on the Simulation Application of Data Mining in Urban Spatial Structure
Bibliographic record
Abstract
Data mining and simulation of the Internet of things (IOT) have been applied more and more widely in the rapidly developing urban research discipline. Urban spatial structure is an important field that needs to be explored in the sustainable urban development, while data mining is relatively rare in the research of urban spatial structure. In this study, 705,747 POI (Point of Interest) were used to conduct simulation analysis of western cities in China by mining the data of online maps. Through kernel density analysis and spatial correlation index, the distribution and aggregation characteristics of different types of POI data in urban space were analyzed and the spatial analysis and correlation characteristics among different functional centers of the city were obtained. The spatial structure of the city is characterized by “multicenters and multigroups”, and the distribution of multicenters is also shown in cities with different functional types. The development degree of different urban centers varies significantly, but most of them are still in their infancy. Data mining of Internet of things (IOT) has good adaptability in city simulation and will play an important role in urban research in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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 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".