The Development and Application of Analysis Model for Spatial Features' Distribution Change
Bibliographic record
Abstract
The analysis of spatial features' distribution change is an important content in many geographic researches. It is always used in the analysis of the migration of population, the expanding of urban area and the change of the pollution sources etc. Many scientists study the phenomena of spatial features' distribution change in order to learn the rules of the nature or the societies. Some of them analyzed the transformation of the product structure of urban areas through studying the change of land-use of the area, and then make the full use of the limited land. Others observed the migration of transients and then selected a suited location to build up a birds' protection area. We always analyze the change of spatial feature's distribution through overlapping two map layers of different times in the same area. The present GIS software such as ArcView and ERDAS has some tools for map layers' overlapping, however, in the practice, we cannot get satisfied results from using such tools. Therefore, we developed some special models for detecting the distribution changes of spatial features. In this paper, we discussed the methods for detecting the distribution changing of point, line and polygon, and introduced the development of the analysis model based on COM GIS. At last we demonstrate the examples of the pollution sources, rivers and land-use in Shanghai using the models we developed.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".