Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources
Bibliographic record
Abstract
The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there are few examples of comprehensive data processing and analysis systems that link together these tools for geographic analyses. This paper develops a general approach to the development of spatial information context from unstructured and informal web data sources through the joint analysis of the data’s thematic, spatial, and temporal properties. We explore the utility of this derived contextual information through a case study into maritime surveillance. Extraction and processing techniques such as toponym extraction, disambiguation, and temporal information extraction methods are used to construct a semi-structured maritime context database supporting global scale analysis. Geographic, temporal, and thematic content were analyzed, extracted and processed from a list of information sources. A geoweb interface is developed to allow user visualization of extracted information, as well as to support space-time database queries. Joint keyword clustering and spatial clustering methods are used to demonstrate extraction of documents that relate to real world events in official vessel information data. The quality of contextual geospatial information sources is evaluated in reference to known maritime anomalies obtained from authoritative sources. The feasibility of automated context extraction using the proposed framework and linkage to external data using standard clustering tools is demonstrated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.016 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".