Interactive visualization tools for spatial data & metadata
Bibliographic record
Abstract
In recent years, the focus of cartographic research has shifted from the cartographic \ncommunication paradigm to the scientific visualization paradigm. With this, there has been a \nresurgence of cognitive research that is invaluable in guiding the design and evaluation of \neffective cartographic visualization tools. The design of new tools that allow effective visual \nexploration of spatial data and data quality information in a resource management setting is \ncritical if decision-makers and policy setters are to make accurate and confident decisions that \nwill have a positive long-term impact on the environment. \nThe research presented in this dissertation integrates the results of previous research in \nspatial cognition, visualization of spatial information and on-line map use in order to explore the \ndesign, development and experimental testing of four interactive visualization tools that can be \nused to simultaneously explore spatial data and data quality. Two are traditional online tools \n(side-by-side and sequenced maps) and two are newly developed tools (an interactive "merger" \nbivariate map and a hybrid o f the merger map and the hypermap). \nThe key research question is: Are interactive visualization tools, such as interactive \nbivariate maps and hypermaps, more effective for communicating spatial information than less \ninteractive tools such as sequenced maps? A methodology was developed in which subjects used \nthe visualization tools to explore a forest species composition and associated data quality map in \norder to perform a range of map-use tasks. Tasks focused on an imaginary land-use conflict for a \nsmall region of mixed boreal forest in Northern Alberta. Subject responses in terms of \nperformance (accuracy and confidence) and preference are recorded and analyzed. Results show \nthat theory-based, well-designed interactive tools facilitate improved performance across all \ntasks, but there is an optimal matching between specific tasks and tools. The results are \ngeneralized into practical guidelines for software developers. The use of confidence as a measure \nof map-use effectiveness is verified. In this experimental setting, individual differences (in terms \nof preference, ability, gender etc.) did not significantly affect performance.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".