Fuzzy Boundaries: Hybridizing Location‐based Services, Volunteered Geographic Information and Geovisualization Literature
Bibliographic record
Abstract
Abstract Mobile applications are particularly exciting to geographers due to their ability to collect swathes of spatial data from citizens, to present information relevant to a user's current location, and to present data via interactive visualizations. While these functions are presented together within a single mobile location‐based application (LBApps), the academic literatures pertaining to each of these three functions are highly fragmented. Thus, we ask: what is the relationship between the three major components of LBApps: location‐based services (LBS), volunteered geographic information (VGI), and geovisualization? Additionally, what are some of the possible resulting implications for users' spatial understandings after interaction with these three components? Here, we present literature from VGI, LBS, and geovisualization that is relevant to mobile applications. We seek to reveal the synergistic relationship between these mechanisms in addition to the existing overlaps and gaps in the literature. We hope that this is a starting point for geographers interested in researching mobile applications to further enhance the collection, distribution, and visualization of spatial data. Like traditional cartography, it is imperative to keep the intended audience in mind during each step of the LBApp design and research process.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".