Towards Qualitative Geovisual Analytics: A Case Study Involving Places, People, and Mediated Experience
Bibliographic record
Abstract
This article seeks to address the gap between the quantitative, summative data that are typically engaged in geovisual analytics and the more personal, experiential ways of knowledge construction accentuated by qualitative GIS. By incorporating diverse forms of data within a high-dimensional conceptual framework, we set out a type of qualitative geovisual analytics. This approach is attentive to the epistemological limitations of singular data sources and highlights the multiple ways of exploring neighbourhoods. The article reports on a project that used an online survey, including collection of personal impressions of San Diego neighbourhoods based on street-level video. Three attribute spaces are conceptualized: survey respondents' characteristics, attributes of San Diego neighbourhoods, and characteristics of the words used to describe these neighbourhoods. The self-organizing map (SOM) technique was used to reduce the dimensionality of these attribute spaces for visual exploration. This approach makes several important contributions, including a demonstration of “scaling up” the work that has been done in qualitative GIS. It productively integrates experiential data with a geovisual analytics approach and reaffirms the multiple meanings of visualization.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".