From sense of place to visualization of place: examining people-place relationships for insight on developing geovisualizations
Bibliographic record
Abstract
Effective resource planning incorporates people-place relationships, allowing these efforts to be inclusive of the different local beliefs, interests, activities and needs. 'Geovisualizations' can serve as potentially powerful tools for facilitating 'place-conscious' resource planning, as they can be developed with high degrees of realism and accuracy, allowing people to recognize and relate to them as 'real places'. However, little research has been done on this potential, and the place-based applications of these visual tools are poorly understood. This study takes steps toward addressing this gap by exploring the relationship between sense of place and 'visualization of place'. Residents of the Capital Regional District of BC, Canada, were surveyed about their relationship with local coastal places, concerns for the coast, and how they mentally visualize these places. Factor analysis identified four sense of place dimensions - nature protection values, community and economic well-being values, place identity and place dependence, and four coastal concerns dimensions - ecological, private opportunities, public space and boating impacts. Visualization data were coded and treated as dependent variables in a series of logistic regressions that used sense of place and coastal concerns dimensions as predictors. Results indicated that different aspects of sense of place and (to a lesser degree) concerns for places influence the types of elements people include in their mental visualization of place. In addition, sense of place influenced the position and perspective people assume in these visualizations. These findings suggest that key visual elements and perspectives speak to different place relationships, which has implications for developing and using geovisualizations in terms of what elements should be included in tools and (if appropriate) depicted as affected by potential management or development scenarios.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".