Modeling Both the Space and Place of Coastal Environments: Exploring an Approach for Developing Realistic Geovisualizations of Coastal Places
Bibliographic record
Abstract
Effective coastal planning incorporates the variety of user needs, values, and interests associated with coastal environments. This requires understanding how people relate to coastal environments as ‘places’, imbued with values and meanings, and accordingly, tools that can capture place and connect with people’s ‘sense of place’ have the potential for supporting effective coastal management strategies. Realistic, immersive geographical visualizations, i.e., geovisualizations, theoretically hold potential to serve such a role in coastal planning; however, significant research gaps exist around this application context. Firstly, place theory and geovisualizations are rarely explicitly linked in the same studies, leaving questions around how to model ‘coastal place', as well as coastal space. Secondly, geovisualization work has focused on terrestrial environments, and research on how to realistically model coastal places is currently in its infancy. The current study addresses the research gaps by developing a coastal geovisualization under place-based considerations, and then examining its capacity as a tool for connecting with people’s sense of place. The research uses Sidney Spit in the Gulf Islands National Park Reserve (BC, Canada) as a study site, and a geovisualization was developed using a combination of ArcGIS, Adobe Photoshop, Trimble SketchUp, and Unity3D. Focus groups were assembled involving Parks Canada staff and Greater Victoria Area residents, and the geovisualization was assessed in terms of its representation of a real-world coastal place and ability for connecting with sense of place. Findings from the study indicate that the presence certain elements in coastal geovisualizations can contribute to realism and sense of place, such as people, dogs, birds, marine life, vegetation, and boats; however, simultaneously, deficiencies in numbers and varieties of these elements can detract from realism and sense of place. In addition, incorporation of soundscape and viewshed elements both demonstrated as significant to the tool’s ability to connect with sense of place, with the latter potentially being more significant among those with higher familiarity with the real-world place. Beach textures were also found to be important for the geovisualization’s ability to connect with sense of place; however, this ability can be compromised when running versions of the tool with lower graphical resolution.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".