Geospatial Intelligence in Business: A Geographic Information System-Based Approach
Bibliographic record
Abstract
Geographic Information Systems (GIS) have become a pivotal tool in modern business strategies, offering geospatial intelligence that significantly enhances decision-making processes. This paper analyzes recreational and sports facilities across Quebec using data from the Open Database of Recreational and Sport Facilities (ODRSF) sourced from Statistics Canada. The research identifies trends and patterns in facility distribution and density to provide insights into regional recreational infrastructure. The study utilizes descriptive analysis, geographic visualization, and heat map techniques to examine the concentration of facilities in various Census Subdivisions (CSDs). Key findings reveal that Gatineau, Montréal, Shawinigan, Laval, Québec, Longueuil, and Sherbrooke exhibit the highest recreational and sports facilities densities, indicating significant community engagement in these areas. The analysis also highlights the prevalence of parks, pools, and sports fields, with parks being the most common facility type, followed by pools and sports fields. The implications of these findings are substantial for businesses and policymakers. High-density regions present opportunities for targeted market strategies, including optimal site selection for new facilities and tailored product offerings. The study also suggests the potential for community-focused programs and partnerships to enhance recreational investments. Overall, the research provides substantial insights into the spatial distribution of recreational facilities, offering a basis for strategic planning and further investigation in the sports and leisure sector.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".