Implementing Technology-Based Visitor Counts in Parks: A Methodological Overview
Bibliographic record
Abstract
Historically, documenting visitor counts in park, recreation, and tourism spaces has been fraught with challenges that often result in data with questionable reliability and validity. However, these counts are necessary for managers in that they inform budgets, staffing, and policy. The purpose of this methodological study is to detail the processes involved in implementing technology-based counting systems within parks with the goal of assisting managers who wish to modernize visitor counting procedures. The first step involves a detailed site analysis, with considerations specific to park boundaries, access to power sources, the availability of WiFi, and whether lighting is needed for the technology to function. Once the site analysis is completed, the technology options can be considered, with the understanding that the accuracy of the counts will be impacted by visitor flow, focal area of interest, the number of counters utilized, whether visitors must be carrying WiFi-enabled devices to be counted, data transmission options, and access to dynamic features such as those that eliminate double counts. A case study approach was used to demonstrate implementation procedures, focusing on site and technology selection, then moving on to installation considerations, data collection, validation, data analysis, and management implications. The Korean War Veterans Memorial (KOWA), a National Park Service holding located within the National Mall and Memorial Parks in Washington, DC, was selected as an optimal site based on semi-porous boundaries, consistent visitor flows, and ready access to power sources. After consideration of price, privacy, ease of installation, and ready access to data, 3D people counters were the chosen technology. The counters were installed in weatherproofed housings and mounted on lampposts that were situated at the two main entrance sites to the memorial. Analysis of twelve weeks of data indicated that the counting accuracy of the 3D counters was high, minimal modifications were needed, and visitor privacy was retained. A similar methodological approach can be applied by park managers within a wide variety of settings.
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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 | 0.128 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| 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".