MétaCan
Menu
Back to cohort
Record W3118892240 · doi:10.18666/jpra-2020-10502

Implementing Technology-Based Visitor Counts in Parks: A Methodological Overview

2021· article· en· W3118892240 on OpenAlexaff
John B. Read, Margaret J. Daniels, Laurlyn K. Harmon

Bibliographic record

VenueJournal of Park and Recreation Administration · 2021
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVisitor patternRecreationStaffingData collectionService (business)TourismComputer scienceBusinessMarketingEnvironmental resource managementGeographySociologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.096
GPT teacher head0.429
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Park and Recreation AdministrationSame topicRecreation, Leisure, Wilderness ManagementFrench-language works237,207