Identifying the Most Popular Entry Routes into a Public Library Using GIS Can Be a Tool to Increase Ease of Navigation and Identify Placement of Marketing Materials
Notice bibliographique
Résumé
Objective – To evaluate and measure how patrons physically navigate entry routes within a public library and determine whether GIS is a useful instrument for this purpose.
 
 Design – Unobtrusive, covert observational study.
 
 Setting – Medium-sized public library in the United States.
 
 Subjects – 1,415 patrons were observed as they entered the library.
 
 Methods – Routes used by patron cases were selected as the unit of analysis. Patron cases were either individuals entering the building alone or groups entering the building together. Patrons were observed from a stationary and unobtrusive location. ArcMap (GIS software) was used to develop the floor plan instrument on which entry routes were recorded and then later analyzed. The paths analyzed were limited to what was considered the “entry area.” Data were collected during three separate one-hour periods for six consecutive days in the fall of 2008. The researcher chose three purposive one-hour time samples with the intention of distributing them across the library’s opening hours.
 
 Main results – The 1,415 patron cases used 195 unique routes that were recorded from the two entrances of the facility, with the east (right) entrance accounting for 83.3% of the cases (n=1178). Two entry routes were consistently the most popular overall and across each of the sample days. The next-most-popular entry routes did not remain constant across the total observed cases and each day’s observed cases or across the sample days. Over 75% of all observed patrons used 22 of the 195 entry routes: 7 routes were used by 30 or more cases each (n=836, 59.1% of all cases), 4 by 20 to 29 cases each (n=95, 6.7% of all cases), and 11 by 10 to 19 cases each (n=159, 11.2% of all cases). The route to the circulation desk was the most popular entry route for patrons. The other most popular route passed toward the rear of the library, but the observer could not record the final destination(s) of that route due to the restricted viewable area.
 
 Conclusion – The study helped the researcher to establish what areas would be ideal locations for the placement of marketing materials and a book display. Knowledge of popular entry routes can also be useful in identifying routes that could be enlarged to ease patron navigation. GIS was shown to be a useful mapping instrument for recording and analyzing routes taken.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,116 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».